Patent Publication Number: US-2023140151-A1

Title: Predicting wellness of a user with monitoring from portable monitoring devices

Description:
RELATED APPLICATIONS 
     This application claims priority from each of U.S. Provisional Application No. 63/000,607, filed 27 Mar. 2020 and U.S. Provisional Application No. 63/032,036, filed 29 May 2020, the subject matter of which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This invention relates to a prediction of wellness of a user with monitoring from portable monitoring devices. 
     BACKGROUND OF THE INVENTION 
     Many disorders affecting the health and wellness of an individual can be difficult to detect in the early stages of the disorder, which is often the time in which intervention is most effective. For example, infectious diseases have incubation periods during which an individual can be contagious to others either without experiencing symptoms or while experiencing only relatively innocuous symptoms. Similarly, in many disorders, timely treatment can spare an individual the worst of the symptoms. 
     SUMMARY 
     In accordance with one aspect of the present invention, a method is provided for monitoring a wellness of a user. A wellness-relevant parameter representing the user is monitored at a portable device over a defined period to produce a time series for the wellness-relevant parameter. A first set and a second set of either cognitive assessment data or psychosocial assessment data are obtained for the user at respective first and second times in the defined period. A value is assigned to the user via a predictive model according to the time series for the wellness-relevant parameter, the first set of either cognitive assessment data or psychosocial assessment data, and the second set of either cognitive assessment data or psychosocial assessment data. 
     In accordance with another aspect of the present invention, a system includes a wearable device that monitors a wellness-relevant parameter representing a user over a defined period to produce a time series for the monitored parameter. A portable device receives a first set and a second set of either cognitive assessment data or psychosocial assessment data for the user at respective first and second times in the defined period. A predictive model assigns a value to the user according to the time series for the wellness-relevant parameter, the first set of either cognitive assessment data or psychosocial assessment data, and the second set of either cognitive assessment data or psychosocial assessment data. 
     In accordance with a further aspect of the present invention, a method is provided for monitoring a wellness of a user. A plurality of wellness-relevant parameters representing the user are monitored at a wearable device over a defined period to produce respective time series for the monitored parameter. A set of features representing the user are extracted from the time series for the plurality of wellness-relevant parameters. The set of features includes a predicted value for at least one of the plurality of wellness-relevant parameters. A value is assigned to the user via a predictive model according to the set of features. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a system for monitoring the wellness of a user in accordance with an aspect of the present invention; 
         FIG.  2    is a schematic example of the system of  FIG.  1    using a plurality of portable monitoring devices; 
         FIG.  3    is a screenshot of a reaction time test from an example cognitive assessment application; 
         FIG.  4    is a screenshot of an attention test from an example cognitive assessment application; 
         FIGS.  5  and  6    are screenshots of a response inhibition test from an example cognitive assessment application; 
         FIG.  7    is a screenshot of a working memory (1-back) test from an example cognitive assessment application; 
         FIG.  8    is a screenshot of a working memory (2-back) test from an example cognitive assessment application; 
         FIG.  9    illustrates example questions for a first survey that is completed in the morning for an example of the system used to predict the onset of symptoms from COVID-19; 
         FIG.  10    illustrates example questions for a second survey that is completed in the evening for the example of  FIG.  9   ; 
         FIG.  11    illustrates a simplified example of a map of risk scores that could be generated for a target location; 
         FIG.  12    illustrates graphs of several wellness-related parameters over a time period before an outbreak of an infectious disease; 
         FIG.  13    illustrates graphs of the parameters of  FIG.  12    during an outbreak; 
         FIG.  14    illustrates a radar plot comparing average values for various wellness-relevant parameters for individuals infected with COVID-19 against the general population; 
         FIG.  15    illustrates one example of a method for monitoring the wellness of a user; 
         FIG.  16    illustrates another example for monitoring the wellness of a user; and 
         FIG.  17    is a schematic block diagram illustrating an exemplary system of hardware components. 
     
    
    
     DETAILED DESCRIPTION 
     The term “wellness” as used herein in intended to refer to the mental, physical, cognitive, social, and emotional health of a user and should be construed to cover each of the health, function, balance, resilience, homeostasis, disease, and condition of the user. In various examples herein, the wellness of the user can relate to the readiness of the user to perform job-related functions, the susceptibility of the user to an infectious disease, the ability of the user to recover from an infectious disease, the exhibition of symptoms of an infectious disease by the user, the degree to which the user exhibits symptoms of an infectious disease, the ability to recover from an infectious disease, the effects of vaccines or other therapeutic substances on the user, including both efficacy and side effects, and the ability to avoid reinfection by a previously contracted infectious disease. 
     A “wellness-relevant parameter” is a physiological, cognitive, sensory (e.g., smell, taste, vision, sweat, hearing, etc.), psychosocial, or behavioral parameter that is relevant to the wellness of a user. 
     A “biological rhythm” is any chronobiological phenomenon that affects human beings, including but not limited to, circadian rhythms, ultradian rhythms, infradian rhythms, diurnal cycle, sleep/wake cycles, and patterns of life. 
     A “portable monitoring device,” as used herein, refers to a device that is worn by, carried by, or implanted within a user that incorporates either or both of an input device and user interface for receiving input from the user and sensors for monitoring either a wellness-relevant parameter or a parameter that can be used to calculate or estimate a wellness-relevant parameter. Examples include wearables, such as smartwatches, rings, and similar devices, mobile devices, such as smartphones, and tablets, and laptop or notebook computers. 
     An “index”, as used herein, is intended to cover composite statistics and AI findings derived from a series of observations and used as an indicator or measure. An index can be an ordinal, continuous, or categorical value representing the observations and correlations, and should be read to encompass statistics traditionally referred to as “scores” as well as the more technical meaning of index. 
     “Psychosocial assessment data” includes psychosocial, behavioral, and stress related parameters that can be used to assess the functionality and stress level of a user. Each of the parameters listed in Table 3 is an example of psychosocial assessment data. 
     “Cognitive assessment data” represents any of executive function, decision making, working memory, attention, and fatigue of a user as assessed by a one or more cognitive tests. Each of the parameters listed in Table 2 is an example of psychosocial assessment data. 
       FIG.  1    illustrates a system  100  for monitoring the health, wellness, and functional state of a user in accordance with an aspect of the present invention. The system  100  includes a plurality of portable monitoring devices  102  and  110  that includes sensors for monitoring systems tracking the wellness parameters for the user. It will be appreciated that a given portable monitoring device (e.g.,  102 ) can either communicate directly with a remote server  120  to provide the wellness-relevant parameters to the server or with another portable monitoring device (e.g.,  110 ) that relays the wellness-relevant parameters to the server. By using portable monitoring devices  102  and  110 , measurements can be made continuous from any of a user&#39;s home, classroom, job, or sports field—literally anywhere from the battlefield to the board room—to effectively provide digital personal protective equipment for the user. As noted above, wellness-relevant parameters can include at least physiological, cognitive, psychosocial, sensory, and behavioral parameters. Table I provides non-limiting examples of physiological parameters that can be measured and exemplary tests, devices, and methods, to measure the physiological parameters. 
     
