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

Observational long-term follow-up study of rapid food oral immunotherapy with omalizumab

Abstract Background A number of clinical studies focused on treating a single food allergy through oral immunotherapy (OIT) with adjunctive omalizumab treatment have been published. We previously demonstrated safety and tolerability of a rapid OIT protocol using omalizumab in a phase 1 study to achieve desensitization to multiple (up to 5) food allergens in parallel, rapidly (7–36 weeks; median = 18 weeks). In the current long-term, observational study, we followed 34 food allergic participants for over 5 years, who had originally undergone the phase 1 rapid OIT protocol. Methods After reaching the maintenance dose of 2 g protein for each of their respective food allergens as a part of the phase 1 study, the long-term maintenance dose was reduced for some participants based on a pragmatic team-based decision. Participants were followed up to 62 months through standard oral food challenges (OFCs), skin prick tests, and blood tests. Results Each participant passed the 2 g OFC to each of their offending food allergens (up to 5 food allergens in total) at the end of the long-term follow-up (LTFU) study. Conclusion Our data demonstrate the feasibility of long-term maintenance dosing of a food allergen without compromising the desensitized status conferred through rapid-OIT. Trial registration Registry: Clinicaltrials.gov. Registration numbers: NCT01510626 (original study), NCT03234764 (LTFU study). Date of registration: November 29, 2011 (original study); July 26, 2017 (LTFU study, retrospectively registered

MOESM2 of Feasibility of sustained response through long-term dosing in food allergy immunotherapy

Additional file 2: Table S1. Summary of allergic reactions as adverse events in LTFU study

MOESM2 of Observational long-term follow-up study of rapid food oral immunotherapy with omalizumab

Additional file 2: Figure S2. Wheal diameter of SPTs for various time points during the dose escalation and after 2 g maintenance dose was reached. Each line represents one participant. The dots are colored by the dose at the specific time point

MOESM1 of Observational long-term follow-up study of rapid food oral immunotherapy with omalizumab

Additional file 1: Figure S1. Allergen-specific IgG4/IgE ratios for various time points during the dose escalation and after 2 g maintenance dose was reached. Each line represents one participant. The dots are colored by the dose at the specific time point