METHOD AND SYSTEM FOR A MOBILE HEALTH PLATFORM

Aspects of the present disclosure involve systems, methods, computer program products, and the like, for tracking, assessing and predicting human behavioral disorders in real time through a mobile device. In general, the mobile health platform involves tracking a geographical location of a user of the system through the mobile device, receiving environmental and user-provided information through the mobile device or from another source, and processing the received information. In one embodiment, the processing of the received information provides for a prediction of a future human behavior and such a prediction may be provided to the user's mobile device. For example, the information may indicate that a user of the mobile device is at risk for a particular human behavior and, as a result, a warning of the risk of the human behavior is transmitted to the user's mobile device.

FIELD OF THE DISCLOSURE

Embodiments of the present invention generally relate to systems and methods for implementing a health platform utilizing a mobile device, and more specifically for analyzing environmental information and user-provided information to predict a potential human behavior and/or provide a warning to the user of the mobile device of a potential human behavior.

BACKGROUND

Advances in genetics have provided a vast understanding of the genetic influences on human behavior, such as drug use and addiction. However, little is known about non-genetic influences, known collectively as the environment, on general human behaviors. For example, real-time assessment of exposure to, and responses to, drugs and psychosocial stress and assessment of how such exposures and responses vary across geographical locations is typically not examined and may be useful in understanding the causes of certain kinds of human behavior. With such environmental-specific analysis, studies of the genetics of human behavior may become more sensitive to the effects of genes whose roles in behavior are subtle or environmentally specific.

SUMMARY

One implementation of the present disclosure may take the form of system for providing an intervention notice to a user of a mobile device. The system may comprise a network communication port for receiving a transfer of data from a mobile computing device, the received data from the mobile computing device comprising at least one indication of a geographic location of a user of the mobile computing device, a database configured to store the received data from the mobile computing device, and a computing device. The computing device may include a processing device and a computer-readable medium with one or more executable instructions stored thereon, wherein the processing device of the computing device executes the one or more instructions to perform certain operations. Such operations performed by the processing device may include receiving environmental risk mapping information associated with the user of the mobile computing device and executing predictive analytics on the correlated environmental risk mapping information with at least one indication of the geographic location of the user of the mobile computing device, the predictive analytics comprising a predicted behavior of the user of mobile computing device. Further, the processing device may transmit an automated decision to the mobile computing device through the network communication port, the automated decision configured to cause the mobile computing device to generate an intervention indicator for the user of the mobile computing device to alter the predicted behavior of the user.

Another implementation of the present disclosure may take the form of a computer-implemented method for an automated assessment of the momentary status of a user. The method may include the operations of receiving a transfer of data from a mobile computing device associated with a user through a network connection, the received data from the mobile computing device comprising at least one indication of a geographic location of the user and storing environmental risk mapping information and the received data from the mobile computing device in a database. In addition, the computer-implemented method may include executing predictive analytics on the environmental risk mapping information with at least one indication of the geographic location of the user to generate a future prediction for the status of the user based on a machine learning model and transmitting an automated decision to the mobile computing device through the network, the automated decision configured to cause the mobile computing device to generate an intervention indicator for the user to alter the predicted status of the user.

Yet another implementation of the present disclosure may take the form of one or more non-transitory tangible computer-readable storage media storing computer-executable instructions for performing a computer process on a machine. The performed computer process includes the operations of receiving initial user data from a user of a human behavior intervention system, storing the initial user data in a user database with an environmental risk mapping information obtained from a third party database, and receiving a transfer of data from a mobile computing device through a network connection, the received data from the mobile computing device comprising at least one indication of a geographic location of a user of the mobile computing device. The process may also include correlating the received environmental risk mapping information with at least one indication of the geographic location of the user of the mobile computing device, executing predictive analytics on the correlated environmental risk mapping information with at least one indication of the geographic location of the user of the mobile computing device, the predictive analytics comprising a predicted behavior of the user of mobile computing device, and transmitting an automated decision to the mobile computing device through the network communication port, the automated decision configured to cause the mobile computing device to generate an intervention indicator for the user of the mobile computing device to alter the predicted behavior of the user.

DETAILED DESCRIPTION

Aspects of the present disclosure involve systems, methods, computer program products, and the like, for tracking, assessing and predicting human behavioral disorders in real time through a mobile device. In general, the mobile health platform involves tracking a geographical location of a user of the system through the mobile device, receiving environmental and user-provided information through the mobile device or from another source, and processing the received information. In one embodiment, the processing of the received information provides for a prediction of a future human behavior and such a prediction may be provided to the user's mobile device. For example, the information may indicate that a user of the mobile device is at risk for a particular human behavior and, as a result, a warning of the risk of the human behavior is transmitted to the user's mobile device. In one embodiment, the mobile device may provide some indication of the perceived risk to the user of the device.

