Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms

Diagnostics, vol. 10, pp. 972

Abstract

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we discuss how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches.

Authors 4

1. Abolfazl Ramezanpour Aachen Leiden Academic Centre for Drug Research Faculty of Mathematics and Natural Sciences

   Leiden University · Shiraz University

   Affiliation as printed

   Department of Physics, School of Sciences, Shiraz University, 71454 Shiraz, Iran

   Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, 2333CC Leiden, The Netherlands

2. Andrew L. Beam

   Brigham and Women's Hospital · Harvard University

   Affiliation as printed

   Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA

   Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA

   Department of Newborn Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA

   Department of Newborn Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA

3. Jonathan H. Chen

   Stanford Medicine · Stanford University

   Affiliation as printed

   Biomedical Informatics, Stanford University School of Medicine, Stanford, CA 94305-5101, USA

   Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, USA

4. Alireza Mashaghi corresponding Aachen Leiden Academic Centre for Drug Research Faculty of Mathematics and Natural Sciences

   Leiden University

   Affiliation as printed

   Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, 2333CC Leiden, The Netherlands