Personalized functional health and fall risk prediction using electronic health records and in-home sensor data
Authors:   , Mishra Anup Kumar 
Published: May 2022

Research has shown the importance of Electronic Health Records (EHR) and in-home sensor data for continuous health tracking and health risk predictions. With the increased computational capabilities and advances in machine learning techniques, we have new opportunities to use multi-modal health big data to develop accurate health tracking models. This dissertation describes the development, evaluation, and testing of systems for predicting functional health and fall risks in community-dwelling older adults using health data and machine learning techniques. In an initial study, we focused on organizing and de-identifying EHR data for analysis using HIPAA regulations. The dataset contained nine years of structured and unstructured EHR data obtained from TigerPlace, a senior living facility at Columbia, MO. The de-identification of this data was done using custom automated algorithms. The de-identified EHR data was used in several studies described in this dissertation. We then developed personalized functional health tracking models using geriatric assessments in the EHR data. Studies show that higher levels of functional health in older adults lead to a higher quality of life and improves the ability to age-in-place. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking the personalized functional health of older adults using a combination of these assessments. In this study, data from 150 older adult residents were used to develop a composite functional health prediction model using Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Tracking functional health objectively could help clinicians to make decisions for interventions in case of functional health deterioration. We next constructed models for fall risk prediction in older adults using geriatric assessments, demographic data, and GAITRite assessment data. A 6-month fall risk prediction model was developed with data from 93 older adult residents. Explainable AI techniques were used to provide explanations to the model predictions, such as which specific features increased the risk of fall in a particular model prediction. Such explanations to model predictions provide valuable insights for targeted interventions. In another study, we developed deep neural network models to predict fall risk from de-identified nursing notes data from 162 older adult residents from TigerPlace. Clinical nursing notes have been shown to contain valuable information related to fall risk factors. This analysis provides the groundwork for future experiments to predict fall risk in older adults using clinical notes. In addition to using EHR data to predict functional health and fall risk in older adults, two studies were conducted to predict fall and functional health from in-home sensor data. Models for in-home fall prediction using depth sensor imagery have been successfully used at TigerPlace. However, the model is prone to false fall alarms in several scenarios, such as pillows thrown on the floor and pets jumping from couches. A secondary fall analysis was performed by analyzing fall alert videos to further identify and remove false alarms. In the final study, we used in-home sensor data streaming from depth sensors and bed sensors to predict functional health and absolute geriatric assessment values. These prediction models can be used to predict the functional health of residents in absence of sparse and infrequent geriatric assessments. This can also provide continuous tracking of functional health in older adults using the streaming in-home sensor data.