       
         
           
               
               
             
               
                 TABLE I 
               
               
                   
               
               
                   
                 Exemplary Devices and Methods to 
               
               
                 Physiological Parameter 
                 Measure Physiological Parameters 
               
               
                   
               
             
            
               
                 Brain Activity 
                 Electroencephalogram, Magnetic Resonance 
               
               
                   
                 Imaging, including functional Magnetic 
               
               
                   
                 Resonance Imaging (fMRI), PET, SPECT, 
               
               
                   
                 MEG, and other brain imaging modalities 
               
               
                   
                 looking at electrical, blood flow, 
               
               
                   
                 neurotransmitter, and metabolic function 
               
               
                 Heart rate 
                 Electrocardiogram and Photoplethysmogram 
               
               
                 Heart rate variability 
                 Electrocardiogram, Photoplethysmogram 
               
               
                 Eye tracking 
                 Pupillometry, including tracking saccades, 
               
               
                   
                 fixations, and pupil size (e.g. dilation) 
               
               
                 Perspiration 
                 Perspiration sensor 
               
               
                 Blood pressure 
                 Sphygmomanometer 
               
               
                 Body temperature 
                 Thermometer 
               
               
                 Blood oxygen saturation 
                 Pulse oximeter/accelerometer 
               
               
                 and respiratory rate 
               
               
                 Skin conductivity 
                 Electrodermal activity 
               
               
                 Sympathetic and 
                 Derived from the above measurements 
               
               
                 parasympathetic tone 
               
               
                 Genetic biomarkers 
                 Genetic testing 
               
               
                 Immune biomarkers 
                 Blood, saliva, and/or urine tests 
               
               
                 including TNF-alpha, 
               
               
                 immune alteration (e.g. 
               
               
                 ILs), oxidative stress, and 
               
               
                 hormones (e.g. cortisol) 
               
               
                   
               
            
           
         
       
     
     The physiological parameters can be measured via wearable or implantable devices as well as self-reporting by the user via applications in a mobile device, which facilitates measuring these physiological parameters in a naturalistic, non-clinical setting. For example, a smart watch can be used to measure the user&#39;s heart rate, heart rate variability, body temperature, blood oxygen saturation, movement, and sleep. These values can also be subject to a diurnal analysis to estimate variability and reviewed in view of expected changes due to biological rhythms, as well as deviations from an expected pattern of biological rhythms. For example, the biological rhythms of a user can be tracked for a predetermined period (e.g., ten days), to establish a normal pattern of biological rhythms. Oscillations in biological rhythms can be detected as departures from this established pattern. 
     The cognitive parameters can be assessed by a battery of cognitive tests that measure, for example, executive function, decision making, working memory, attention, and fatigue. Table II provides non-limiting examples of cognitive parameters that are gamified and that can be measured and exemplary methods and tests/tasks to measure such cognitive parameters. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE II 
               
               
                   
                   
               
               
                   
                   
                 Exemplary Tests and Methods to 
               
               
                   
                 Cognitive Parameter 
                 Measure Cognitive Parameters 
               
               
                   
                   
               
             
            
               
                   
                 Temporal discounting 
                 Kirby Delay Discounting Task 
               
               
                   
                 Alertness and fatigue 
                 Psychomotor Vigilance Task 
               
               
                   
                 Focused attention and 
                 Erikson Flanker Task 
               
               
                   
                 response inhibition 
               
               
                   
                 Working memory 
                 N-Back Task 
               
               
                   
                 Attentional bias towards 
                 Dot-Probe Task 
               
               
                   
                 emotional cues 
               
               
                   
                 Inflexible persistence 
                 Wisconsin Card Sorting Task 
               
               
                   
                 Decision making 
                 Iowa Gambling Task 
               
               
                   
                 Risk taking behavior 
                 Balloon Analogue Risk Task 
               
               
                   
                 Inhibitory control 
                 Anti-Saccade Task 
               
               
                   
                 Sustained attention 
                 Sustained Attention 
               
               
                   
                 Executive function 
                 Task Shifting or Set Shifting Task 
               
               
                   
                   
               
            
           
         
       
     
     These cognitive tests can be administered in a clinical/laboratory setting or in a naturalistic, non-clinical setting such as when the user is at home, work or other non-clinical setting. A smart device, such as a smartphone, tablet, or smart watch, can facilitate measuring these cognitive parameters in a naturalistic, non-clinical setting. For example, the Erikson Flanker, N-Back and Psychomotor Vigilance Tasks can be taken via an application on a smart phone, tablet, or smart watch. 
     Table III provides non-limiting examples of psychosocial, behavioral, and stress related parameters that can be measured and exemplary tests, devices, and methods, to measure the behavioral parameters. 
     
       
         
           
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                 Psychosocial or 
                 Exemplary Tests and Methods to Measure 
               
               
                 Behavioral Parameter 
                 Psychosocial or Behavioral Parameters 
               
               
                   
               
             
            
               
                 Symptom log 
                 Presence of specific symptoms (i.e. fever, 
               
               
                   
                 headache, cough, loss of smell) 
               
               
                 Burnout 
                 Burnout inventory or similar 
               
               
                 Physical, Mental, and 
                 User-Reported Outcomes Measurement 
               
               
                 Social Health 
                 Information System (PROMIS) 
               
               
                 Depression 
                 Hamilton Depression Rating Scale 
               
               
                 Anxiety 
                 Hamilton Anxiety Rating Scale 
               
               
                 Mania 
                 Snaith-Hamilton Pleasure Scale 
               
               
                 Mood/ 
                 Profile of Mood States; Positive Affect 
               
               
                 Catastrophizing scale 
                 Negative Affect Schedule 
               
               
                 Affect 
                 Positive Affect Negative Affect Schedule 
               
               
                 Impulsivity 
                 Barratt Impulsiveness Scale 
               
               
                 Anhedonia 
                 Snaith-Hamilton Pleasure Scale 
               
               
                 Sleep 
                 Sleep onset &amp; offset, sleep quality, sleep 
               
               
                   
                 quantity, from wearable accelerometer and 
               
               
                   
                 PPG 
               
               
                 Activity level 
                 Daily movement total, time of activities, 
               
               
                   
                 from wearable accelerometer, steps 
               
               
                 Adverse Childhood 
                 Childhood trauma 
               
               
                 Experiences 
               
               
                 Daily Activities 
                 Exposure, risk taking 
               
               
                 Daily Workload and 
                 NASA Task Load Index, Perceived Stress 
               
               
                 Stress 
                 Scale (PSS), Social Readjustment Rating 
               
               
                   
