{
  "id": 8980697,
  "title": "Machine learning model distinguishes levels of psychological resilience in health care workers with 75% accuracy",
  "url": "https://urgent.news/2026/09/21/machine-learning-model-distinguishes-levels-of-psychological",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T18:40:05.000Z",
  "source": {
    "name": "Medical Xpress",
    "slug": "medical-xpress",
    "url": "https://medicalxpress.com/news/2026-09-machine-distinguishes-psychological-resilience-health.html"
  },
  "original_language": "en",
  "account": "A study published in Discover Artificial Intelligence has found that machine learning models can accurately predict levels of psychosocial resilience in healthcare workers, with 75.6% accuracy. Researchers from the University of Chicago trained logistic regression, random forest, and support vector machine models using data from the How Right Now Mental Health & Coping dataset. The models classified workers into high and low resilience categories based on four psychometric variables—resilience, ability to bounce back, control, and confidence. Stress, depression, and anxiety emerged as the strongest predictors of low resilience, while coping strategies such as seeking social support, engaging in hobbies, prayer, and meditation were associated with higher resilience. However, the data represent a single point in time, are based on self-reported responses from a U.S. population, and may not be generalizable to other healthcare systems. The authors suggest validating the model with Latin American healthcare workers and incorporating additional measures such as sleep data or physiological indicators. They also propose that such interpretable machine learning models could be used by occupational health departments to identify at-risk individuals at an early stage, without relying on complex artificial intelligence systems.",
  "summary": "A study published in Discover Artificial Intelligence used logistic regression, random forest and support vector machine (SVM) to classify health care workers according to their level of psychosocial resilience, using information collected during the COVID-19 pandemic. The logistic regression model produced the most accurate results, with an accuracy of 75.6% and an area under the ROC curve of…",
  "key_points": [
    "Machine learning models achieve 75.6% accuracy in predicting healthcare workers' resilience levels",
    "Researchers from University of Chicago used data from How Right Now Mental Health & Coping dataset"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}