Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case

TL;DR

This paper introduces a method for extracting actionable insights from black-box machine learning models, demonstrated through predicting and preventing agitation episodes in dementia patients by analyzing environmental factors.

Contribution

It presents a novel approach to interpret black-box models for sequential data, enabling actionable decision-making in healthcare scenarios.

Findings

Actionable items like light level changes can trigger agitation episodes.

The method effectively identifies environmental factors influencing dementia agitation.

Implementation helps caregivers intervene proactively.

Abstract

Machine learning has shown successes for complex learning problems in which data/parameters can be multidimensional and too complex for a first-principles based analysis. Some applications that utilize machine learning require human interpretability, not just to understand a particular result (classification, detection, etc.) but also for humans to take action based on that result. Black-box machine learning model interpretation has been studied, but recent work has focused on validation and improving model performance. In this work, an actionable interpretation of black-box machine learning models is presented. The proposed technique focuses on the extraction of actionable measures to help users make a decision or take an action. Actionable interpretation can be implemented in most traditional black-box machine learning models. It uses the already trained model, used training data, and data processing techniques to extract actionable items from the model outcome and its time-series inputs. An implementation of the actionable interpretation is shown with a use case: dementia-related agitation prediction and the ambient environment. It is shown that actionable items can be extracted, such as the decreasing of in-home light level, which is triggering an agitation episode. This use case of actionable interpretation can help dementia caregivers take action to intervene and prevent agitation.