An artificial intelligence malnutrition screening tool based on electronic medical records

Nutrition screening is a fundamental step to ensure appropriate intervention in patients with malnutrition. An automatic tool of nutritional risk screening based on electronic health records will improve efficiency and elevate the malnutrition diagnosis rate. We aimed to develop an artificial intelligence (AI) malnutrition screening tool based on electronic medical records and compare it with the patient interview-based tool.

We conducted a cross-sectional study at a comprehensive tertiary hospital in China. Data of malnutrition information were extracted from electronic health records (EHR) and were used to train and test an AI tool for the malnutritional risk screening. We adopted the GLIM framework as a reference standard for assessing malnutrition. Six widely used machine learning algorithms for auxiliary diagnosis prediction, including Support Vector Machine, Random Forest, extreme gradient boosting (XGBoost), Logistic Regression, AdaBoost, and Gradient Boosting were compared and visualized using SHapley Additive exPlanations (SHAP). After feature screening, simplified algoritms were cross validated at an independent data set.

495 inpatients enrolled were randomly divided into training and validation groups for algorithm development. 10 features annotated manually from free texts and 32 features selected from structured EHRs entered the models. XGBoost had the highest area under the receiver operating characteristic curve (AUC) and the top six features were weight loss, decreased food intake, prealbmine, white cell, BMI group, and percent of neutrophils. In simplified models, Random Forest acquired the highest AUC of 0.97 based on first sources data from interviews and 0.87 based on EHR data.

Inpatients' EHR data could be integrated by AI to detect the risk of malnutrition. This AI-enabled tool may hold promise for timely and efficient nutrition screening in newly admitted inpatients.