                 Scale (SRRS) 
               
               
                 Social Determents 
                 Social determents of health questionnaire 
               
               
                 of Health 
               
               
                   
               
            
           
         
       
     
     The behavioral and psychosocial parameters can measure the user&#39;s functionality, such as the user&#39;s movement via wearable devices as well as subjective/self-reporting questionnaires. These parameters can also be used to quantify an overall stress level of the user that is updated at regular intervals. The subjective/self-reporting questionnaires can be collected in a clinical/laboratory setting or in a naturalistic, in the wild, non-clinical setting such as when the user is at home, work, or other non-clinical setting. A smart device, such as a smartphone, tablet, or personal computer can be used to administer the subjective/self-reporting questionnaires. Using embedded accelerometers and cameras, these smart devices can also be used to capture the user&#39;s movements as well as facial expression analysis to analyze the user&#39;s facial expressions that could indicate mood, anxiety, depression, agitation, and fatigue. 
     In addition to one or more combinations of physiological, cognitive, psychosocial, and behavioral parameters, clinical data can also be part of the multi-dimensional feedback approach to predicting wellness. Such clinical data can includes, for example, the user&#39;s clinical state, the user&#39;s medical history (including family history), employment information, and residential status. 
     The remote server that analyzes the data collected by the portable monitoring devices  102  and  110 . The remote server  120  can be implemented as a dedicated physical server or as part of a cloud server arrangement. In addition to the remote server, data can be analyzed on the local device itself and/or in a federated learning mechanism. Information received from the portable monitoring devices  102  and  110  is provided to a feature extractor  122  that extracts a plurality of features for use at a predictive model  124 . The feature extractor  122  determines categorical and continuous parameters representing the wellness-relevant parameters. In one example, the parameters can include descriptive statistics, such as measures of central tendency (e.g., median, mode, arithmetic mean, or geometric mean) and measures of deviation (e.g., range, interquartile range, variance, standard deviation, etc.) of time series of the monitored parameters, as well as the time series themselves. In one implementation, the feature extractor  124  can perform a wavelet transform on the time series of values for one or more parameters to provide a set of wavelet coefficients. It will be appreciated that the wavelet transform used herein is two-dimensional, such that the coefficients can be envisioned as a two-dimensional array across time and either frequency or scale. 
     For a given time series of parameters, x i , the wavelet coefficients, W a (n), produced in a wavelet decomposition can be defined as: 
     
       
         
           
             
               
                 
                   
                     
                       W 
                       a 
                     
                     ( 
                     n 
                     ) 
                   
                   = 
                   
                     
                       a 
                       
                         - 
                         1 
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         M 
                       
                       
                         
                           x 
                           i 
                         
                         ⁢ 
                         
                           ψ 
                           ⁡ 
                           ( 
                           
                             
                               i 
                               - 
                               n 
                             
                             a 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                       
                   3 
                 
               
             
           
         
       
     
