Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatme...

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Veröffentlicht in:	PloS one 2022-11, Vol.17 (11), p.e0277507
Hauptverfasser:	Cox, Meredith, Panagides, J C, Tabari, Azadeh, Kalva, Sanjeeva, Kalpathy-Cramer, Jayashree, Daye, Dania
Format:	Artikel
Sprache:	eng
Schlagworte:	Aged Biology and Life Sciences Cardiovascular system Care and treatment Computer and Information Sciences Death Decision-making Diabetes Diagnosis Disease prevention Female Health aspects Health risks Hospital care Humans Ischemia Laboratories Learning algorithms Machine Learning Male Medicine and Health Sciences Methods Modelling Mortality Mortality risk Multilayer perceptrons Patient Readmission Patients Peripheral Arterial Disease - surgery Peripheral vascular diseases Prevention Quality control Quality improvement Race Risk Risk Assessment Risk Factors Sex Statistical methods Subgroups Support vector machines Variables Vascular diseases
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