In a further embodiment, a machine learning process may be employed within the mobile health platform to improve and tune the prediction of future human behavior. Thus, information obtained from one or more users of the mobile health platform may be provided to the machine learning process, as well as the accuracy of provided predictions of the human behavior. Through multiple iterations of the processing and application of information to provide a prediction, the mobile health platform may become more and more accurate over time. Further, the predictions of the mobile health platform may adjust to new environmental information or types of users through the machine learning process. In one embodiment, the prediction of the mobile health platform may be specific to an individual user. In other embodiments, groups or other subsets of the users of the system may be analyzed to provide a prediction.

In one particular implementation of the mobile health platform, analysis and prediction of human behaviors may be applied for individuals suffering with drug addiction. In practice, the mobile health platform provides an automated prediction for a future risk of drug use in real time as an individual goes about their daily life. Such a warning to an individual of a pending negative event may be received through a mobile device carried or otherwise corresponding to the user. However, while the present disclosure was initially developed as a mobile intervention for drug addition, its application is not limited to drug addiction or other risky behaviors. Rather, the mobile health platform may be applied to any endeavor that includes human behaviors, from risk management of an organization to advertisement for purchasing of a good. In general, the platform is envisaged to be utilized and applied to any intervention requiring behavioral change.

In particular, the mobile health platform described herein can be applied to tracking mental health disorders and generating an automated prediction for the risk of a possible negative behavioral episode. The automated prediction could be delivered to either: i) the user, ii) a health professional, iii) both or iv) an other actor that could instigate change in the user. The following is a non-exclusive list of possible behavioral disorders that the mobile health platform could be used to change: 1) Attention Deficit Hyperactivity Disorder, 2) Drug abuse, 3) Alcohol use/abuse/Alcoholism, 4) Gambling addiction, 5) Alzheimer's Disease, 6) Binge eating and eating disorders, 7) Bipolar disorder, 8) Depression and depressive disorders, 9) Generalized anxiety disorder, 10) Mood disorders, 11) Panic disorder, 12) Post-traumatic stress disorder, and 13) Cigarette smoking. However, it should be appreciated that the systems and methods described herein may be utilized for any purpose in providing a notification to a user of a mobile device.

One advantage of accurate assessment of environmental exposure (to stressors, drug availability, or drugs themselves) is minimizing the delay between exposure and reporting. The tools proposed and discussed herein, such as personal digital assistants (PDAs) and/or global positioning system (GPS) units, are those which users can carry with them during their daily routines, enabling them to report stress and drug use as they occur. Proximate self-reported data collection may occur in real time and be compared to data from standard retrospective self-report methods and to biological measures, all from the same users. Further, obtaining real-time geographic-location data allows for evaluation of the roles of different neighborhoods or areas, which will be compared to standard fixed demographic indicators such as current address.

FIG. 1is a schematic diagram illustrating a general process of a mobile health platform system. In general, the operating process of the mobile health platform includes the transmission of information between various components, devices, and machines of the platform. For example, a mobile device17may be utilized to track a user's position, receive information from the user, and provide one or more messages or other indicators to the user. A server7or other network device may receive information from the mobile device17and process the information as described below. A database9may be in communication with the server7to store received information and/or instructions executed by the server to perform one or more of the operations described below. Further, although particular components of the mobile health platform are described herein, it should be appreciated that any number of additional components may be included in the system to facilitate the collection, transmission, and processing of the information discussed without deviating from the operation of the system described herein. For example, the mobile device17may communicate wirelessly with the server7through a telecommunications network comprising any number of telecommunication devices, connections, routers, and the like. Thus, although some components of the mobile health platform are discussed herein, other components may be included as understood by those of ordinary skill in the art.

To begin the process of the mobile health platform, a study user or clinic patient1provides user information to a clinical setting3. “Clinical setting” may include any device7where health and behavioral data5may be received and collected by a machine and stored in a database9. For example, the machine7may be a server, laptop, personal computer, tablet, or any other computing device that receives information5about the user1. Further, the machine7may be in communication with the database9for storing the received information5, among other received information. The user information may be provided to the machine7from the user himself, from another computing device (such as a mobile device), or from a user or operator of the machine.

In one embodiment of the process of the mobile health platform, the machine7may also collect data on the user's natural environment13through a data transfer27to assess exposure to environmental risks or behavioral triggers. Such information may be provided to the machine7through a mobile device17associated with the user1. Further, additional data on the user's natural environment may be transmitted and stored as digital geo-referenced environmental-risk maps15in the database9. The environmental-risk maps15are constructed from interpolated numbers that can represent 1) independent observers' ratings of risk (or events and/or measures contributing to risk) in the geographical regions where the user spends time and/or moves (i.e., activity space) or 2) user-entered locations of the sites of negative events. The maps15can also be derived from third party databases of public information, such as crime data, or commercial density and locations, such as liquor stores, bars and/or shopping centers, income information (such as from tax data), and the like. For example, areas listed as “high crime” areas from a third party database may be included in the environment risk maps15and provided to the database9for storing. In another example, databases available through a public network, such as the Internet, may be mined for information to provide to the database9of the mobile health platform. Areas or other information of the environment risk mapping15may be used as described further below to determine when a user1is in a risky situation and an intervention or warning is provided to the user. The numbers used in the environmental risk maps15can be “presence only” indicators or can be dichotomous, categorical, or continuous measures.

