Improving diagnostic recognition of primary hyperparathyroidism with machine learning.

Journal: Surgery
Published Date: Apr 1, 2017

Abstract

BACKGROUND: Parathyroidectomy offers the only cure for primary hyperparathyroidism, but today only 50% of primary hyperparathyroidism patients are referred for operation, in large part, because the condition is widely under-recognized. The diagnosis of primary hyperparathyroidism can be especially challenging with mild biochemical indices. Machine learning is a collection of methods in which computers build predictive algorithms based on labeled examples. With the aim of facilitating diagnosis, we tested the ability of machine learning to distinguish primary hyperparathyroidism from normal physiology using clinical and laboratory data.

Authors

  • Yash R Somnay
    Section of Endocrine Surgery, Department of Surgery, University of Wisconsin, Madison, WI.
  • Mark Craven
    Department of Biostatistics and Medical Informatics, and the Department Computer Science, University of Wisconsin, Madison, WI.
  • Kelly L McCoy
    Division of Endocrine Surgery, Department of Surgery, University of Pittsburgh, Pittsburgh, PA.
  • Sally E Carty
    Division of Endocrine Surgery, Department of Surgery, University of Pittsburgh, Pittsburgh, PA.
  • Tracy S Wang
    Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
  • Caprice C Greenberg
    Wisconsin Surgical Outcomes Research Program, Department of Surgery, University of Wisconsin, Madison, WI.
  • David F Schneider
    Section of Endocrine Surgery, Department of Surgery, University of Wisconsin, Madison, WI. Electronic address: schneiderd@surgery.wisc.edu.

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