Machine learning in clinical and epidemiological research: isn't it time for biostatisticians to work on it?

Q3 Nursing

Epidemiology Biostatistics and Public Health Pub Date : 2022-01-27 DOI:10.2427/13245

D. Azzolina, I. Baldi, G. Barbati, P. Berchialla, D. Bottigliengo, Andrea Bucci, S. Calza, P. Dolce, V. Edefonti, Andrea Faragalli, G. Fiorito, I. Gandin, F. Giudici, D. Gregori, C. Gregorio, F. Ieva, C. Lanera, G. Lorenzoni, M. Marchioni, A. Milanese, A. Ricotti, V. Sciannameo, G. Solinas, M. Vezzoli

引用次数: 12

Abstract

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques.

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临床和流行病学研究中的机器学习:生物统计学家不是时候研究它了吗?

近年来，医学统计学和机器学习(ML)技术之间出现了广泛的交叉应用。

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

Epidemiology Biostatistics and Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

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期刊介绍： Epidemiology, Biostatistics, and Public Health (EBPH) is a multidisciplinary journal that has two broad aims: -To support the international public health community with publications on health service research, health care management, health policy, and health economics. -To strengthen the evidences on effective preventive interventions. -To advance public health methods, including biostatistics and epidemiology. EBPH welcomes submissions on all public health issues (including topics like eHealth, big data, personalized prevention, epidemiology and risk factors of chronic and infectious diseases); on basic and applied research in epidemiology; and in biostatistics methodology. Primary studies, systematic reviews, and meta-analyses are all welcome, as are research protocols for observational and experimental studies. EBPH aims to be a cross-discipline, international forum for scientific integration and evidence-based policymaking, combining the methodological aspects of epidemiology, biostatistics, and public health research with their practical applications.