In general, after the initial/periodic collection of data5at the clinic machine7, the user (indicated inFIG. 1as user11, although user1and user11may be the same actor) returns to his or her natural environment13to go about daily-life activities. During this time, a technique referred to herein as Geographical Momentary Assessment (GMA) is used to collect information about the user's behavior in the natural environment on a handheld device17. The GMA is a combination of a Geographical Positioning System (GPS)19and Ecological Momentary Assessment (EMA)21that can be done on one or more mobile devices17. For example, GMA data may include movement (i.e., track) information (GPS)19and user self-reports regarding moods and behaviors (EMA)23. A GPS unit19in communication with the mobile device17collects time-stamped movement (i.e., track) information about a user's location. An EMA unit21, which can be integrated with or separate from the GPS unit, allows the user to report moods and behaviors as they occur23. For example, a program may be executed by the mobile device17that provides an interface through which a user of the mobile device provides the EMA information23. EMA data23may be collected in at least two ways: 1) randomly timed prompts to complete on-screen questionnaires, and 2) user-initiated event reports whenever a behavior of interest occurs (e.g., smoking, binge eating, or any other behavior of interest to the mobile health platform).

In addition, intensive ambulatory physiological monitoring25can be added to the GMA through one or more components of the mobile device17. For example, one or more sensors, such as accelerometers, may be in communication with the mobile device17to collect physiological information about a user in real time in the natural environment. In another example, a blood pressure device may be worn by the user11and the user's blood pressure may be monitored and included in the EMA21. The above described collected data is transferred27to the clinical machine7and stored in the database9. In general, the data transfer can be done: 1) directly via a hardwire connection in the machine7or an accompanying network, 2) remotely via Bluetooth (or another wireless data transmission process), 3) through an automated data dump via secure html over the Internet when the user is in their natural environment, 4) via an automated cloud connection when the user is in their natural environment, and the like.

In one embodiment of the mobile health platform system, the database9may contains: 1) user data collected in the clinical setting5from the user1, environmental risk maps made independently of or concurrently with the user15, and GMA data (EMA data21, GPS location data19, ambulatory physiological monitoring data25, user-reported data23, etc.) collected from the user in the natural environment17. The data received at the clinical setting3may be processed and analyzed by the machine17, as indicated in box29ofFIG. 1. The output of the processing and analyses29is an automated decision31predicting potential future events that are behaviors of clinical interest to the user11. The machine7next transmits the automated decision through the data transfer27back to the mobile device17of the user by any of the methods mentioned above. In some instances, the mobile device17displays the prediction of a future event on the screen while the user is in their natural environment31to invoke a reaction by the user11. The display of the automated prediction is referred to as the intervention33. The intervention33can then be coupled to other information transmitted to the user via the mobile device17that helps the user cope with, and potentially prevent, a negative event.

FIG. 2is a schematic diagram illustrating a process for developing a database109in relation to a mobile health platform system. The database109ofFIG. 2may be similar to the database described above with reference toFIG. 1. As described, information is obtained by the machine107of the clinical setting101and stored in the database109. In particular, clinical data105collected concerning a user103through the machine107may be stored in a database109. Environmental risk mapping111is obtained from one or more third party or public databases and completed on the machine107and also stored on a database109. In a natural environment113, a user115may carry a GMA unit or mobile device117, which collects data on the user's movement and records the user's responses to questions about events119. The data collected by the GMA unit117are transferred121back to a machine107and stored on a database109. This data transfer121may occur over any communication medium known or hereafter developed, including but not limited to, wired communication over a network, wireless communication over a network, a combination of wired and wireless networks, mobile hard drive, etc. Box123ofFIG. 2illustrates some examples of the content of the database109organized into three types of directories or sub-databases: 1) clinical data125, 2) GMA data127and 3) environmental data collected independently of the user129. Examples of these three categories of data stored in a database are provided in list125, list127and list129and in relation to drug addiction. In general, however, the information stored in the database109may include any information associated with a human behavior and may be organized in any manner.

