Deep Learning for Improved Risk Prediction in Surgical Outcomes.

Scientific Reports
Ali JalaliLuis M Ahumada

Abstract

The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients' families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results ...Continue Reading

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Citations

Feb 10, 2021·BMC Medical Informatics and Decision Making·Chenxi SunZhenjie Wang
Apr 29, 2021·World Journal for Pediatric & Congenital Heart Surgery·Dimitris BertsimasGeorge E Sarris
Jul 25, 2021·Archives of Disease in Childhood·Sarah Ln ClarkeAthimalaipet V Ramanan
Nov 4, 2021·BMC Medical Informatics and Decision Making·Chenxi SunHongyan Li
Dec 4, 2021·Current Opinion in Cardiology·Jef Van den EyndeCedric Manlhiot

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Software Mentioned

DecisionTreeClassifier
Python scikit - learn
Keras
GB
Tableau
LogisticRegression
SAS

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