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

Ramezanpour, Abolfazl; Beam, Andrew L.; Chen, Jonathan H.; Mashaghi, Alireza

doi:10.3390/diagnostics10110972

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

¹

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

²

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

³

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

⁴

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

⁵

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

⁶

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

⁷

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

^*

Author to whom correspondence should be addressed.

Diagnostics 2020, 10(11), 972; https://doi.org/10.3390/diagnostics10110972

Submission received: 6 November 2020 / Revised: 16 November 2020 / Accepted: 17 November 2020 / Published: 19 November 2020

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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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.

Keywords: diagnostic process; statistical physics; disease progression

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MDPI and ACS Style

Ramezanpour, A.; Beam, A.L.; Chen, J.H.; Mashaghi, A. Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms. Diagnostics 2020, 10, 972. https://doi.org/10.3390/diagnostics10110972

AMA Style

Ramezanpour A, Beam AL, Chen JH, Mashaghi A. Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms. Diagnostics. 2020; 10(11):972. https://doi.org/10.3390/diagnostics10110972

Chicago/Turabian Style

Ramezanpour, Abolfazl, Andrew L. Beam, Jonathan H. Chen, and Alireza Mashaghi. 2020. "Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms" Diagnostics 10, no. 11: 972. https://doi.org/10.3390/diagnostics10110972

APA Style

Ramezanpour, A., Beam, A. L., Chen, J. H., & Mashaghi, A. (2020). Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms. Diagnostics, 10(11), 972. https://doi.org/10.3390/diagnostics10110972

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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

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