Stress prediction using micro-EMA and machine learning during COVID-19 social isolation
| Autor: | Enhao Zheng, Huining Li, Nicole Roma, Chenhan Xu, Zijian Zhong, Tania Von Visger, Steven Lamkin, Yu-Ping Chang, Wenyao Xu |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Elastic net regularization
Coronavirus disease 2019 (COVID-19) Computer science Medicine (miscellaneous) Health Informatics Machine learning computer.software_genre Article Health Information Management Prediction model Stress (linguistics) Range (statistics) medicine Social isolation Set (psychology) Micro-EMA business.industry Perceived stress Computer Science Applications Scale (social sciences) Ensemble prediction Artificial intelligence medicine.symptom business computer Information Systems |
| Zdroj: | Smart Health (Amsterdam, Netherlands) |
| ISSN: | 2352-6483 |
| Popis: | Accurately predicting users’ perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1-2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day’s PSS scores, and higher than 81% accuracy for predicting the next 7 day’s stress labels. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect