Work

Classifying Lung Cancer Severity with Ensemble Machine Learning in Health Care Claims Data.

Year: 2017

Type: article

Abstract: ) considering an augmented set of administrative claims data. We find our ensemble machine learning algorithm with a classification rule defined by the median substantially outperforms an existing clinical decision tree for this problem, yielding full sample performance of 93% sensitivity, 92% specificity, and 93% accuracy. This work has the potential for broad applicability as provider organizations, payers, and policy makers seek to measure quality and outcomes of cancer care and improve on risk adjustment methods.

Source: PubMed

Authors: Savannah L Bergquist, Gabriel A Brooks, Nancy L Keating, Mary Beth Landrum, Sherri Rose

Institutions: Harvard University, Dartmouth–Hitchcock Medical Center, Brigham and Women's Hospital

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health

Award: Health Policy Training Program: Promoting Outcomes, Quality, and Diffusion of Advances