     wherein ψ is the wavelet function, M is the length of the time series, and a and n define the coefficient computation locations. 
     It will be appreciated that the wavelet coefficients can be used as individual features as well as aggregated to make composite features. In one example, a center of the mass, represented as an ordered pair of time and either frequency or scale, can be used to provide features at the predictive model  124 . Alternatively, one or more weighted combination of wavelet coefficients can be used as features, with the weights for each combination determined during a training process of the predictive model. 
     Additionally or alternatively, the wellness-relevant parameters can be used to assign a plurality of categorical parameters to the user according to thresholds for wellness-relevant parameters or rule sets that act upon time series of values for the wellness-relevant parameters, for example, representing the presence or absence of a given condition or behavior. The predictive model  124  can also utilize user data  126  stored at the remote server  120 , including, for example, employment information (e.g., title, department, shift), age, sex, home zip code, genomic data, nutritional information, medication intake, household information (e.g., type of home, number and age of residents), social and psychosocial, consumer spending and profiles, financial, food safety, physical abuse, and relevant medical history. In addition the model can combine multiple users to interact together to refine prediction such as social model of spouse, children, family, co-workers, friends and others. 
     The predictive model  124  can utilize one or more pattern recognition algorithms, each of which analyze the extracted features or a subset of the extracted features to assign a continuous or categorical parameter to the user. In one example, the assigned parameter can represent a predicted “burnout” of the user, that is, a predicted decrease in cognitive function, due to stress, fatigue, or illness, to an extent that will materially affect job performance. In this example, sleep and activity data can be used along with results from a cognitive assessment and mood reporting applications to provide a continuous index representing the degree of burnout experienced by the user. It will be appreciated, however, that additional or alternative features can be used in the analysis and that the index can be replaced with a categorical classification (e.g., “near baseline”, “reduced”, “impaired”) in some implementations, for example, by applying one or more decision thresholds to the index. 
     In another example, the predictive model  124  can be used to provide an index representing an internal marker of brain body balance, homeostasis, resilience, and wellness. In yet another example, the predictive model  124  can be used to provide an index representing a measure of homeostasis for the user or to predict levels of the autonomic nervous system tone, as well as certain biomarkers representing various body organs, the eye, the cardiovascular system, the gastrointestinal tract, GU, Immune and endocrine systems, including glucose, C-reactive protein, and IL-6. In still another example, the predictive model  124  can predict a present or future pathogen (e.g., virus, bacteria, fungus, prion) concentration in a given tissue or bodily fluid of a patient. In another example, the predictive model  124  wherein the value represents can represent an expected degree of immunity provided to the user by immunization. For example, the output of predictive model can represent an expected concentration of antibodies associated with a given vaccine in the blood of the user after a predetermined period following the immunization. 
     In a still further example, the wellness-relevant data can be used to provide a continuous index representing the risk posed to the user by a specific illness or class of illnesses (e.g., immune disorders, cytokine storm, cancers, and infectious diseases). For example, the index can represent a risk of infection, risk of being contagious, expressed, for example, as a predicted time for the individual to become contagious or a predicted virus PCR (polymerase chain reaction) levels in the nasopharynx and mouth and saliva, or a blood test, a predicted time to an onset of symptoms, a probability of recovery from a potential infection, or a single value representing a blend of two or more of these factors. Immune disorders include autoimmune disorders, hypersensitivity syndromes, immune deficiency disorders, and combinations thereof. Such immune disorders can be caused by cell-mediated immunity (T lymphocytes), humoral immunity (B lymphocytes) and immune tolerance. Immune disorders may result in destruction of body tissue, abnormal growth of an organ, and/or changes in organ function. An immune disorder may affect one or more organ or tissue types. 
     An autoimmune disorder is a type of immune disorder resulting from an abnormal or exaggerated adaptive immune response that targets healthy cells or tissues that should not normally cause an immune reaction in the body. Autoimmune disorders include disorders in line with Witebsky&#39;s Postulates. These disorders can include multiple sclerosis, ankylosing spondylitis, rheumatoid arthritis, celiac disease, myositis, myasthenia gravis, Addison&#39;s disease, lupus, hemolytic anemia, vitiligo, scleroderma, psoriasis, Hashimoto&#39;s disease, Addison&#39;s disease, Grave&#39;s disease, reactive arthritis, Sjogren&#39;s syndrome, nephritis, chronic Lyme disease, vasculitis, endocarditis, alopecia areata, urticaria, vasculitis, uveitis, pemphigus, Fibromyalgia, thrombophelebitis, erythema nodusum, dermatitis, eczema, Type 1 Diabetes, temporal arteritis, Crohn&#39;s Disease, Behcet&#39;s disease, or psoriatic arthritis. 
     Hypersensitivity syndromes include immediate (Type I) hypersensitivity, antibody-mediated (Type II) hypersensitivity, immune complex-medicated (Type III) hypersensitivity, and cell-mediated (Type IV) hypersensitivity. Non-limiting examples of Type I hypersensitivity disorders are chronic or acute allergies, atopic forms of bronchial asthma, and anaphylaxis. Non-limiting examples of Type II hypersensitivity syndromes are autoimmune hemolytic anemia, autoimmune thrombocytopenic purpura, pemphigus vulgaris, vasculitis caused by antineutrophil cytoplasmic antibodies, Goodpasture syndrome, acute rheumatic fever, myasthenia gravis, Graves&#39; disease, insulin-resistant diabetes, and pernicious anemia. Type II hypersensitivity syndromes may be caused by the production of antibodies that bind to non-self antibodies, such as after an allogenic transplantation resulting in organ rejection; blood-group incomparability resulting in hemolysis; antibodies that bind to tumor-associated antigens resulting in paraneoplastic syndromes, neuropathies, and channelopathies, for example. Type II hypersensitivity may also be caused by antibodies directed against cell-membrane bound medications resulting in medication-induced cell death, such as heparin-induced thrombocytopenia, for example. Non-limiting examples of Type III hypersensitivity disorders are systemic lupus erythematosus, poststreptococcal glomerulonephritis, acute glomerulonephritis, serum sickness, Arthus reaction, reactive arthritis, and polyarteritis nodosa. Non-limiting examples of Type IV hypersensitivity syndromes are contact dermatitis, multiple sclerosis, type 1 diabetes, transplant rejection, rheumatoid arthritis, tuberculosis, and peripheral neuropathy. 
     Immune deficiency disorders include primary immunodeficiency disorders and secondary immunodeficiency disorders. Non-limiting examples of primary immunodeficiency disorders are X-linked agammaglobulinemia, common variable immunodeficiency, isolated IgA deficiency, hyper-IgM syndrome, DiGeorge syndrome, severe combined immunodeficiency disease (SCID), Wiskott-Aldrich syndrome, and genetic deficiencies of the complement system. Non-limiting examples of secondary immunodeficiency disorders are Acquired Immunodeficiency Syndrome (AIDS), human immunodeficiency virus (HIV) infection, combined immune deficiency syndrome (CIDS), and a spinal cord injury-induced immune depression syndrome (SCI-IDS). 
