Sensors, Vol. 24, Pages 2640: Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals

Sensors, Vol. 24, Pages 2640: Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals Sensors doi: 10.3390/s24082640 Authors: Jason Nan Matthew S. Herbert Suzanna Purpura Andrea N. Henneken Dhakshin Ramanathan Jyoti Mishra Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research