Feasibility Study of Monitoring Deterioration of Outpatients Using Multimodal Data Collected by Wearables

Abstract

Hospital readmission rate is high for heart failure patients. Early detection of deterioration will help doctors prevent readmissions, thus reducing health care cost and providing patients with just-in-time intervention. Wearable devices (e.g., wristbands and smart watches) provide a convenient technology for continuous outpatient monitoring. In the paper, we explore the feasibility of monitoring outpatients using Fitbit Charge HR wristbands and the potential of machine learning models to predicting clinical deterioration (readmissions and death) among outpatients discharged from the hospital. We developed and piloted a data collection system in a clinical study which involved 25 heart failure patients recently discharged from a hospital. The results from the clinical study demonstrated the feasibility of continuously monitoring outpatients using wristbands. We observed high levels of patient compliance in wearing the wristbands regularly and satisfactory yield, latency and reliability of data collection from the wristbands to a cloud-based database. Finally, we explored a set of machine learning models to predict deterioration based on the Fitbit data. Through 5-fold cross validation, K nearest neighbor achieved the highest accuracy of 0.8800 for identifying patients at risk of deterioration using the health data from the beginning of the monitoring. Machine learning models based on multimodal data (step, sleep and heart rate) significantly outperformed the traditional clinical approach based on LACE index. Moreover, our proposed weighted samples one class SVM model can reach high accuracy (0.9635) for predicting the deterioration happening in the future using data collected by a sliding window, which indicates the potential for allowing timely intervention.

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

“…This includes data from novel sources such as social network platforms, commercial transaction records, smartphones and wearable devices, sensors, and Internet‐connected devices, both in the home and the public realm. Research has demonstrated that these data can be used to inform improvements in population health, for example, by characterising disease outbreaks in near real time, identifying adverse effects of medications, and predicting clinical deterioration after hospital discharge . The United Kingdom (and England in particular) also falls behind other countries in linkage of cross‐sectoral data to inform analyses of health (eg, from employment and criminal justice systems).…”

“…Research has demonstrated that these data can be used to inform improvements in population health, for example, by characterising disease outbreaks in near real time, 9 identifying adverse effects of medications, 10 and predicting clinical deterioration after hospital discharge. 11 The United Kingdom (and England in particular) also falls behind other countries in linkage of cross-sectoral data to inform analyses of health (eg, from employment and criminal justice systems).…”

Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study

“…To construct machine learning models based on activity metrics data, we applied feature engineering techniques to extract three types of features: statistical, semantic, and biobehavioral rhythmic features. We extracted first- and second-order statistical features from the daily step count, heart rate, and sleep time-series data [ 17 ]. The first-order statistical features used in our analysis were mean, maximum, minimum, skewness, and kurtosis.…”

“…We then performed detrended fluctuation analysis (DFA) on the data, which evaluates long-range correlation of noisy time-series data, and used the root-mean-square deviation from the trend, namely the fluctuation, from DFA as the feature in our analysis. [ 17 ]. The semantic features collected provided summaries of the patient’s daily activity level and sleep quality.…”

“…There were three possible causes of missing data: (1) improper wearing of the device, (2) lack of user compliance (not wearing the device), and (3) loss of connectivity for longer than 7 days. For patients with missing data, we applied a two-level imputation method to the activity metrics collected by our telemonitoring devices [ 17 ]. The data-level imputation was to fill the missing data points in heart rate time series if the daily data yield, defined as the fraction of the expected data points that were successfully collected, was equal to or above the threshold (10%).…”