Opportunities in Machine Learning for Healthcare

By Marzyeh Ghassemi, Tristan Naumann, Peter F. Schulam, Andrew Beam, Rajesh Ranganath. 2018

112 citations recorded by the source corpus.

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “Real-world examples of confounding, such as Examples 1 and 2, have lead to increasing calls for causal knowledge to be properly incorporated into decision support algorithms in healthcare 54 .”
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks (2019, background): “Researchers have predicted mortality, billing codes, length-of-stay prediction, intervention onset and offset prediction, among other targets (Ghassemi et al., 2018; Suresh et al., 2017; Raghu et al., 2017; Choi et al., 2017; Ghassemi et al., 2017; Che et al., 2018).”
Hurtful words: quantifying biases in clinical contextual word embeddings (2020, background): “In clinical applications, machine learning has the potential to improve patient outcomes, cut costs, and reduce physician burnout [22, 58, 63].”
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 [103].”
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, methodology): “ture extraction by automatically extracting features from preprocessed data, the limitation is that these methods require an abundant amount of data for effective prediction [90].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “, the decision of the model will change from A to B if the doctor replaces treatment C with D [26].”