A Review of Challenges and Opportunities in Machine Learning for Health.

By Marzyeh Ghassemi, Tristan Naumann, Peter F. Schulam, Andrew Beam, Irene Y. Chen, Rajesh Ranganath. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018

398 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/5ba3f64789fb54bee10a891bc21222964d83c687

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Citations in the corpus

Federated Learning: Opportunities and Challenges (2021, background): “Electronic Health Records (EHR) is considered as the main source of healthcare data formachine learning applications [11].”
Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, background): “[55] - - + - - + [7] - - + + - [28] - - + + - [18] - - - - [60] - - - - + [46] - - - - [84] - - + + - + [104] - - + + - [49] - - - - + + [82] + - + - - + [56] - - + - - [76] - + + + - + + [77] - + + - + + [78] + - + - - + + [42] - - + + + - + [44] - - - - [29] - - + - + + [79] - - + - - + [27] - - -”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “cannot be exploited through classical learning algorithms and to answer them we need to analyze the data from the lens of causality [115].”
Ethical Machine Learning in Healthcare (2020, background): “ harm. In medicine, excitement about human-level performance (1) of ML for health is balanced against ethical concerns, such as the potential for these tools to exacerbate existing health disparities (2, 3, 4, 5). For instance, recent work has demonstrated that state-of-the-art clinical prediction m”