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

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.

Application of machine learning constructs to predict post-operative complications and adverse events following shoulder hemiarthroplasty

“…Utilizing objective clinical data that is readily accessible before or upon admission, ML algorithms can provide valuable insights to better characterize preoperative risk. [17][18][19][20][21] For instance, our analysis consistently identified preoperative hematocrit as a significant variable, which aligns with previous studies demonstrating the importance of addressing low hematocrit levels in patients undergoing reverse TSA and HA following a proximal humerus fracture to mitigate the risk of 30-day mortality. 15 This approach utilizes both subjective variables that are present in current preoperative risk classifications and objective variables such as comorbidities, functional status, and overall health, enhancing the accuracy and comprehensiveness of risk assessment.…”