Literature DB >> 30866728

Review of Medical Decision Support and Machine-Learning Methods.

Abdullah Awaysheh1, Jeffrey Wilcke1, François Elvinger2,3, Loren Rees4, Weiguo Fan4, Kurt L Zimmerman1.   

Abstract

Machine-learning methods can assist with the medical decision-making processes at the both the clinical and diagnostic levels. In this article, we first review historical milestones and specific applications of computer-based medical decision support tools in both veterinary and human medicine. Next, we take a mechanistic look at 3 archetypal learning algorithms-naive Bayes, decision trees, and neural network-commonly used to power these medical decision support tools. Last, we focus our discussion on the data sets used to train these algorithms and examine methods for validation, data representation, transformation, and feature selection. From this review, the reader should gain some appreciation for how these decision support tools have and can be used in medicine along with insight on their inner workings.

Entities:  

Keywords:  learning algorithms; machine learning; medical decision support; review

Mesh:

Year:  2019        PMID: 30866728     DOI: 10.1177/0300985819829524

Source DB:  PubMed          Journal:  Vet Pathol        ISSN: 0300-9858            Impact factor:   2.221


  8 in total

Review 1.  Different Data Mining Approaches Based Medical Text Data.

Authors:  Wenke Xiao; Lijia Jing; Yaxin Xu; Shichao Zheng; Yanxiong Gan; Chuanbiao Wen
Journal:  J Healthc Eng       Date:  2021-12-06       Impact factor: 2.682

2.  Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data.

Authors:  Tera Pijnacker; Richard Bartels; Martin van Leeuwen; Erik Teske
Journal:  Parasit Vectors       Date:  2022-01-29       Impact factor: 3.876

3.  College Sports Decision-Making Algorithm Based on Machine Few-Shot Learning and Health Information Mining Technology.

Authors:  Rui Zhang
Journal:  Comput Intell Neurosci       Date:  2022-03-31

4.  Machine learning in the loop for tuberculosis diagnosis support.

Authors:  Alvaro D Orjuela-Cañón; Andrés L Jutinico; Carlos Awad; Erika Vergara; Angélica Palencia
Journal:  Front Public Health       Date:  2022-07-26

5.  Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.

Authors:  Yoko Nagamori; Ruth Hall Sedlak; Andrew DeRosa; Aleah Pullins; Travis Cree; Michael Loenser; Benjamin S Larson; Richard Boyd Smith; Richard Goldstein
Journal:  Parasit Vectors       Date:  2020-07-11       Impact factor: 3.876

Review 6.  Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA).

Authors:  Jonathan L Lustgarten; Ashley Zehnder; Wayde Shipman; Elizabeth Gancher; Tracy L Webb
Journal:  JAMIA Open       Date:  2020-04-11

7.  Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques.

Authors:  Young Jae Kim; Ji Soo Jeon; Seo-Eun Cho; Kwang Gi Kim; Seung-Gul Kang
Journal:  Diagnostics (Basel)       Date:  2021-03-30

Review 8.  The One Medicine concept: its emergence from history as a systematic approach to re-integrate human and veterinary medicine.

Authors:  Tracey A King
Journal:  Emerg Top Life Sci       Date:  2021-11-12
  8 in total

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