How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature.

By Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, Lily H. Peng. Journal of the American Medical Association (JAMA), 2019

474 citations recorded by the source corpus.

In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and establishing the clinical effectiveness of the tool. Machine learning-based tools should also be assessed for the type of machine learning model used and its appropriateness for the input data type and data set size. Machine learning models also generally have additional prespecified settings called hyperparameters, which must be tuned on a data set independent of the validation set. On the validation set, the outcome against which the model is evaluated is termed the reference standard. The rigor of the reference standard must be assessed, such as against a universally accepted gold standard or expert grading.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/97f4a6f87f258053f2677504647696f1803c6794

How researchers see this paper

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In Artificial Intelligence

1. Sarah Graham
2. Ho-Cheol Kim
3. M. de Choudhury
4. John B. Torous
5. Ellen E. Lee
6. Colin A. Depp
7. Dilip Jeste

Citations in the corpus

• Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “(27) provided a guide on how to interpret ML-based publications and assess the appropriateness of the ML model for the size and type of input data.”