5 research outputs found
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 PETâCT-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 physiciansâ manual outlines on the
same tumors. The repeatability of the physiciansâ measurements is also
assessed. IWS utilizes multiple levels of segmentation performed with the
use of âfuzzy regionsâ 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 âbest resultâ
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