     Non-limited examples of infectious disease include  Acinetobacter  infections, Actinomycosis, African sleeping sickness (African trypanosomiasis), AIDS (acquired immunodeficiency syndrome), Amoebiasis, Anaplasmosis, Angiostrongyliasis, Anisakiasis, Anthrax, Arcanobacterium  haemolyticum  infection, Argentine hemorrhagic fever, Ascariasis, Aspergillosis, Astrovirus infection, Babesiosis,  Bacillus cereus  infection, Bacterial meningitis, Bacterial pneumonia, Bacterial vaginosis,  Bacteroides  infection, Balantidiasis, Bartonellosis,  Baylisascaris , infection, BK virus infection, Black piedra, Blastocystosis, Blastomycosis, Bolivian hemorrhagic fever, Botulism (and Infant botulism), Brazilian hemorrhagic fever, Brucellosis, Bubonic plague,  Burkholderia  infection, Buruli ulcer, Calicivirus infection (Norovirus and Sapovirus), Campylobacteriosis, Candidiasis (Moniliasis; Thrush), Capillariasis, Carrion&#39;s disease, Cat-scratch disease, Cellulitis, Chagas disease (American trypanosomiasis), Chancroid, Chickenpox, Chikungunya,  Chlamydia, Chlamydophila pneumoniae  infection (Taiwan acute respiratory agent or TWAR), Cholera, Chromoblastomycosis, Chytridiomycosis, Clonorchiasis,  Clostridium difficile , colitis, Coccidioidomycosis, Colorado tick fever (CTF), Common cold (Acute viral rhinopharyngitis; Acute coryza), Coronavirus disease 2019, Creutzfeldt-Jakob disease (CJD), Crimean-Congo hemorrhagic fever (CCHF), Cryptococcosis, Cryptosporidiosis, Cutaneous larva migrans (CLM), Cyclosporiasis, Cysticercosis, Cytomegalovirus infection, Dengue fever, Desmodesmus infection, Dientamoebiasis, Diphtheria, Diphyllobothriasis, Dracunculiasis, Ebola hemorrhagic fever, Echinococcosis, Ehrlichiosis, Enterobiasis (Pinworm infection),  Enterococcus  infection, Enterovirus infection, Epidemic typhus, Erythema infectiosum (Fifth disease), Exanthem subitum (Sixth disease), Fasciolasis, Fasciolopsiasis, Fatal familial insomnia (FFI), Filariasis, Food poisoning by  Clostridium perfringens , Free-living amebic infection,  Fusobacterium  infection, Gas gangrene (Clostridial myonecrosis), Geotrichosis, Gerstmann-Strussler-Scheinker syndrome (GSS), Giardiasis, Glanders, Gnathostomiasis, Gonorrhea, Granuloma inguinale (Donovanosis), Group A streptococcal infection, Group B streptococcal infection,  Haemophilus influenzae  infection, “Hand, foot and mouth disease (HFMD)”, Hantavirus Pulmonary Syndrome (HPS), Heartland virus disease,  Helicobacter pylori  infection, Hemolytic-uremic syndrome (HUS), Hemorrhagic fever with renal syndrome (HFRS), Hendra virus infection, Hepatitis A, Hepatitis B, Hepatitis C, Hepatitis D, Hepatitis E, Herpes simplex, Histoplasmosis, Hookworm infection, Human bocavirus infection, Human  ewingii  ehrlichiosis, Human granulocytic anaplasmosis (HGA), Human metapneumovirus infection, Human monocytic ehrlichiosis, Human papillomavirus (HPV) infection, Human parainfluenza virus infection, Hymenolepiasis, Epstein-Barr virus infectious mononucleosis (Mono), Influenza (flu), Isosporiasis, Kawasaki disease, Keratitis, Kingella kingae infection, Kuru, Lassa fever, Legionellosis (Legionnaires&#39; disease), Pontiac fever, Leishmaniasis, Leprosy, Leptospirosis, Listeriosis, Lyme disease (Lyme borreliosis), Lymphatic filariasis (Elephantiasis), Lymphocytic choriomeningitis, Malaria, Marburg hemorrhagic fever (MHF), Measles, Middle East respiratory syndrome (MERS), Melioidosis (Whitmore&#39;s disease), Meningitis, Meningococcal disease, Metagonimiasis, Microsporidiosis, Molluscum contagiosum (MC), Monkeypox, Mumps, Murine typhus (Endemic typhus),  Mycoplasma  pneumonia,  Mycoplasma genitalium  infection, Mycetoma, Myiasis, Neonatal conjunctivitis (Ophthalmia neonatorum), Nipah virus infection, Norovirus (children and babies), “(New) Variant Creutzfeldt-Jakob disease (vCJD, nvCJD)”, Nocardiosis, Onchocerciasis (River blindness), Opisthorchiasis, Paracoccidioidomycosis (South American blastomycosis), Paragonimiasis, Pasteurellosis, Pediculosis capitis (Head lice), Pediculosis corporis (Body lice), “Pediculosis pubis (pubic lice, crab lice)”, Pelvic inflammatory disease (PID), Pertussis (whooping cough), Plague, Pneumococcal infection,  Pneumocystis  pneumonia (PCP), Pneumonia, Poliomyelitis,  Prevotella  infection, Primary amoebic meningoencephalitis (PAM), Progressive multifocal leukoencephalopathy, Psittacosis, Q fever, Rabies, Relapsing fever, Respiratory syncytial virus infection, Rhinosporidiosis, Rhinovirus infection, Rickettsial infection, Rickettsialpox, Rift Valley fever (RVF), Rocky Mountain spotted fever (RMSF), Rotavirus infection, Rubella,  Salmonellosis , SARS (severe acute respiratory syndrome), Scabies, Scarlet fever, Schistosomiasis, Sepsis, Shigellosis (bacillary dysentery), Shingles (Herpes zoster), Smallpox (variola), Sporotrichosis, Staphylococcal food poisoning, Staphylococcal infection, Strongyloidiasis, Subacute sclerosing panencephalitis, Bejel, Syphilis, Yaws, Taeniasis, Tetanus (lockjaw),  Tinea barbae  (barber&#39;s itch),  Tinea capitis  (ringworm of the scalp),  Tinea corporis  (ringworm of the body),  Tinea cruris  (Jock itch),  Tinea manum  (ringworm of the hand),  Tinea nigra, Tinea pedis  (athlete&#39;s foot),  Tinea unguium  (onychomycosis),  Tinea versicolor  ( Pityriasis versicolor ), Toxocariasis (ocular larva migrans (OLM)), Toxocariasis (visceral larva migrans (VLM)), Toxoplasmosis, Trachoma, Trichinosis, Trichomoniasis, Trichuriasis (whipworm infection), Tuberculosis, Tularemia, Typhoid fever, Typhus fever,  Ureaplasma urealyticum  infection, Valley fever, Venezuelan equine encephalitis, Venezuelan hemorrhagic fever,  Vibrio vulnificus  infection,  Vibrio parahaemolyticus  enteritis, Viral pneumonia, West Nile fever, White piedra ( tinea blanca ),  Yersinia pseudotuberculosis  infection, Yersiniosis, Yellow fever, Zeaspora, Zika fever, Zygomycosis. 
     Where multiple classification or regression models are used, an arbitration element can be utilized to provide a coherent result from the plurality of models. The training process of a given classifier will vary with its implementation, but training generally involves a statistical aggregation of training data into one or more parameters associated with the output class. The training process can be accomplished on a remote system and/or on the local device or wearable, app. The training process can be achieved in a federated or non-federated fashion. For rule-based models, such as decision trees, domain knowledge, for example, as provided by one or more human experts, can be used in place of or to supplement training data in selecting rules for classifying a user using the extracted features. Any of a variety of techniques can be utilized for the classification algorithm, including support vector machines, regression models, self-organized maps, fuzzy logic systems, data fusion processes, boosting and bagging methods, rule-based systems, or artificial neural networks. 
     Federated learning (aka collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data samples. This approach stands in contrast to traditional centralized machine learning techniques where all data samples are uploaded to one server, as well as to more classical decentralized approaches which assume that local data samples are identically distributed. Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. Its applications are spread over a number of industries including defense, telecommunications, IoT, or pharmaceutics. 
     For example, an SVM classifier can utilize a plurality of functions, referred to as hyperplanes, to conceptually divide boundaries in the N-dimensional feature space, where each of the N dimensions represents one associated feature of the feature vector. The boundaries define a range of feature values associated with each class. Accordingly, an output class and an associated confidence value can be determined for a given input feature vector according to its position in feature space relative to the boundaries. In one implementation, the SVM can be implemented via a kernel method using a linear or non-linear kernel. 
     An ANN classifier comprises a plurality of nodes having a plurality of interconnections. The values from the feature vector are provided to a plurality of input nodes. The input nodes each provide these input values to layers of one or more intermediate nodes. A given intermediate node receives one or more output values from previous nodes. The received values are weighted according to a series of weights established during the training of the classifier. An intermediate node translates its received values into a single output according to a transfer function at the node. For example, the intermediate node can sum the received values and subject the sum to a binary step function. A final layer of nodes provides the confidence values for the output classes of the ANN, with each node having an associated value representing a confidence for one of the associated output classes of the classifier. 
     Many ANN classifiers are fully-connected and feedforward. A convolutional neural network, however, includes convolutional layers in which nodes from a previous layer are only connected to a subset of the nodes in the convolutional layer. Recurrent neural networks are a class of neural networks in which connections between nodes form a directed graph along a temporal sequence. Unlike a feedforward network, recurrent neural networks can incorporate feedback from states caused by earlier inputs, such that an output of the recurrent neural network for a given input can be a function of not only the input but one or more previous inputs. As an example, Long Short-Term Memory (LSTM) networks are a modified version of recurrent neural networks, which makes it easier to remember past data in memory. 
