2022
DOI: 10.1161/jaha.122.026987
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Machine Learning Approach to Predict In‐Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States

Abstract: Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in‐hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were ob… Show more

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Cited by 9 publications
(7 citation statements)
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“…While previous studies have yielded inconclusive findings regarding the advantage of ML over traditional statistics in performing different clinical tasks ( 34 , 35 , 59 , 60 ), it is generally believed that complex ML models often require big data to achieve optimal performance ( 61 ). This study involved a substantial dataset of more than 4,000 subjects with a balanced class distribution, where all three ML models showed significant improvements over the standard logistic regression, albeit to a modest extent.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…While previous studies have yielded inconclusive findings regarding the advantage of ML over traditional statistics in performing different clinical tasks ( 34 , 35 , 59 , 60 ), it is generally believed that complex ML models often require big data to achieve optimal performance ( 61 ). This study involved a substantial dataset of more than 4,000 subjects with a balanced class distribution, where all three ML models showed significant improvements over the standard logistic regression, albeit to a modest extent.…”
Section: Discussionmentioning
confidence: 99%
“…The LightGBM has a similar structure to the XGBoost but uses a different strategy to split the data. These ML models represent the state-of-the-art ML techniques that show remarkable outcomes in a variety of tasks (32)(33)(34)(35). Logistic regression served as a baseline model to allow unbiased performance assessment for these ML models.…”
Section: Model Developmentmentioning
confidence: 99%
“…Meanwhile, there is another concern that a small number of the events of interest may limit the potentials of ML models in discovering the underlying patterns from these rare “positive” cases. This situation is in fact common in many diseases with low incidence or prevalence rates [ 38 , 43 ]. For example, the positive RLN nodes in this study only accounts for less than 20% on each side, which could result in a model biased towards a negative prediction.…”
Section: Discussionmentioning
confidence: 99%
“…Following a similar approach as previously conducted studies, we randomly divided our cohort into training (80%) and testing samples (20%). 30 , 31 Baseline variables between the 2 samples were summarized as count with percentage and were compared using χ 2 or Fisher exact test for categorical variables; continuous variables were summarized as mean with SD and were compared using Student t ‐test. The standardized difference was calculated to assess the effect size between the 2 samples, with a value <0.10 considered negligible.…”
Section: Methodsmentioning
confidence: 99%