A Review of Challenges and Opportunities in Machine Learning for Health.
A Review of Challenges and Opportunities in Machine Learning for Health.
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发表时间：
2018-06
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通讯作者：
M. Ghassemi;Tristan Naumann;Peter F. Schulam;A. Beam;I. Chen;R. Ranganath
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作者：
M. Ghassemi;Tristan Naumann;Peter F. Schulam;A. Beam;I. Chen;R. Ranganath
现代电子健康记录 (EHR) 提供数据来回答有临床意义的问题。电子病历中不断增长的数据使得医疗保健领域使用机器学习的时机已经成熟。然而，临床环境中的学习带来了独特的挑战，使常见机器学习方法的使用变得复杂。例如，电子病历中的疾病标记不清，病情可能包含多种潜在的内型，健康个体的代表性不足。本文作为入门读物来阐明这些挑战，并强调机器学习社区成员为医疗保健做出贡献的机会。
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.