     A rule-based classifier applies a set of logical rules to the extracted features to select an output class. Generally, the rules are applied in order, with the logical result at each step influencing the analysis at later steps. The specific rules and their sequence can be determined from any or all of training data, analogical reasoning from previous cases, or existing domain knowledge. One example of a rule-based classifier is a decision tree algorithm, in which the values of features in a feature set are compared to corresponding threshold in a hierarchical tree structure to select a class for the feature vector. A random forest classifier is a modification of the decision tree algorithm using a bootstrap aggregating, or “bagging” approach. In this approach, multiple decision trees are trained on random samples of the training set, and an average (e.g., mean, median, or mode) result across the plurality of decision trees is returned. For a classification task, the result from each tree would be categorical, and thus a modal outcome can be used. 
     In one implementation, the predictive model  124  can include a constituent model that predicts future values for wellness-relevant parameters, such as a convolutional neural network that is provided with one or more two-dimensional arrays of wavelet transform coefficients as an input. The wavelet coefficients detect not only changes in time, but also in temporal patterns, and can thus reflect changes in the ordinary biological rhythms of the user. In one implementation, the wellness-relevant parameters predicted by the constituent models can include measured parameters such as heart rate, temperature, and heart rate variability as well as symptoms such as headaches, fatigue, shortness of breath, coughing, and sleep disruption. It will be appreciated that a given constituent model can use data in addition to the wavelet coefficients, such as other measured features and user data  126  to provide these predictions. 
     The output of the predictive model  124  can be a categorical parameter representing a status of the user, such as “infected” or “not infected”, “contagious” or “not contagious”, or “recovered” or “not recovered.” In one example, used for screening secured areas for individuals who may be contagious, for example, airport security and medical admissions, the categorical parameter can represent whether an individual can be admitted immediately, denied admission, or subjected to further screening. A categorical parameter can also represent ranges of likelihoods for a current or predicted status. In another implementation, the output of the predictive model  124  can be a continuous parameter, such as a likelihood of a predicted or current status. In one example, the predictive model  124  can include one or more constituent models that predict a value for a wellness-relevant parameter at a future time. For example, a given model can predict a heart rate or temperature for a user at a future time (e.g., in three days) based on received data from the feature extractor  122  and stored user data  126 . These predicted values can be provided to a user or utilized as inputs to additional models to predict a status of the user at the future time. In one example, the predictive model  124  includes a plurality of convolutional neural networks, each configured to predict a future value for a wellness-relevant parameter, with the predicted values from the plurality of convolutional neural networks used to predict a future status of the user. 
     In some implementations, the predictive model  124  can include a feedback component  128  can tune various parameters of the predictive model  124  based upon the accuracy of predictions made by the model. In one example, the feedback component  128  can be shared by a plurality of predictive models  124 , with the outcomes for users associated with each predictive model compared to the outcomes predicted by the output of the model. Parameters associated with the model, such as thresholds for producing categorical inputs or outputs from continuous values, can be adjusted according to the differences in the actual and predicted outcomes. In one example, a continuous output of the system can be compared to a threshold value to determine if the patient is infectious or non-infectious. This threshold can be varied by the feedback model  128  to increase the accuracy of the determination. 
     Alternatively, the predictive model  124  can obtain feedback at the level of the individual model. For example, in a predictive model  124  using constituent models to predict future values of wellness-relevant parameters, the model receives consistent feedback as to the accuracy of these predictions once the wellness-relevant parameter is measured. This feedback can be used to adjust parameters of the model, including individualized thresholds for that user to produce categorical inputs or outputs from continuous values, or baseline values for biological rhythms associated with the patient. Alternatively, feedback can be provided from a final output of the model and compared to other data, such as a user-reported status (e.g., symptomatic or asymptomatic for a given condition), to provide feedback to the model. In one implementation, a reinforcement learning approach can be used to adjust the model parameters based on the accuracy of either predicted future values of wellness-relevant parameters at intermediate stages of the predictive model  124  or the output of the predictive model. For example, a decision threshold used to generate a categorical output from a continuous index produced by the predictive model  124  can be set at an initial value based on feedback from a plurality of models from previous users and adjusted via the reinforcement model to generate a decision threshold specific to the user. 
       FIG.  2    is a schematic example  150  of the system of  FIG.  1    using a plurality of portable monitoring devices  152 ,  154 , and  160 . In the illustrated implementation, the first and second portable monitoring devices  152  and  154  are wearable devices, worn on the wrist and finger, respectively. Wellness-relevant parameters monitored by the first and second portable monitoring devices  152  and  154  can include, for example, heart rate, heart rate variability, metrics of sleep quality, biological rhythm variations, metrics of sleep quantity, physical activity of the user, body orientation, movement, arterial blood pressure, respiratory rate, peripheral arterial oxyhemoglobin saturation, as measured by pulse oximetry, maximum oxygen consumption, temperature, and temperature variation. Wearable devices, as used herein, can include any wearable items implemented with appropriate sensors, including watches, wristbands, rings, headbands, headbands, and other wearable items that can maintain sensors in an appropriate position for monitoring the wellness-relevant parameters. It will be appreciated that a given wearable device  152  and  154  can monitor many of these parameters with great frequency (e.g., every five minutes) allowing for a detailed time series of data to be generated. 
     The system  150  can further include a mobile device  160  that communicates with the first and second portable monitoring devices  152  and  154  via a local transceiver  162 . The mobile device  160  can also include a graphical user interface  164  that allows a user to interact with one or more data gathering applications  166  stored at the base unit. One example of a possible data gathering applications can include a cognitive assessment application that tests various measures of cognitive function. These can include working memory, attention, and response inhibition, fatigue, cognition. Further, these metrics can be compared to an established baseline to estimate a measure of fatigue for the user. Screenshots from an example cognitive assessment application are provided as  FIGS.  3 - 8   . Another data gathering application can include a questionnaire application that allows the user to self-report symptoms, mood, mental, physical, and emotional states, and stress.  FIG.  9    illustrates example questions for a first survey that is completed in the morning for an example of the system used to predict the onset of symptoms from COVID-19.  FIG.  10    illustrates example questions for a second survey that is completed in the evening for this example. In general, the data gathering applications  166  can be selected and configured to monitor each of:
         1. Attention, alertness, Fatigue-Neuropsychologist—measurements of mental overload, decision making, concentration, distraction, inhibitory control, Flanker task, Reaction time, # of times and lapses in hitting a light, choice reaction task and others with attentional components, distractibility, focus, continuous recognition, stroop   2. Memory—SAGE-Self-administered gerocognitive examination, declarative memory   3. Language—   4. Mood and Emotions—CES-D, depression and mood profiles   5. Reward and risk taking—delayed discounting, reward learning,   6. Perceptual processing—visual, auditory, olfactory, somatosensory/multimodal   7. Fatigue— psychomotor vigilance task and other attentional tasks   8. Sensory—systems such as smell, taste, vision, hearing, touch   9. Motor   10. Neural Capacity   11. Social Systems   12. Social network       