FIG. 3is a schematic diagram illustrating a process for a data processing analytics sequence201utilized with a mobile health platform system. Through the process illustrated inFIG. 3, a prediction of behavior may be generated by a machine203or computing device and delivered to a user via a GMA unit253or mobile device to act as an intervention255event for the user. The process starts when the machine203(such as a network server or other computing device) is used to access data stored in a database205. One example of the data included in the database205is illustrated inFIG. 2. The machine203executes a Pre-Processing operation207, which is broken into two types of data flows. The first data flow209deals with pre-existing209or previously collected data that is in the database205(independently of new data221collected from the user in real time). The second data flow221is for newly received or stored data in the database205. Both Pre-existing209and New Data221processing flows follow a similar processing sequence. In general, the difference between the two sequences is that they may also include data from other users of the mobile health system and/or other outside sources to be combined with a specific user's data. Pre-existing Data209may also be used to develop automated data analytics that are applied to New Data as they come in via real-time transfer. New Data209are data collected from a user and transferred (through data transfer27,121) to the database in real time from a user, a user's mobile device, or other third party database or computing device.

In the pre-processing207step for pre-existing data209, the machine203identifies raw data that may be of poor quality and flags them so that they will not be used in the processes of the system. In one embodiment, identification of poor-quality data is completed mathematically by computer-implemented statistical operations executed by the machine203. The data that are not identified as of poor quality are combined213by the machine203. In general, combining the data can be completed as a spatial join or intersect215with other spatial data or as a temporal join217with non-spatial data that have a timestamp. These two operations, as is possible with most of the operations described herein, are interchangeable in their order of operations. The spatial join or intersect215is completed with the GMA data, for example, by using the longitude and latitude collected by the GPS component of the mobile device17. The GPS data are then spatially overlaid on digital environmental-risk maps by the machine203. For example, the GPS data are used to sample the environmental-risk maps at the relevant longitudes and latitudes. The output is a new GIS shape file or text file (i.e., .txt or csv file) with the GPS data combined to the environmental-risk map data15. The temporal join217is completed, for example, by intersecting the data collected by the GPS component with time stamps of data that are not geographically referenced. The term “joining by timestamp” may include combining or fusing different information about a user11(i.e., collected by different devices or sensors) into comparable increments in time. For example, the devices used for EMA, GPS, and intensive ambulatory physiological monitoring25can collect data simultaneously, but at different temporal frequencies: the EMA data might be collected sporadically, reflecting a handful of events per day, while the GPS data might consist of multiple events per minute or hour, and the physiology data can be collected at the sub-second level. In some instances, the data cannot be analyzed without being joined together due to the difference in temporal collecting of the data. The joining by timestamp described herein allows the EMA21to be connected to GPS19and/or the physiological sensors25, and the GPS timestamp allows the EMA and physiological sensors to be linked spatially to environmental-risk maps15. Once the data are joined together, a final Pre-Processing operation is utilized to aggregate the joined data to comparable spatial or temporal units that can be fed into the computational data analytics. This may include aggregating high-frequency data by averaging the joined data (i.e., by space and/or time) over larger incremental instances, such as one replicate every 10, 20, or 30 minutes. Aggregating also produces consistent temporal data replication for randomly varying data, such as EMA21or speed-based GPS19data collection.

In the pre-processing207step for New Data221, the operations are implemented by a machine203after real-time GMA data are transferred27,121from the mobile device17. The New Data are accessed by a machine203and any poor-quality data are removed223from the dataset, similar to above. Identifying poor-quality data223may be completed mathematically by computer implemented pre-existing statistical operations that were developed as discussed above with reference to step211. The New Data are then combined225by spatial227and temporal229joining methods consistent with the same processes as in operation213described above. The New Data are then aggregated in231so that they are in units consistent with the aggregated data from step219.

After the data are Pre-Processed207, they are fed into a Data Analytics233sequence of processing steps. The outcome of the Data Analytics233processing sequence is an automated or manual decision (i.e., based on machine learning) that predicts a future event of interest for a user. The first step in the sequence233is to run Behavioral Statistics235to detect reliable relationships among GMA entries (i.e., “how much are you craving drugs?”), intensive ambulatory physiological data, and exposure to environmental risks. An example of a specific type of Behavioral Statistic235is multilevel or hierarchical mixed models. The Behavioral Statistics235are run on Pre-Existing Data237. The Behavioral Statistics235determine, for example, what kinds of environmental risk variables are related to specific EMA responses (such as drug craving) and what duration of environmental exposure most reliably predicts EMA responses. After the Behavioral Statistics235produce an outcome, the results of the outcome are used to guide the Machine Learning239analyses, as discussed below

