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

Dingwen Li, Jay Vaidya, Michael Wang, Ben Bush, Chenyang Lu, Marin Kollef, Thomas Bailey

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


In the article, we explore the feasibility of monitoring outpatients using Fitbit Charge HR wristbands and the potential of machine learning models to predict clinical deterioration (readmissions and death) among outpatients discharged from the hospital. We developed and piloted a data collection system in a clinical study that involved 25 heart failure patients recently discharged. The results 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 fivefold cross-validation, K nearest neighbor achieved the highest accuracy of 0.8667 for identifying patients at risk of deterioration using the data collected 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 with estimated confidence can reach high accuracy (0.9635) for predicting the deterioration using data collected within a sliding window, which indicates the potential for allowing timely intervention.

Original languageEnglish
Article number3344256
JournalACM Transactions on Computing for Healthcare
Issue number1
StatePublished - Mar 2 2020


  • Medical data mining
  • deterioration early warning
  • heart failure
  • ubiquitous computing
  • wearable tracker


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