     The mobile device  160  further comprises a network transceiver  168  via which the system  150  communicates with a remote server  170  via a local area network or Internet connection. In this example, the remote server  170  includes a predictive model implemented as a recurrent neural network, specifically a network with a long short-term memory architecture. In this example, wellness-relevant parameters from the wearable devices  152  and  154 , such as temperature, in combination with questionnaire responses and cognitive assessment, can be provided to the predictive model as time series along with other relevant data. An output of the model is an index representing risk posed to the user by COVID-19. 
     It will be appreciated that data can be collected from a plurality of users who may be socially connected, for example, as family, coworkers, or friends. An example is the concept of “herd immunity” computed as a social context around an individual. Social connections between users can be self-reported or derived from self-reported data, or, in one example, determined through analysis of location history from the mobile devices of monitored users. The use of location data or proximity sensors, which detect portable monitoring devices associated with other users within a threshold distance, might allow for instances of frequent spatial proximity that are not deliberate social contact (e.g., sharing a common vehicle for public transportation.) In one example, Bluetooth or similar short-range communication between mobile devices carried by users can be used to determine that users have been spatially proximate. An index indicating susceptibility or contraction to an infectious disease could be used as part of a predictor for other, connected individuals. This data could also be used to predict locations at which a disease might spread, allowing for an artificial intelligence driven smart social distancing. It will be appreciated that information gathered from users will be stored in encrypted form and shared only after removal of personally identifying data to preserve users&#39; privacy. 
     In one example, a high traffic location, such as a retail store, an airport, college campus, school, or hospital, could have a number of Bluetooth beacons at known locations. As users pass the beacons, the Bluetooth transceiver in their mobile device will interact with the beacon, with an identifier for the user and a time stored for each interaction. These values, as well as other location and proximity information collected by the application, can be employed for contact tracing as well as for determining the risk of infection associated with various locations. A similar process can be performed using geolocation data collected by a GPS receiver, with users passing through a geofenced region associated with a given location recorded or the presence of infected or contagious users passing through a dynamic geofence associated with each device recorded. 
     Location data from user devices and/or designed Bluetooth beacons can be used to generate a mapping of infection risk across a region of interest. In one example, the presence of user who reports symptoms associated with a given infectious disease via one of the data gathering applications  166  can be assigned to a given location. In another example, both users with reported symptoms and users who are predicted to be contagious from the predictive model  124  can be used to generate the risk score. In one implementation, the contribution to the risk score for users who are predicted to be contagious can be weighted according to a probability or confidence value associated with the prediction of contagiousness. 
     The map can be adjusted to show a symbol, color, or other indicator of the infection, and a risk score can be generated. The risk score can represent a total number of infections reported at that location for a given infectious disease, a number of infections reported at that location over a defined window of time, or a number infections reported at that location, either in total or over a defined window of time divided by an area of the location to generate a value representing a density of infections in that location. The risk score for each location can be shown on the map. 
       FIG.  11    illustrates a simplified example of a map of risk scores  180  that could be generated for a target location. In the simplified example, the risk scores for locations are illustrated as three categorical values, with a first category representing no known risk of infection, a second category representing a low level of infection risk, and a third category representing an increased level of infection risk. In the illustrated map  180 , the first level of infection risk is represented as locations with no shading, the second level of infection risk is represented as locations with light shading  182 , and the third level of infection risk is represented as locations with darker shading  184 . It will be appreciated that each categorical value can be provided by applying a defined or dynamic threshold to a continuous risk score generated for each location. 
     In one implementation, the thresholds used to define each category can be defined according to the characteristics of the user, for example, as represented by the user data  126 , or by a determination of the user&#39;s resilience to infection as determined at the predicted model  124 . For example, if a user is in a high-risk category for infection (e.g., older, immunocompromised, or comorbid condition), the threshold can be lowered to represent the user&#39;s increased risk of infection. Similarly, if the user&#39;s resilience is determined to be lowered at a given time, the thresholds can be temporarily lowered to represent the user&#39;s decreased ability to resist infection. Accordingly, the map can not only be personalized to a given user, but can be adjusted to represent the risk to the user at a specific time. 
     In addition, the generated index can be used as a preventative measure by advising a susceptible individual to avoid social contact or predicting and forecasting contagiousness. For example, an individual known to be susceptible to or about to become contagious with a particular infectious disease might engage in enhanced social distancing until their condition improves. Similarly, a supervisor might remove employees that are particularly susceptible or likely to become contagious from direct contact with customers, particularly in a health care setting. When both susceptibility and forecasted contagiousness can be obtained within a population, individuals forecasted to be contagious can be warned against contact with susceptible individuals within their social network, reducing the spread of the disease among vulnerable populations. 
     Indices measuring resilience or likelihood of recovery can be used for allocating scarce medical resources. For example, individuals with a high resilience can be instructed, at least initially, to treat the disease as outpatients, as it is less likely they will develop symptoms requiring hospital care. Similarly, when drugs or medical equipment, such as ventilators, are in short supply, they can be given to patients with higher need or likelihoods of recovery to maximize the effectiveness of medical resources. In other instances the indices can guide diagnostic and medical status classifications and treatment options to be more effective by taking into account ones overall body resilience and 
     Finally, the data for a given location of interest can be used to detect regions in which infections are likely to begin spreading.  FIG.  12    illustrates several wellness-relevant parameters over a time period before an outbreak of an infectious disease.  FIG.  13    illustrates the same parameters during an outbreak. It will be noted that the average body temperature and heart rate variability at the location of interest drops, while averages for heart rate as well as measures of readiness and activity fall as the outbreak progresses. It will be appreciated from  FIG.  12   , however, that each of these trends were evident before the outbreak was underway, and the use of the predictive model  140 , specifically the evaluation of time series of these values at a recurrent network (e.g., a LSTM) can allow for prediction of the outbreak in time to take measures to reduce its severity. 
       FIG.  14    illustrates a radar plot  190  comparing average values for a set of various wellness-relevant parameters for individuals infected with COVID-19  192  against average values for the set of various wellness-relevant parameters for the general population  194 . As can be seen from the chart, individuals with COVID-19 suffer moderate reductions in sleep quality and sleep duration, and show significant reductions in attention, reported wellness, and heart rate variability. The patient&#39;s resting heart rate also increases significantly. The patient also experiences a slightly increased “workload,” that is, the cost, in the form of additional stress and fatigue, of performing daily tasks. It will be appreciated that these wellness-relevant parameters can be of particular use in identifying the onset of COVID-19 infections in asymptomatic users. 
     In view of the foregoing structural and functional features described above, methods in accordance with various aspects of the present invention will be better appreciated with reference to  FIGS.  15  and  16   . While, for purposes of simplicity of explanation, the methods of  FIGS.  15  and  16    are shown and described as executing serially, it is to be understood and appreciated that the present invention is not limited by the illustrated order, as some aspects could, in accordance with the present invention, occur in different orders and/or concurrently with other aspects from that shown and described herein. Moreover, not all illustrated features may be required to implement a method in accordance with an aspect the present invention. 
