Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.Comment: Accepted for publication in The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), in the Innovative Applications of Artificial Intelligence track. This is the extended version with some stylistic fixes from the first posting and complete author lis

Contrast-enhanced Dedicated Breast CT: Initial Clinical Experience1

Conspicuity of malignant breast masses at contrast-enhanced breast CT is significantly better than that at mammography or unenhanced breast CT, whereas conspicuity of lesions associated with malignant calcifications is better at contrast-enhanced breast CT than at unenhanced breast CT and is similar at contrast-enhanced breast CT and mammography

Comparison of two-dimensional and three-dimensional iterative watershed segmentation methods in hepatic tumor volumetrics

In this work the authors compare the accuracy of two-dimensional (2D) and three-dimensional (3D) implementations of a computer-aided image segmentation method to that of physician observers (using manual outlining) for volume measurements of liver tumors visualized with diagnostic contrast-enhanced and PET∕CT-based non-contrast-enhanced (PET-CT) CT scans. The method assessed is a hybridization of the watershed method using observer-set markers with a gradient vector flow approach. This method is known as the iterative watershed segmentation (IWS) method. Initial assessments are performed using software phantoms that model a range of tumor shapes, noise levels, and noise qualities. IWS is then applied to CT image sets of patients with identified hepatic tumors and compared to the physicians’ manual outlines on the same tumors. The repeatability of the physicians’ measurements is also assessed. IWS utilizes multiple levels of segmentation performed with the use of “fuzzy regions” that could be considered part of a selected tumor. In phantom studies, the outermost volume outline for level 1 (called level 1_1 consisting of inner region plus fuzzy region) was generally the most accurate. For in vivo studies, the level 1_1 and the second outermost outline for level 2 (called level 2_2 consisting of inner region plus two fuzzy regions) typically had the smallest percent error values when compared to physician observer volume estimates. Our data indicate that allowing the operator to choose the “best result” level iteration outline from all generated outlines would likely give the more accurate volume for a given tumor rather than automatically choosing a particular level iteration outline. The preliminary in vivo results indicate that 2D-IWS is likely to be more accurate than 3D-IWS in relation to the observer volume estimates