Research Data from University of Pennsylvania Update Understanding of Computer Science (The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density): Computers – Computer Science

2026 FEB 11 (NewsRx) -- By a News Reporter-Staff News Editor at CDC & FDA Daily -- Data detailed on computer science have been presented. According to news reporting out of Philadelphia, Pennsylvania, by NewsRx editors, research stated, “Labor market forecasting relies heavily on economic time-series data, often overlooking the ‘health-wealth’ gradient that links population health to workforce participation.”

Our news reporters obtained a quote from the research from University of Pennsylvania: “This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. We constructed a multi-source longitudinal dataset (2014-2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state level. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability. The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test R 2 = 0.800).”

According to the news editors, the research concluded: “A leakage-safe stacked Ridge ensemble yielded comparable performance (Test R 2 = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.”

For more information on this research see: The Health-Wealth Gradient in Labor Markets: Integrating Health, Insurance, and Social Metrics to Predict Employment Density. Computation, 2026,14(1):22. (Computation - http://www.mdpi.com/journal/computation). The publisher for Computation is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/computation14010022.

Our news journalists report that more information may be obtained by contacting Dingyuan Liu, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States. Additional authors for this research include Qiannan Shen, Jiaci Liu.

(Our reports deliver fact-based news of research and discoveries from around the world.)