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Updated: Sep 11, 2025

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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一种多算法机器学习模型,用于预测早产儿预产患者的早产风险

Yanhong Xu1, Yizheng Zu2, Ying Zhang1

  • 1College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University Fujian Maternity and Child Health Hospital, Fuzhou, Fujian, People's Republic of China.

International journal of general medicine
|August 11, 2025
PubMed
概括

机器学习准确地预测了早产子 (EOPE) 的早产儿. 一个组合模型确定了胎儿生长限制,血清囊素C和C反应蛋白作为关键风险因素,有助于早期干预.

关键词:
临床预测模型的临床预测模型.早期发病的妊娠前.机器学习是机器学习.过早分娩 过早分娩是什么

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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