CONFEDERATED MACHINE LEARNING ON HORIZONTALLY AND VERTICALLY SEPARATED MEDICAL DATA FOR LARGE-SCALE HEALTH SYSTEM INTELLIGENCE

A patient ’ s health information is generally fragmented across silos . Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system , privacy concerns and regulatory barriers limit data centralization . Machine learning can be conducted in a federated manner on patient datasets with the same set of variables , but separated across sites of care . But federated learning can not handle the situation where different data types for a given patient are separated vertically across different organizations . We call methods that enable machine learning model training on data separated by two or more degrees “ confederated machine learning. ” We built and evaluated a confederated machine learning model to stratify the risk of accidental falls among the elderly . 1 INTRODUCTION . Significance . Access to a large amount of high quality data is possibly the most important factor for success in advancing medicine with machine learning and data science . However , valuable healthcare data are usually distributed across isolated silos , and there are complex operational and regulatory concerns . Data on patient populations are often horizontally separated , each other across different practices and health systems . In addition , individual patient data are often vertically separated , by data type , across her sites of care , service , and testing . We train a confederated learning model in a manner to stratify elderly patients by their risk of a fall in the next two years , using diagnoses , medication claims data and clinical lab test records of patients . Traditionally , federated machine learning refers to distributed learning on horizontally separated data ( Yue Zhao , Meng Li , Liangzhen Lai , Naveen Suda , Damon Civin , Vikas Chandra 2018 ; Cano , Ignacio , Markus Weimer , Dhruv Mahajan , Carlo Curino , and Giovanni Matteo Fumarola 2016 ; H. Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , Blaise Agüera y Arcas 2016 ; Anon n.d. ) . Algorithms are sent to different data silos ( sometimes called data nodes ) for training . Models obtained are aggregated for inference . Federated learning can reduce data duplication and costs associated with data transfer , while increasing security and shoring up institutional autonomy . ( Geyer , R. C. , Klein , T. , Nabi , M. 2017 ; al . 2016 ) , ( Yue Zhao , Meng Li , Liangzhen Lai , Naveen Suda , Damon Civin , Vikas Chandra 2018 ; al . 2015 ) ( Geyer , R. C. , Klein , T. , Nabi , M. 2017 ; al . 2016 ) . Notably , a patient ’ s vertically separated data may span data types–for example , diagnostic , pharmacy , laboratory , and social services . Machine learning on vertically separated data has used a split neuron network ( Praneeth et al . 2018 ) and homomorphic encryption ( Praneeth et al . 2018 ; Stephen et al . 2017 ) . However , these new methods require either information communication at each computational cycle or state-of-art computational resource organization , which are usually impractical in many healthcare systems where support for data analysis is not the first priority , high speed synchronized computation resources are often not available , and data availability is inconsistent . To accelerate a scalable and collaborative rapid learning health system ( Friedman et al . 2010 ; Mandl et al . 2014 ) , we propose a confederated machine learning method that trains machine learning models on data both horizontally and vertically separated by jointly learning a high level representation from data distributed across silios ( Qi et al . 2017 ; Zhang Xiao 2015 ; Zhai et al . 2014 ) . This method does not require frequent information exchange at each training epoch nor state-of-the-art distributed computing infrastructures . As such , it should be readily implementable , using existing health information infrastructure . We demonstrate this approach by developing a model of accidental falls among people at least 65 years , a problem which causes approximately 50.7 deaths per 100,000 in the US annually ( Anon 2019 ) . Women and men from 65 to 74 years old had a 12-month fall incident rate of 42.6 and 41.3 per 100 , respectively ; once over 74 years old , the incident rate climbed to 50.6 and 62.0 per 100 respectively , according to the 2008 National Health Interview Survey in 2008 ( Verma et al . 2016 ) . Nationally , the direct medical costs attributable to falls is 34 billion dollars ( Kramarow et al . 2015 ; Verma et al . 2016 ; Heinrich et al . 2010 ; Haasum Johnell 2017 ; Yang et al . 2016 ; Dollard et al . 2012 ; Overstall 1985 ; Lord et al . 2007 ) . There are highly effective approaches to mitigating the risk of falls that could be selectively applied to individuals identified as being at high risk . These include medication adjustments , exercises , and home interventions ( McMurdo et al . 2000 ; Kosse et al . 2013 ) . Multifactorial clinical assessment and management can reduce falls by more than 20 We train a confederated learning model to stratify elderly patients by their risk of a fall in the next two years , using horizontally and vertically separated diagnosis data , medication claims data and clinical lab test records of patients . The goal is to compare confederated learning with both centralized learning and traditional federated learning , and specifically test whether a confederated learning approach can simultaneously address horizontal and vertical separation . 2 METHODS . 