Detection of atypical response trajectories in biomedical longitudinal databases.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-10-24 eCollection Date: 2023-11-01 DOI:10.1515/ijb-2020-0076

Lucio José Pantazis, Rafael Antonio García

引用次数: 0

Abstract

Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a tool that allows to search conceptually different types of anomalies in the data simultaneously.

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生物医学纵向数据库中非典型反应轨迹的检测。

许多卫生保健专业人员和机构管理纵向数据库，包括对不同患者的长期随访。纵向数据经常表现出额外的复杂性，如高可变性、相关测量和缺失数据。混合效应模型被广泛用于克服这些困难。这项工作提出使用线性混合效应模型作为工具，允许同时搜索数据中概念上不同类型的异常。

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.