The Machine Learning239is used to develop an automated inference for a future EMA response. The automated inference is based on Pre-Existing data241that are used to develop and test a training model. For example, the Behavioral Statistics in operation235could show that environmental risks such as drug paraphernalia on the sidewalk contribute to heroin craving, and that exposure to these risks 6 hours prior to the EMA entry are good predictors of EMA reports of craving. The machine learning239would then be set up to use the 6 hours of exposure data prior to an EMA response. To develop a future-predicting model, for example, at 30, 60 or 90 minutes into the future, the specific amount of time prior EMA entry is dropped from the 6 hours of data. Meaning, if the intent of the machine-learning model239is to predict heroin craving 90 minutes into the future with the environmental-exposure data, environmental-exposure increments representing time between 0 and 90 minutes before the event are dropped from the full exposure sample. Rather, if 6 hours of time are to be used to predict heroin craving 90 minutes into the future, these predictions would use environmental-exposure data between 91-420 minutes prior to the EMA event. The output of the machine learning239is a new model (stored as a new self-contained file) that infers an EMA-derived outcome from the Pre-Existing241data where the results are at an acceptable accuracy, such as a kappa greater than 0.6. Under these definitions for a machine-learning model239at an acceptable accuracy, we can use Pre-Existing data241to make an inference about New Data221without having to re-train the model every time New Data are transferred into the system. In step243, as New Data are transferred27,121into the system, the New Data are processed221. The output at231is then fed into the Machine Learning239output, which is the saved files from the machine learning model derived from Pre-Existing data209and which is at an acceptable accuracy. The New Data245, which are real-time data in some instances, are then run with the output from239to predict a future EMA response, for example, at increments of 30, 60, and 90 minutes into the future. The output from the prediction or inference is an automated decision247about a future event. In one embodiment, the automated decision247is a new number or output that is stored as a text file (i.e., txt, csv, etc.) and is transferred249back to the mobile device253in a similar capacity as described above. During the data transfer, the mobile device253is in use by the user in the natural environment251. If the automated decision247indicates a statistical likelihood that the user will experience a negative behavioral event in the near future, for example, heroin craving 30, 60, or 90 minutes into the future, the mobile device253will automatically instigate an intervention255. The specific mode of mobile-based intervention can vary, from a vibrating device, a sound, a flashing screen on the mobile device253, to a recommendation that the user use coping skills to reduce craving, leave the risky situation, or contact a source of support. In addition to an automated intervention, the user can also self-monitor their risk of a future negative event by checking the automated decision at their own discretion or at intervals throughout the day that are preset by the clinician. Thus, the mobile device253can raise the patient's awareness of risk and deliver the automated decision247in real time while the user is in the natural environment251.

FIG. 4is a schematic diagram illustrating a pre-processing sequence for a mobile health platform system. The illustration ofFIG. 4may be utilized by the system outlined above in relation toFIG. 3. In the embodiment shown, a Clean Data Sequence417starts401with new GPS data403collected from a user via a mobile device17,117. Raw GPS data403may have location error related to satellite signal quality. Thus, the mobile health system on machine7may utilize internal data-quality-control calculations made by the software on the receiver to determine the quality of the raw GPS data403. Sometimes the internal quality controls fail to be calculated, which leaves a certain proportion of GPS data403with no quality control. First, GPS data403with quality-control measures are used and classified as good405data or bad407data. The GPS data403thus classified are then transmitted to machine-learning software409, which is used to develop a predictive model with ancillary features in the GPS data such as altitude, distance, speed, and time. Once a new machine-learning model409is developed to a reasonable accuracy, the model is then implemented on GPS data with no quality-control values to classify them as good or bad. The new outputs411from the scoring are then used to remove potentially bad data. Next, a signal-processing filter413is run on the good GPS data405or411to further improve coordinate location. One example of a filter413that may be utilized in this process is a Kalman filter. The filtering413smooths longitude and latitude by timestamp and velocity recorded at each timestamp. A separate filter is run on each track recorded of GPS locations per user. The filtering413can also infer coordinates that are considered low quality by the machine learning flagging for good and bad quality data. In general, filtering413is an optional component to the process. The result of the filtering413is a New Output415for longitude and latitude per GPS recorded location, which is the end419of the Clean Data Sequence.

A Combine Data Sequence443may also be included or performed that starts421optionally with either combining the data over Time423or Space431. Combining over Time423may be used with a Time Stamp for Variable1425, normally GPS and/or mobile device data from415, to be combined mathematically with the Time-Stamp for Variable2427. Variable2can be any other data stored in the Database123as long as the data has a Time-Stamp. For example, Clinical Data125are recorded when a user is in a Clinic, and normally in less frequent periods than the GMA/GPS data. The Clinical Data125can be joined to GPS/GMA data that are recorded at more frequent intervals than the Clinical Data. Time-Stamp may include a unique value representing time that can be compared mathematically to another value. To combine Variable1and Variable2, the Time-Stamp of Variable2is subtracted429from the Time-Stamp of Variable1. The result of the subtraction429is a New Column439indicating the difference in time between Variable1and Variable2. The Combine Data Sequence for Space431uses a latitude (i.e., Lat) and a longitude (i.e., Long) for Variable1, which is the GPS/GMA data from415, to Join437to Variable2. Variable2435is any Map Data129stored on the Database123. The Join437of Variable1and Variable2includes GPS/GMA location coordinates being used to sample vector or raster data. The result of the Join437is a New Column439indicating the value from Variable2for each coordinate value Variable1. Creating the New Column439is the End441of the Combine Data Sequence443.

