Faculty Opinions recommendation of Use of machine learning to shorten observation-based screening and diagnosis of autism.

2012 ◽  
Author(s):  
Jane Holmes Bernstein
Keyword(s):  
Machine Learning ◽  
Screening And Diagnosis

Applications of Machine Learning in Disease Pre-screening

2019 ◽  
pp. 278-320 ◽  
Author(s):  
Upendra Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Classification Accuracy ◽  
Medical Information ◽  
Disease Risk ◽  
Learning Technology ◽  
Crucial Step ◽  
Computer Aided ◽  
Applications Of Machine Learning ◽  
Screening And Diagnosis

Computers in disease prescreening are utilized to interpret medical information. This is known as computer-aided pre-screening tool (CAPST). CAPST helps in improving the accuracy of diagnosis in medicine. The medical experts usually take the outcome of the CAPST as a second opinion to make the final diagnostic decisions. Fast and accurate prediction of disease risk and diagnosis is crucial step for the successful treatment of an individual. The AI-based machine learning technology has undergone significant developments over the past few years and is successfully used in many intelligent applications covering problems of variety of domains. One of the most stimulating questions is whether these techniques can be successfully applied to medicine in disease pre-screening and diagnosis and what kind of data it requires to be trained and learned. There are so many real-time examples of the problems where machine learning methods are applied successfully, especially in medicine. Many of them showed significant improvement in classification accuracy.

Applications of Machine Learning in Disease Pre-screening

2021 ◽  
pp. 1052-1084
Author(s):  
Upendra Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Classification Accuracy ◽  
Medical Information ◽  
Disease Risk ◽  
Learning Technology ◽  
Crucial Step ◽  
Computer Aided ◽  
Applications Of Machine Learning ◽  
Screening And Diagnosis

Computers in disease prescreening are utilized to interpret medical information. This is known as computer-aided pre-screening tool (CAPST). CAPST helps in improving the accuracy of diagnosis in medicine. The medical experts usually take the outcome of the CAPST as a second opinion to make the final diagnostic decisions. Fast and accurate prediction of disease risk and diagnosis is crucial step for the successful treatment of an individual. The AI-based machine learning technology has undergone significant developments over the past few years and is successfully used in many intelligent applications covering problems of variety of domains. One of the most stimulating questions is whether these techniques can be successfully applied to medicine in disease pre-screening and diagnosis and what kind of data it requires to be trained and learned. There are so many real-time examples of the problems where machine learning methods are applied successfully, especially in medicine. Many of them showed significant improvement in classification accuracy.

scholarly journals Use of machine learning to shorten observation-based screening and diagnosis of autism

2012 ◽  
Vol 2 (4) ◽  
pp. e100-e100 ◽  
Author(s):  
D P Wall ◽  
J Kosmicki ◽  
T F DeLuca ◽  
E Harstad ◽  
V A Fusaro
Keyword(s):  
Machine Learning ◽  
Screening And Diagnosis

scholarly journals Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review

2020 ◽  
Vol 5 (4) ◽  
pp. 147-159
Author(s):  
Kimberley Yu ◽  
Maha N. Syed ◽  
Elena Bernardis ◽  
Joel M. Gelfand
Keyword(s):  
Machine Learning ◽  
Clinical Management ◽  
Lesion Segmentation ◽  
Search Queries ◽  
Or Management ◽  
Challenges And Opportunities ◽  
Machine Learning Applications ◽  
Prediction Of Complications ◽  
Screening And Diagnosis ◽  
Lesion Severity

Background: Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction. Objective: To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances. Methods: We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and “psoriasis” in the title and/or abstract. Results: Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment. Conclusion: Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

scholarly journals Review of Machine Learning Applications Using Retinal Fundus Images

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 134
Author(s):  
Yeonwoo Jeong ◽  
Yu-Jin Hong ◽  
Jae-Ho Han
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Related Macular Degeneration ◽  
Machine Learning Techniques ◽  
Fundus Image ◽  
Learning Methods ◽  
Age Related ◽  
Automated Screening ◽  
Complex Features ◽  
Screening And Diagnosis

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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