Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms

By Berk Ustun, M. Westover, Cynthia Rudin, M. Bianchi. 2016

99 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Physiology, Pulmonary and Respiratory Medicine, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. Cynthia Rudin
2. Tong Wang
3. Berk Ustun

In Physiology

1. Cynthia Rudin
2. Tong Wang
3. Berk Ustun

In Pulmonary and Respiratory Medicine

1. Cynthia Rudin
2. Tong Wang
3. Berk Ustun

In Medical & Health AI

1. Cynthia Rudin
2. Tong Wang
3. Berk Ustun
4. Lesia Semenova
5. D. Parkes
6. Yang Liu
7. Chudi Zhong
8. Zhi Chen

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Table 2: A scoring system for sleep apnea screening (Ustun et al., 2016).”
• Learning Optimized Risk Scores (2016, background): “RiskSLIM risk scores are the risk assessment counterpart to SLIM scoring systems (Ustun et al., 2013; Ustun and Rudin, 2016; Zeng et al., 2016; Ustun et al., 2016).”
• Learning Optimized Or's of And's (2015, methodology): “We analyzed polysomnography and self-reported clinical information from 1922 patients tested in the clinical sleep laboratory of Massachusetts General Hospital, first analyzed in 2015 (Ustun and Rudin, 2015; Ustun et al., 2015).”
• Optimized Risk Scores (2017, methodology): “RiskSLIM models are similar to SLIM models [28, 32, 34, 36] in that the score functions are fully optimized for feature selection, small integer coecients and operational constraints.”
• A Review on Fairness in Machine Learning (2022, background): “3) Decoupled Classifiers with Preference Guarantees • ProPublica • Adult • Apnea [200] • Cancer [196] Recursive feature selection procedure for building decoupled classifiers.”
• Fairness without Harm: Decoupled Classifiers with Preference Guarantees (2019, background): “, 2016); apnea, a dataset to diagnose obstructive sleep apnea (Ustun et al., 2016); and cancer, a dataset to diagnose lung cancer (National Lung Screening Trial Research Team, 2011).”
• Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice (2018, background): “The data provided for this study were records from all patients at the Massachusetts General Hospital Sleep Lab above 18 years old that underwent an definitive test for OSA called polysomnography (1,922 patients) between 2009 and 2013.”