Challenges and Opportunities in Clinical Prediction Models

doi:10.3969/j.issn.1673-5765.2024.05.001

Abstract

Abstract: Clinical prediction models can predict individual outcomes or risks based on effect evidence obtained from the population, serving as a powerful tool linking evidence-based medicine and precision medicine. With the continuous advancement of data measurement, storage, interoperability, and analytical techniques, the application prospect for clinical prediction models is becoming increasingly clear. However, it also faces many problems and challenges. This paper aims to provide an overview of the fundamental concepts, application scenarios, current dilemmas, and development opportunities of clinical prediction models.

Key words: Clinical prediction model; Big data; Machine learning

摘要： 临床预测模型可基于从群体获得的效应证据去预测个体的结局或风险，是链接循证医学和精准医学的强有力工具。随着数据测量、存储、互联互通及分析技术的不断进步，临床预测模型的应用前景将越来越明朗，但与此同时，也面临着诸多问题和挑战。本文对临床预测模型的基础概念、应用场景以及现在面临的困境和发展机遇做一个梳理。

关键词: 临床预测模型; 大数据; 机器学习

CLC Number:

GU Hongqiu. Challenges and Opportunities in Clinical Prediction Models[J]. Chinese Journal of Stroke, 2024, 19(5): 481-487.

谷鸿秋. 临床预测模型的困境与机遇[J]. 中国卒中杂志, 2024, 19(5): 481-487.

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