Literature DB >> 30102808

eDoctor: machine learning and the future of medicine.

G S Handelman1, H K Kok2, R V Chandra3,4, A H Razavi5,6, M J Lee7, H Asadi3,8,9.   

Abstract

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.
© 2018 The Association for the Publication of the Journal of Internal Medicine.

Entities:  

Keywords:  artificial intelligence; machine learning; medicine; supervised machine learning; unsupervised machine learning

Mesh:

Year:  2018        PMID: 30102808     DOI: 10.1111/joim.12822

Source DB:  PubMed          Journal:  J Intern Med        ISSN: 0954-6820            Impact factor:   8.989


  78 in total

Review 1.  The use of deep learning technology for the detection of optic neuropathy.

Authors:  Mei Li; Chao Wan
Journal:  Quant Imaging Med Surg       Date:  2022-03

2.  Machine Learning for Urodynamic Detection of Detrusor Overactivity.

Authors:  Kevin T Hobbs; Nathaniel Choe; Leonid I Aksenov; Lourdes Reyes; Wilkins Aquino; Jonathan C Routh; James A Hokanson
Journal:  Urology       Date:  2021-10-29       Impact factor: 2.649

Review 3.  Artificial Intelligence for Drug Toxicity and Safety.

Authors:  Anna O Basile; Alexandre Yahi; Nicholas P Tatonetti
Journal:  Trends Pharmacol Sci       Date:  2019-08-02       Impact factor: 14.819

Review 4.  The Use of Quantitative Digital Pathology to Measure Proteoglycan and Glycosaminoglycan Expression and Accumulation in Healthy and Diseased Tissues.

Authors:  A Sally Davis; Mary Y Chang; Jourdan E Brune; Teal S Hallstrand; Brian Johnson; Sarah Lindhartsen; Stephen M Hewitt; Charles W Frevert
Journal:  J Histochem Cytochem       Date:  2020-09-16       Impact factor: 2.479

Review 5.  Application of machine learning in predicting hospital readmissions: a scoping review of the literature.

Authors:  Yinan Huang; Ashna Talwar; Satabdi Chatterjee; Rajender R Aparasu
Journal:  BMC Med Res Methodol       Date:  2021-05-06       Impact factor: 4.615

Review 6.  Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.

Authors:  Jun Tan; Feng Qin; Jiuhong Yuan
Journal:  Transl Androl Urol       Date:  2021-04

7.  Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.

Authors:  Cheyenne Mangold; Sarah Zoretic; Keerthi Thallapureddy; Axel Moreira; Kevin Chorath; Alvaro Moreira
Journal:  Neonatology       Date:  2021-07-14       Impact factor: 4.035

8.  Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention.

Authors:  Shangyu Liu; Shengwen Yang; Anlu Xing; Lihui Zheng; Lishui Shen; Bin Tu; Yan Yao
Journal:  Cardiovasc Diagn Ther       Date:  2021-06

9.  Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study.

Authors:  Jun Zhang; Hong Peng; Yu-Lin Wang; Hua-Feng Xiao; Yuan-Yuan Cui; Xiang-Bing Bian; De-Kang Zhang; Lin Ma
Journal:  Front Oncol       Date:  2021-05-13       Impact factor: 6.244

Review 10.  Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Authors:  Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba
Journal:  Eur J Hybrid Imaging       Date:  2020-12-09
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