Integration of feature vectors from raw laboratory, medication and procedure names improves the precision and recall of models to predict postoperative mortality and acute kidney injury.

Link: https://doi.org/10.1038/s41598-022-13879-7
Authors: Hofer, Ira S; Kupina, Marina; Laddaran, Lori; Halperin, Eran

Abstract: Manuscripts that have successfully used machine learning (ML) to predict a variety of perioperative outcomes often use only a limited number of features selected by a clinician. We hypothesized that techniques leveraging a broad set of features for patient laboratory results, medications, and the surgical procedure name would improve performance as compared to a more limited set of features chosen by clinicians. Feature vectors for laboratory results included 702 features total derived from 39 laboratory tests, medications consisted of a binary flag for 126 commonly used medications, procedure name used the Word2Vec package for create a vector of length 100. Nine models were trained: baseline features, one for each of the three types of data Baseline + Each data type, (all features, and then all features with feature reduction algorithm. Across both outcomes the models that contained all features (model 8) (Mortality ROC-AUC 94.32 ± 1.01, PR-AUC 36.80 ± 5.10 AKI ROC-AUC 92.45 ± 0.64, PR-AUC 76.22 ± 1.95) was superior to models with only subsets of features. Featurization techniques leveraging a broad away of clinical data can improve performance of perioperative prediction models.