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Updated: Jun 11, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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在AI模型中评估和减少子组差异:对儿科COVID-19测试结果的分析

Alexander Libin1, Jonah T Treitler2, Tadas Vasaitis3

  • 1AIM AHEAD Consortium, Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS), Medstar Research Health Institute, Georgetown University, Washington, D.C., USA.

medRxiv : the preprint server for health sciences
|October 7, 2024
PubMed
概括

人工智能 (AI) 医疗保健模型中的子组差异很常见,影响了公平性. 合成数据显示,有可能减少这些差异,改善儿童COVID-19测试中的AI模型公平性.

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack07:31

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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