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Updated: May 23, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

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开发一种机器学习预测模型,用于在低资源环境中使用常规电子医疗记录在艾滋病毒护理中对随访损失的预测

Tamrat Endebu1, Girma Taye2, Wakgari Deressa2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia. seetame2008@gmail.com.

BMC medical informatics and decision making
|May 19, 2025
PubMed
概括

机器学习可以准确地预测艾滋病毒护理中的随访损失 (LTFU),识别关键的风险因素,如遵守和服务交付模型. 这种工具有助于在资源较低的环境中进行主动干预.

关键词:
艾滋病病毒 艾滋病病毒 艾滋病病毒对后续行动的损失低资源设置 低资源设置机器学习 机器学习模型开发 模型开发预测工具是一个预测工具.

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