2.1 DATA SOURCE AND COHORT . The study uses claims data from a major U.S. health plan . Elements include the insurance plan type and coverage periods , age , sex , medications , and diagnoses associated with billed medical services , from July 1 2014 to June 31 2017 . The dataset contains an indicator for insurance coverage by month . Only beneficiaries over the age of 65 by the beginning of the study period , and having full medical and pharmacy insurance coverage during the 36-month period were included . The study period is divided into a 12-month observational period , a 1 week gap period and a follow-up period of 23 months and 3 weeks . Individuals not enrolled in the Medicare Advantage program were excluded to ensure completeness of the private and public insurance data . In addition , members with fall-related diagnoses within observational or gap were excluded . For each individual , the predictive model uses claims from a 12-month observation period from the start of the study period . The outcomes ( claims indicative of falls ) are measured during the follow period .. The study cohort comprises 119,335 beneficiaries , with 56.6 % female . The input features to the confederated machine learning model include age , gender , diagnoses as ICD 9 or ICD 10 codes , medications represented as National Drug Codes ( NDC ) and lab tests ( encoded as LOINC codes ) . Lab test results were not available for this study . On average , each individual has 13.6 diagnoses , 6.9 prescriptions , and 7.4 LOINC codes during the 12 month observational period . 10,584 ( 8.9 % ) of beneficiaries in the cohort had at least one fall in the 21-month testing period . The number of people and falls from each state are summarized in Supplementary Table 1 . 2.2 STUDY OUTCOME . An online International Classification of Diseases , Ninth and tenth Revision ( ICD-9 and ICD-10 ) lookup resource provided by the Centers for Medicare and Medicaid was used to obtain all codes directly associated with an accidental fall , for example , fall from a sidewalk curb ( E880.1 ) . For each member , we marked with a binary outcome variable ( 0 or 1 ) of whether a person had any fall-related claims during the follow-up period . A total of 690,295,549 medical claim diagnoses were coded in ICD-9 and 900,713,946 were in ICD-10 . A total of 84 ICD-9 ( Homer et al . 2017 ) and 330 ICD-10 ( Hu Baker 2012 ) codes were used to identify falls . 2.3 CONFEDERATED MACHINE LEARNING . For a specific state s ∈ 1 , 2 , ... S with S = 34in this study , Each individual i has a , diagnosis vector Xdiagsi , a medication claim vector X med si from pharmacy , lab test vector X lab si from clinical lab and a binary label Ysi , where in each state i ∈ 1 , 2 , .... , ns with ns being the number of beneficiaries in state s . At a high level , confederated learning can be intuitively understood as follows : a universal machine learning model that has both representation learning and classification components is designed for all data sites . The representation learning component takes as input a variety of input features drawn from different types of data , which can not be presumed to all be present at the same time ( Figure 1 and 2 ) . We took the following steps : Step 1 .. Claims for diagnoses , medications and lab tests during the observation period are the input features . The output of the classifier is a binary variable indicating whether the beneficiary had a fall during the follow up period . We simulate horizontal and vertical separation by separating the data for beneficiaries by U.S. state of residence . We simulate vertical separation by assuming that beneficiaries ’ diagnoses are only available in simulated clinics , medication claims data are only kept in simulated pharmacies and lab data only in simulated labs . Data are presumed to not be shared among different organizations nor across state lines . In total , we simulated data distributed across 102 distinct nodes including 34 clinic nodes,34 pharmacy nodes and 34 lab nodes . Step 2 . For the distributed model training , each site was delivered an array of binary target labels ( fall or no fall during the 2 year follow-up period ) , linkable to the individual beneficiary . Step 3 . A neural network model f is designed.Two different branches of input neurons ( layer ζ1 ) and their directly linked connection to the next fully connected hidden layer ( layer ζ2 ) were used as learning subcomponents for each input data type ( Figure 1 and 2 , and see next section for details ) : medications or lab tests . After the two branches merges , the second hidden serve as a high level representation learning and joining layer ( layer ζ3 ) to integrate representation learned from different data types . The third layers is a classifier layer ( layer ζ4 ) . it is worth pointing out that the representation and classifying power may be not completely separated by each layer in the neural network models . The parameter Θof f is randomly initialized . Step 4 . The confederated learning model is trained as shown in Figure 1 and 2 . Models with identical structures and parameters are sent to all the 102 nodes together with the binary label of falls in the follow-up period . When the machine learning model is trained on a specific data type , such as medication claims , only the representation learning subcomponents for that data type are active and subcomponents for other data types are frozen . This is implemented by sending a data availability indicator to each data source to indicate whether a certain data type d is available at each site . For example , ωPharmacy = [ 1 , 0 ] can be used to indicate that medication data is available at a pharmacy but lab data and diagnoses are not , where ωPharmacymed = 1 , ω Pharmacy diag = 0 and ω Pharmacy lab = 0.ωis used as input into layer ζ3 . Step 5 . We aggregated trained parameters from all sites by averaging the parameters to produce the updated model . By doing this , the data type specific components ( ζ1 and ζ2 , joint representation component ( ζ3 ) and classification components ( ζ4 ) were learned simultaneously from different data types and individuals from all 102 sites . Information sharing among vertical separated data is achieved by joining the the representation at ζ3 . Step 6 . After model aggregation , the updated model was sent out to all 102 sites again to repeat the global training cycle .

Summary: This paper considers the problem of learning from medical data that is separated both horiontally (across different practices and centers) and vertically (by data type). The contribution is a "confederated" machine learning method that learns across these divides. The particular application considered here as means of illustration is that of fall prediction in the elderly. Specifically the authors investigate an ML approach to risk-stratifying elderly patients with respect their likelihood of falling in the next two years.