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Online Decision Support Tool that Explains Temporal Prediction of Activities of Daily Living (ADL)

This paper presents an online decision support tool that can be used to assess and predict functional abilities in terms of nine Activities of Daily Living (ADLs) up to one year ahead. The tool is based on previously developed Computational Barthel Index (CBIT) and has been rebuilt using Gradient Boost (GB) models with average Area under ROC (AUC) of 0.79 (0.77-0.80), accuracy of 0.74 (0.70-0.79), recall of 0.78 (0.58-0.93), and precision of 0.75 (0.67-0.82) when evaluating ADLs for new patients. When re-evaluating patients, the models achieved AUC 0.95 (0.94-0.96), accuracy of 0.91 (0.90-0.92), recall of 0.91 (0.86-0.95), and precision of 0.92 (0.88-0.94). The decision support tool has been equipped with a prediction explanation module that calculates and visualizes influence of patient characteristics on the predicted values. The explanation approach focuses on patient characteristics present in the data, rather than all attributes used to construct models. The tool has been implemented in Python programming language using Flask Web framework and is accessible through a website or an Application Programming Interface (API).