Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data

2022-12-20 · Dianbo Liu, Karmel W. Choi, Paulo Lizano, William Yuan, Kun-Hsing Yu, Jordan W. Smoller, Isaac Kohane

Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare insurance claims and 2) electronic health records (EHRs). Design, setting and participants: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources were analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. Main outcomes and measures: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR.

📄 PDF Abstract BibTeX arXiv:2212.10320

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Large Language Models (LLMs) have shown promise in natural language processing tasks, with the potential to automate systematic reviews. This study evaluates the performance of three state-of-the-art LLMs in conducting s…

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2026-01-12 · Haotian Gao, Xiangying Zhang, Jingyuan Li, Xinchong Chen 외 arxiv

Drug discovery represents a time-consuming and financially intensive process, and virtual screening can accelerate it. Scoring functions, as one of the tools guiding virtual screening, have their precision closely tied t…

Protein-Ligand Affinity Prediction Graph Neural Network Drug Discovery