Methods for identifying health status from routinely collected health data: An overview

IF 2.8 4区 医学 Q2 INTEGRATIVE & COMPLEMENTARY MEDICINE
Mei Liu , Ke Deng , Mingqi Wang , Qiao He , Jiayue Xu , Guowei Li , Kang Zou , Xin Sun , Wen Wang
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引用次数: 0

Abstract

Routinely collected health data (RCD) are currently accelerating publications that evaluate the effectiveness and safety of medicines and medical devices. One of the fundamental steps in using these data is developing algorithms to identify health status that can be used for observational studies. However, the process and methodologies for identifying health status from RCD remain insufficiently understood. While most current methods rely on International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods hold promise for more accurately identifying the health status, they remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to boost the credibility of findings from observational studies that use RCD.
从常规收集的健康数据中确定健康状况的方法:概述。
常规收集的健康数据(RCD)目前正在加速评估药品和医疗器械有效性和安全性的出版物。使用这些数据的一个基本步骤是开发算法,以确定可用于观察性研究的健康状况。然而,从RCD中确定健康状况的过程和方法仍然不够了解。虽然目前大多数方法依赖于国际疾病分类(ICD)代码,但它们可能并不普遍适用。尽管机器学习方法有望更准确地识别健康状态,但它们在RCD研究中仍未得到充分利用。为了解决这些重要的方法学差距,我们概述了使用RCD在观察性研究中识别健康状况的关键步骤和方法学考虑。这篇综述有可能提高使用RCD的观察性研究结果的可信度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Integrative Medicine Research
Integrative Medicine Research Medicine-Complementary and Alternative Medicine
CiteScore
6.50
自引率
2.90%
发文量
65
审稿时长
12 weeks
期刊介绍: Integrative Medicine Research (IMR) is a quarterly, peer-reviewed journal focused on scientific research for integrative medicine including traditional medicine (emphasis on acupuncture and herbal medicine), complementary and alternative medicine, and systems medicine. The journal includes papers on basic research, clinical research, methodology, theory, computational analysis and modelling, topical reviews, medical history, education and policy based on physiology, pathology, diagnosis and the systems approach in the field of integrative medicine.
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