An Aggregate Data Sequence455is a processing step implemented to simplify the results of the Combine Data Sequence443for data output in the New Column439, which is included in the Aggregate Data Sequence as447. The Aggregate Data Sequence455is executed to convert these new raw numbers in447(linked to the GPS/GMA data on a row-by-row basis) to new numbers that can be processed through statistics and predictive analytics. This can be utilized when the GPS sampling is either i) too dense to discern any meaningful behavioral statistic or prediction (i.e., one replicate a minute per day) or ii) randomly varying in time, where the randomness reduces any numerical inference obtained from the behavioral statistics and predictive analyses for consistent replication in time. There are generally two ways to aggregate the new data447, either by time or by space. In operation449, aggregating by time is completed by either summing or averaging447the data to consistent units of time. For example, data447can be in any increment greater than a minute and/or used to create a consistent replication in time from randomly varying temporal data. If the Environmental Risk Maps15,111are sampled in437, output439is a new column with unique measure for each Environmental Risk Map at the precise latitude and longitude for each coordinate recorded by the GPS/GMA/mobile device. The time-stamps of the GPS/GMA/mobile device can then be used to aggregate449the unique environmental risk values per coordinate to consistent average values for the same unit in time. For example, a new column with a disorder map sampled to every GPS time stamp could be aggregated to average disorder value per 10, 20 or 30 minutes. In operation451, the new column of data447is either averaged or summed over space. For example, if the space data from431are joined to vector files, such as a boundary polygon representing a neighborhood map, the result in column439is a unique value per neighborhood for each coordinate collected by the GPS/GMA. The corresponding GPS/GMA and/or Environmental Risk Maps can then be aggregated as the sum or average of any of these values451per neighborhood. The result of either449or451is a New Output453for either the data aggregated by time or space.

FIG. 5is a schematic diagram illustrating a behavioral statistics sequence for a mobile health platform system. In one embodiment, the behavioral statistics sequence503is the same or similar behavioral statistics sequence235described above with reference toFIG. 3. The Behavioral Statistics Sequence235,503is an automated database learning system that is coupled to behavioral-statistics software. The process starts with the outputs of the aggregate data sequence455, shown inFIG. 5as box505. The Data Preparation507step is used here to either join509EMA responses21,117to the aggregated data505and/or complete additional processing for data manipulation511. In box509, the EMA responses are joined to the aggregate data output505by adding the responses as new columns in output505. To do this, output505may be first transposed from columns to rows as part of the joining process509. The Data Manipulation511completed here on the aggregate data output505adjusts the output joined with EMA responses509to specifically fit the input-parameter requirements for the behavioral-statistics software515. Additionally, output505joined to responses509can include additional data manipulation511to create specific time-series analyses, such as assessing the cumulative experience of environmental exposure (from environmental mapping111,129) prior to an EMA event logged by the mobile device117. For example, cumulative environmental exposures of up to twelve hours or more prior to the event logging in the mobile device17may be used to assess the relationship between environmental exposures at varying cumulative increments and the response in the GMA. This may be necessary because the behavioral-statistics software is not designed to run test statistics in time-series, so the temporal element in the data must be first manipulated to account for and normalize exposure values for time.

The Test Hypotheses processing sequence513is used to test whether information collected by the mobile device17on users' EMA responses21remains consistent with a priori predictions521as new cohorts of users are evaluated. Regression analysis software is used to assess the relationship between users' EMA responses21and any other data stored on the database123and processed through the Pre-Processing Sequences207shown inFIG. 4(417,443,455). An example here is an Environmental Risk Map15stored on the database9, processed through processes417,443, and455, and then through process507. It is after all the data processing through step507that the data from the Environmental Risk Map15may be quantitatively compared to the EMA responses. The regression model used here falls generally under the term multilevel model, which can also be called any of the following: hierarchical model, mixed model, or random-effects model. The data output from data preparation process507is thus plugged into regression analysis software515and the result is new output517. New output517can be any assortment of test-statistics describing the relationship between EMA responses and any other data stored on the database123—typically an odds ratio, a beta weight, or a group of model-adjusted means, all with associated confidence intervals.

The Behavioral Statistics Sequence503may also include the Prior Probabilities Database519. The database519contains empirically based estimates of the likelihood that a given new hypothesis is true, or that a parameter will assume some given range of values. After each run of the regression analysis software515and the generation of New Output517for parameter values, an automated code to Evaluate Parameter Estimates523may be executed. Here, the processing initially compares output517to the a priori predictions521. The next step is to determine whether to Revise the Prior Probabilities525. In one particular embodiment, a zero or low value in operation523means no and a one or positive value in operation523means yes. If the Revise Prior Probabilities525determines a no, the process ends531. Alternatively, if the Revise Prior Probabilities525is determined to be a yes, then there is internal feedback527to the Prior Probabilities Database519. As new data enter the Behavioral Statistics Sequence from the aggregate data output505, data preparation507, and test hypothesis process513, the processing is replicated, where evaluating parameter estimates523is tested against either a priori predictions521and/or user informed probabilities529. User informed probabilities529can be updated for test statistics as more and more user data enter the Behavioral Statistics sequence, which may cause the revise prior probabilities decision525to change in light of more data and/or as new EMA responses are uploaded.

