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.
View Article and Find Full Text PDFObjective: Safe, tolerable, effective approaches to weight restoration are needed for adults with anorexia nervosa (AN). We examined weight outcomes and patient satisfaction with an integrated, inpatient-partial hospitalization, meal-based behavioral program that rapidly weight restores a majority of patients.
Method: Consecutively discharged inpatients (N = 149) treated on weight gain protocol completed an anonymous questionnaire assessing treatment satisfaction at inpatient discharge.
Objective: Information on nutritional rehabilitation for underweight patients with avoidant/restrictive food intake disorder (ARFID) is scarce. This study characterized hospitalized youth with ARFID treated in an inpatient (IP)-partial hospitalization behavioral eating disorders (EDs) program employing an exclusively meal-based rapid refeeding protocol and compared weight restoration outcomes to those of patients with anorexia nervosa (AN).
Method: Data from retrospective chart review of consecutive underweight admissions (N = 275; age 11-26 years) with ARFID (n = 27) were compared to those with AN (n = 248) on clinical features, reason for discharge, and weight restoration variables.