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Updated: May 2, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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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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通过机器学习模型使用临床和实验室数据预测怀孕早期的孕前症

Songchang Chen1, Jia Li2,3, Xiao Zhang2,3

  • 1Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, 200011, China.

BMC medical informatics and decision making
|May 1, 2025
PubMed
概括

这项研究确定了用于预测产前 (PE) 的关键实验室标志物,并使用临床和实验室数据开发了早期发病的PE (EOPE) 和晚发病的PE (LOPE) 的准确预测模型.

关键词:
实验室数据 实验室数据机器学习模型机器学习模型孕前症 孕前症

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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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