FIG. 6is a schematic diagram illustrating a machine learning sequence for a mobile health platform system. The process starts601with a Data Preparation process603, where Input from the Behavioral Statistics605sequence is used to Manipulate Data607from output453and/or aggregate data new output505. The combination of input from behavioral stats605and the manipulated data607means that input data to the machine learning are used for data that are found to have robust predictive value in the test statistics from output517. One example is the calculation of a time window for significant relationships between environmental risk data and EMA responses. For example, some studies show significant relationships between environmental risk exposures and subjective responses up to 6 hours later. Informed by those findings, the Machine Learning Sequence would subset into a New Output609only data for the 6 hours immediately preceding each EMA time stamp. However, any subset of the data may be utilized by the sequence. In addition to this, when time-series-based predictions are used, the point is to predict a future response. In order to run these future predictions, for example at 30, 60 or 90 minutes into the future, data 30, 60 or 90 minutes in time immediately preceding the EMA time stamp are dropped from the time-series sample used to make the prediction. This additional data manipulation is part of operation607to output609.

Output609from the data preparation is used as the input to the Machine Learning239, which is broken into two sequences. The first sequence is illustrated as operation239ofFIG. 3and deals with running the Machine Learning Sequence on Pre-existing data611on the database. Predictor data613shows the possible data inputs that can be used as Predictor data, which include, but are not limited to: Map Data615, Clinical Data617, and GMA Data619. Predictor data613is used to predict the Target data621for any EMA Response623. Predictor data613and target data621can be at any increment in time prior to the EMA event (i.e., 30, 60 or 90 minutes into the future). Machine-learning software625is used to develop a training model that uses predictor data613to predict target data621. Machine learning software625can be, for example, a regression or classification predictor. Examples of machine-learning software625are support-vector machines, random Forests, and adaboost. The output from machine-learning software625is a new file that includes the newly developed machine-learning model627, allowing the system, as shown inFIG. 1, to make automated predictions.

As shown inFIG. 1, Data Transfer27may occur between the mobile device17in the Natural Environment13and the machine7in the clinical setting3.FIG. 2also shows the data transfer in121between the Natural Environment113and the machine107of the clinical setting101in database development. Additionally,FIG. 3shows how a Future Prediction243is made with New Data245in the Data Analytics233sequence. InFIG. 3, future prediction243shows this processing of real-time data to make future predictions, where process243is described in further detail in operation641ofFIG. 6. First, to get new real-time data, the mobile device631collects new data633in the natural environment635. The mobile device transfers data through data transfer637to the machine7. In general, the data are Pre-Processed through the sequences outlined above in relation toFIG. 5, and then through the data preparation operation603, so that it replicates the same data format and structure as was used in predictor data613. The Machine-Learning Sequence for Future Prediction of Real Time data is shown in sequence641. Here predictor data643is the same or similar to predictor data613, except the processing uses new, real-time data in predictor data643. The new model653created in the machine-learning software is then accessed with the machine-learning software651and processed with predictor data643. The output is a new data file655, which has been scored for a future EMA prediction657, for example, the EMA value at 30, 60, or 90 minutes into the future.

In review,FIG. 7illustrates a machine-implemented sequence for a mobile health intervention that is delivered as one attribute of the Mobile Health Platform. Sequence A starts at301, where a mobile device unit automatically collects data in operation303. Further detail is provided on operation303inFIG. 1utilizing the mobile device17(with GPS component19and EMA component21), self-reporting data23, and intensive ambulatory physiological monitoring components25and inFIG. 2. Returning toFIG. 7, the GMA data are transferred305back to the database307where they are combined with environmental-risk maps309that also reside in the database. Further descriptions of data transfer305, database307, and environmental risk maps309are provided in machine7, database9, and environment risk management15ofFIG. 1and machine107, database109, environmental mapping111, and data transfer121ofFIG. 2. In another operation of sequence A, predictive analytics (i.e., machine learning or data mining)311are run on the contents of the database to generate an automated decision313. Additional descriptions for predictive analytics operations311and automated decision313are provided in data processing and analytics29and automated decision31ofFIG. 1, pre-processing operations207and data analytics operations233ofFIG. 3and in the content related toFIGS. 4, 5 and 6. After an automated decision is generated313, it is transferred back to the mobile unit17in operation315and an automated intervention is triggered by the mobile unit in operation317if the user is at imminent risk of a future negative event. Further descriptions of steps data transfer315and mobile or GMA device intervention317are provided in steps data transfer27, mobile device17, and intervention33ofFIG. 1and operations249-255inFIG. 3. Sequence B replicates Sequence A, except in that the data transfer261occurs before the automated decision in step263. In relation toFIG. 3, Sequence B inFIG. 7means that steps243and245can occur on the mobile device253and come after step249. Some individual contents of the sequence can be omitted, changed, edited, adapted, or implemented as one full automated step.

