Frail People in LABLand: Development of an Easy-to-Use Machine Learning Model to Identify Frail People in Hospitals Based on Laboratory Data.

BACKGROUND: Frail individuals are very vulnerable to stressors, which often lead to adverse outcomes. To ensure an adequate therapy, a holistic diagnostic approach is needed which is provided in geriatric wards. It is important to identify frail individuals outside the geriatric ward as well to ensure that they also benefit from the holistic approach. OBJECTIVES: The goal of this study was to develop a machine learning model to identify frail individuals in hospitals. The model should be applicable without additional effort, quickly and in many different places in the healthcare system. METHODS: We used Gradient Boosting Decision Trees (GBDT) to predict a frailty target derived from a gold standard assessment. The used features were laboratory values, age and sex. We also identified the most important features. RESULTS: The best GBDT achieved an AUROC of 0.696. The most important laboratory values are urea, creatinine, granulocytes, chloride and calcium. CONCLUSION: The model performance is acceptable, but insufficient for clinical use. Additional laboratory values or the laboratory history could improve the performance.

SAINTENS: Self-Attention and Intersample Attention Transformer for Digital Biomarker Development Using Tabular Healthcare Real World Data.

BACKGROUND: Deep learning currently struggles with tabular data, but it can benefit from multimodal learning. SAINT is a deep learning model for tabular data on which we base our presented developments. OBJECTIVES: In this study, we introduce SAINTENS as a new deep learning method, specifically for the in healthcare predominant tabular real world data. METHODS: For this purpose, we compare SAINTENS with SAINT and the State of the Art Machine Learning methods for tabular data. We use tabular data from geriatrics to predict four different targets (dysphagia, pressure ulcers, decompensated heart failure and delirium). We determine the relevant feature sets and train the models on these sets. RESULTS: Both SAINTENS and SAINT models are at least on the same performance level as the current State of the Art (Gradient Boosting Decision Trees). CONCLUSION: In combination with multimodal learning SAINTENS and SAINT may be used on real world data comprising tabular, text and image data, for discovery and development of new digital biomarkers.