       FIG.  15    illustrates one example of a method for monitoring a wellness of a user. At  202 , a wellness-relevant parameter representing the user is monitored at a portable device over a defined period to produce a time series for the wellness-relevant parameter. Examples of wellness-relevant parameters can include heart rate variability and body temperature, which can be monitored, for example, at a wearable device. At  204 , a first set and a second set of either or both of cognitive assessment data and psychosocial assessment data are obtained for the user at respective first and second times in the defined period. In one example, the user is prompted to interact with a cognitive assessment application or psychosocial assessment application at a base unit, such as a mobile device, associated with the portable device to provide the first and second sets of assessment data. 
     At  206 , a value is assigned to the user via a predictive model according to the time series for the wellness-relevant parameter and the first and second sets of either cognitive assessment data or psychosocial assessment data. In one example, the value represents a predicted risk posed to the user by a specific illness or class of illnesses, such as a predicted or forecasted contagiousness of the user, a predicted number of days until the user will be contagious with an infectious disease, or a predicted pathogen level from DNA, RNA or protein or antibody measurements in the nose, nasopharynx, mouth, blood or other body fluid. 
     In one implementation, the predictive model performs a wavelet decomposition on the time series for the wellness-relevant parameter to provide a set of wavelet coefficients. The wavelet coefficients can themselves be used as features for the predictive model or they can be aggregated into one or more composite features. For example, a weighted combination of at least a portion of the set of wavelet coefficients can be generated with the weights assigned during a training process of the predictive model. Alternatively, a center of mass of a two-dimensional array based on the set of wavelet coefficients can be generated to provide features for the predictive model. The predictive model can also use intermediate predictions as features in assigning the value. For example, future values for one or more wellness-related parameters can be predicted from the monitored data and then used as features in the predictive model. 
     In one example, the predictive model can utilize feedback to adjust parameters associated with the predictive model, for example, via retraining of the model or the use of a reinforcement learning process on one or more specific parameters, such as decision thresholds for generating categorical values from continuous outputs. In this implementation, an outcome associated with the user is measured and compared to the value assigned to the user via a predictive model. A parameter associated with the predictive model is changed according to this comparison. 
       FIG.  16    illustrates another example of a method for monitoring a wellness of a user. At  302 , a plurality of wellness-relevant parameters representing the user are monitored at a wearable device over a defined period to produce respective time series for the monitored parameter. At  304 , a set of features representing the user are extracted from the time series for the plurality of wellness-relevant parameters. The set of features includes a predicted value for at least one of the plurality of wellness-relevant parameters, such as heart rate variability or body temperature. At  306 , a value is assigned to the user via a predictive model according to the set of features. 
       FIG.  17    is a schematic block diagram illustrating an exemplary system  400  of hardware components capable of implementing examples of the systems and methods disclosed herein. The system  400  can include various systems and subsystems. The system  400  can be a personal computer, a laptop computer, a workstation, a computer system, an appliance, an application-specific integrated circuit (ASIC), a server, a server BladeCenter, a server farm, etc. 
     The system  400  can include a system bus  402 , a processing unit  404 , a system memory  406 , memory devices  408  and  410 , a communication interface  412  (e.g., a network interface), a communication link  414 , a display  416  (e.g., a video screen), and an input device  418  (e.g., a keyboard, touch screen, and/or a mouse). The system bus  402  can be in communication with the processing unit  404  and the system memory  406 . The additional memory devices  408  and  410 , such as a hard disk drive, server, standalone database, or other non-volatile memory, can also be in communication with the system bus  402 . The system bus  402  interconnects the processing unit  404 , the memory devices  406 - 410 , the communication interface  412 , the display  416 , and the input device  418 . In some examples, the system bus  402  also interconnects an additional port (not shown), such as a universal serial bus (USB) port. 
     The processing unit  404  can be a computing device and can include an application-specific integrated circuit (ASIC). The processing unit  404  executes a set of instructions to implement the operations of examples disclosed herein. The processing unit can include a processing core. 
     The additional memory devices  406 ,  408 , and  410  can store data, programs, instructions, database queries in text or compiled form, and any other information that may be needed to operate a computer. The memories  406 ,  408 , and  410  can be implemented as computer-readable media (integrated or removable), such as a memory card, disk drive, compact disk (CD), or server accessible over a network. In certain examples, the memories  406 ,  408  and  410  can comprise text, images, video, and/or audio, portions of which can be available in formats comprehensible to human beings. 
     Additionally or alternatively, the system  400  can access an external data source or query source through the communication interface  412 , which can communicate with the system bus  402  and the communication link  414 . 
     In operation, the system  400  can be used to implement one or more parts of a system for monitoring a wellness of a user in accordance with the present invention. Computer executable logic for implementing the monitoring system resides on one or more of the system memory  406 , and the memory devices  408  and  410  in accordance with certain examples. The processing unit  404  executes one or more computer executable instructions originating from the system memory  406  and the memory devices  408  and  410 . The term “computer readable medium” as used herein refers to a medium that participates in providing instructions to the processing unit  404  for execution. This medium may be distributed across multiple discrete assemblies all operatively connected to a common processor or set of related processors. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments can be practiced without these specific details. For example, physical components can be shown in block diagrams in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques can be shown without unnecessary detail in order to avoid obscuring the embodiments. 
     Implementation of the techniques, blocks, steps, and means described above can be done in various ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof. 
     Also, it is noted that the embodiments can be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart can describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations can be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function. 
     Furthermore, embodiments can be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and/or any combination thereof. When implemented in software, firmware, middleware, scripting language, and/or microcode, the program code or code segments to perform the necessary tasks can be stored in a machine readable medium such as a storage medium. A code segment or machine-executable instruction can represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and/or program statements. A code segment can be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, and/or memory contents. Information, arguments, parameters, data, etc. can be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, ticket passing, network transmission, etc. 
     For a firmware and/or software implementation, the methodologies can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions can be used in implementing the methodologies described herein. For example, software codes can be stored in a memory. Memory can be implemented within the processor or external to the processor. As used herein the term “memory” refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored. 
     Moreover, as disclosed herein, the term “storage medium” can represent one or more memories for storing data, including read only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, wireless channels, and/or various other storage mediums capable of storing that contain or carry instruction(s) and/or data. 
     What have been described above are examples. It is, of course, not possible to describe every conceivable combination of components or methodologies, but one of ordinary skill in the art will recognize that many further combinations and permutations are possible. Accordingly, the disclosure is intended to embrace all such alterations, modifications, and variations that fall within the scope of this application, including the appended claims. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on. Additionally, where the disclosure or claims recite “a,” “an,” “a first,” or “another” element, or the equivalent thereof, it should be interpreted to include one or more than one such element, neither requiring nor excluding two or more such elements.