FIG. 8is a schematic diagram illustrating a machine learning model database for a mobile health platform system. The system is based on a machine703that may be in a clinical setting701. At the end of the Machine Learning Sequence in705, a new model627and a new prediction655are saved in a database in707,611, and641. Database707is the Machine Learning Database, which stores an assortment of machine learning models and their new prediction outputs. The contents of the Machine Learning Database are shown in more detail in box709in the flowchart. Box709shows that the database is split into two parts 1) machine learning models and 2) the output from machine learning models: new predictions. The models can predict for either an individual person or a group of 2 or more people in any combination that yields an accurate model. This process can be used for either real-time momentary predictions or future predictions at any point into the future. Momentary predictions mean a prediction for the GMA at its present local time. A future prediction is a prediction result for an event at a specified increment beyond the GMA's present time, for example, 30, 60 or 90 minutes into the future. The machine learning database also stores the results to the machine learning models. When there is a data transfer to the GMA711,27, and249, the data stored on the Machine Learning Database707are transferred to715, which is the Machine Learning Database on the handheld mobile device711,17,253being carried by the user in the natural environment719. Database715stores the models and outputs that are most accurate for the user carrying that mobile or GMA device713. This means different GMA devices could store different machine learning models or results in database715, but all models and results are stored in database707on a machine709. Depending on the processing capabilities of the GMA, the data stored in database715could be either output627or output655. If the GMA device does not have the capacity to process data and the machine learning model, then output655is stored in database715. If the GMA device does have the capacity to process data and the machine learning model, then output627is stored on database715. The GMA713accesses database715to determine whether to initiate an intervention717. Database707can be stored in any kind of machine703that can be used to continuously update database707via data transfer27,121,637, which stores all data from all the GMA devices in the system. In addition, database707can be separated into multiple databases and/or integrated, updated, or merged with any of the databases described inFIGS. 1-7. The machine learning database in database715is only updated at regular increments in time (e.g., daily or weekly) when output627is stored on database715. When output655is stored on database715, the database is updated more frequently (e.g., every 10 mins, 20 minutes or 30 minutes, depending on data transfer rates in data transfer711).

FIG. 9is a block diagram illustrating an example of a computing device or computer system900which may be used in implementing the embodiments of the components of the network disclosed above. For example, the computing system900ofFIG. 9may be the machine7of the clinical setting3discussed above. The computer system (system) includes one or more processors902-906. Processors902-906may include one or more internal levels of cache (not shown) and a bus controller or bus interface unit to direct interaction with the processor bus912. Processor bus912, also known as the host bus or the front side bus, may be used to couple the processors902-906with the system interface914. System interface914may be connected to the processor bus912to interface other components of the system900with the processor bus912. For example, system interface914may include a memory controller914for interfacing a main memory916with the processor bus912. The main memory916typically includes one or more memory cards and a control circuit (not shown). System interface914may also include an input/output (I/O) interface920to interface one or more I/O bridges or I/O devices with the processor bus912. One or more I/O controllers and/or I/O devices may be connected with the I/O bus926, such as I/O controller928and I/O device940, as illustrated.

I/O device940may also include an input device (not shown), such as an alphanumeric input device, including alphanumeric and other keys for communicating information and/or command selections to the processors902-906. Another type of user input device includes cursor control, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processors902-906and for controlling cursor movement on the display device.

System900may include a dynamic storage device, referred to as main memory916, or a random access memory (RAM) or other computer-readable devices coupled to the processor bus912for storing information and instructions to be executed by the processors902-906. Main memory916also may be used for storing temporary variables or other intermediate information during execution of instructions by the processors902-906. System900may include a read only memory (ROM) and/or other static storage device coupled to the processor bus912for storing static information and instructions for the processors902-906. The system set forth inFIG. 9is but one possible example of a computer system that may employ or be configured in accordance with aspects of the present disclosure.

According to one embodiment, the above techniques may be performed by computer system900in response to processor904executing one or more sequences of one or more instructions contained in main memory916. These instructions may be read into main memory916from another machine-readable medium, such as a storage device. Execution of the sequences of instructions contained in main memory916may cause processors902-906to perform the process steps described herein. In alternative embodiments, circuitry may be used in place of or in combination with the software instructions. Thus, embodiments of the present disclosure may include both hardware and software components.

A machine readable medium includes any mechanism for storing or transmitting information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). Such media may take the form of, but is not limited to, non-volatile media and volatile media. Non-volatile media includes optical or magnetic disks. Volatile media includes dynamic memory, such as main memory916. Common forms of machine-readable media may include, but are not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions.