Explainable Hybrid Machine Learning Technique for Healthcare Service Utilization

Abstract: In the era of data, predictive and prescriptive analytics in healthcare is enabled by machine learning (ML) algorithms. The varied healthcare entities pose challenges in the inclusion of ML predictive models in the rule-based claims processing system. The hybrid ML algorithm proposed in this research article is for handling huge volumes of data in predicting a member’s utilization of Medicaid home healthcare service. The member’s demographic features, health details and enrolment details are generally considered for building the utilization model though health details may not be available for new members. It is also a widely accepted fact that health outcomes are driven also by social and environmental features, furthermore, the analysis of Medicaid home healthcare service data proved the same. Hence various social temporal features such as their living place, federal poverty level, etc., are also considered for predicting the member expenditure in Medicaid home healthcare services. The home healthcare service utilization model predicts the member expenditure in home healthcare services in the subsequent years with the ability to peer group members in a home healthcare service considering the member demographics, their morbidities, and the socio-temporal features. In the home healthcare services utilization prediction modelling, the methodology of segmenting the input features using a clustering algorithm provides labels for the classification algorithm contributing to the accuracy of predicting the member expenditure. The clique clustering algorithm used in the model training phase discovers the similarities in member characteristics and provides labels for the extreme gradient boosting tree classifier. The best curve fitting function for each class is chosen either as linear or logarithmic or exponential or Gaussian in the training phase. The proposed hybridization of Clique clustering, Extreme Gradient Boost Tree classification, and Curve fitting algorithms address the lean availability of member information during enrolment for determining the member utilization of home healthcare services by their expenditure. The proposed hybrid home healthcare service utilization model, ‘Auto-labelled Boosted Regressor’ (ALBR), achieved an AUROC of 0.98 and an AUPRC of 0.90. The complexity of the hybrid ML model, ALBR, necessitated that the decisions and actions of the ML model is explainable to healthcare decision/policy makers. Post-hoc explainable ML methods approximate the behaviour of the complex ML technique using ALBR model as a black box by extracting relationships between sub-spaces of the feature values and the predictions. The home healthcare services utilization model is explained across frequent as well as the infrequent sub-spaces of the feature values using the extrapolated Medicaid home healthcare services data. The comparison of the relationship of the sub-spaces in the categorical features such as living facility, disability, and gender with the utilization for the training data and validation data shows similarity. Conclusively, the main contributions of this work are listed as follows:i.Formulate ML technique for increasing the effectiveness and robustness against data uncertainties in the prediction of home healthcare services utilization by a member.ii.Build explainable ML solutions and exhibit the use of visualizations for the post-hoc explainability of the ML models that would benefit the stakeholders

Keywords: Machine Learning, Artificial Intelligence, Explainable Model, Healthcare, Hybrid Algorithm, Visualization

DOI: 10.54941/ahfe1004837

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