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Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

For any rule l: q1 AND q2 AND … AND qn→w, the percentage of data instances satisfying q1, q2, ..., and qn and linking to w is termed l’s support showing l’s coverage. Among all data instances satisfying q1, q2, ..., and qn, the percentage of data instances linking to w is termed l’s confidence reflecting l’s precision. Our original automatic explanation method uses rules with support ≥smin and confidence ≥cmin.

Gang Luo, Michael D Johnson, Flory L Nkoy, Shan He, Bryan L Stone

JMIR Med Inform 2020;8(12):e21965

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Article has an altmetric score of 16

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods

The likelihood, L, that an ED can successfully use machine learning for this scenario is equal to p1× p2. p1 is the probability that a health care researcher in the ED can build a high-quality machine learning model for this scenario using Auto-ML. p2 is the probability that the ED can successfully deploy the model if it can be built.

Gang Luo, Bryan L Stone, Michael D Johnson, Peter Tarczy-Hornoch, Adam B Wilcox, Sean D Mooney, Xiaoming Sheng, Peter J Haug, Flory L Nkoy

JMIR Res Protoc 2017;6(8):e175

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Article has an altmetric score of 20