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Assessing the Evidence for e-Resources for Mental Health Self-Management: A Systematic Literature Review

Eleni Karasouli, Ann Adams

JMIR Ment Health 2014;1(1):e3

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The Coags Uncomplicated App: Fulfilling Educational Gaps Around Diagnosis and Laboratory Testing of Coagulation Disorders

Craig Kessler, Ellinor I Peerschke, Meera B Chitlur, Roshni Kulkarni, Natalia Holot, David L Cooper

JMIR Med Educ 2017;3(1):e6

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Unsupervised Machine Learning to Identify High Likelihood of Dementia in Population-Based Surveys: Development and Validation Study

Definitive diagnosis of dementia in the ADAMS sample was assigned by a consensus of clinical experts [11], using international diagnostic criteria for dementia (cognitive or behavioral disorders associated with significant decline in social or occupational

Laurent Cleret de Langavant, Eleonore Bayen, Kristine Yaffe

J Med Internet Res 2018;20(7):e10493

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Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment

Public health surveillance uses ANN to forecast diseases distributions, whereas Guan et al (2004) used ANN to forecast incidents of hepatitis.

Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi

JMIR Public Health Surveill 2019;5(1):e11357

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Patients’ Attitudes Toward Electronic Health Information Exchange: Qualitative Study

It includes the city of North Adams and several smaller towns that surround it.

Steven Simon, J. Stewart Evans, Alison Benjamin, David Delano, David Bates

J Med Internet Res 2009;11(3):e30

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Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation

ANN-based algorithms have already shown high performance in terms of accuracy, sensitivity, and specificity for classification problems.

Tahmina Nasrin Poly, Md.Mohaimenul Islam, Muhammad Solihuddin Muhtar, Hsuan-Chia Yang, Phung Anh (Alex) Nguyen, Yu-Chuan (Jack) Li

JMIR Med Inform 2020;8(11):e19489

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Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Another GA was used in the work of Catalogna et al to support an ANN controller [29].

Ivan Contreras, Josep Vehi

J Med Internet Res 2018;20(5):e10775

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Structural Basis for Designing Multiepitope Vaccines Against COVID-19 Infection: In Silico Vaccine Design and Validation

/498 (97.39)–0.06279NontoxicB44:02Consensus (ann/smm)0.06N protein9KPRQKRTAT487/498 (97.79)–0.20542NontoxicB07:02Consensus(ann/smm/comblib_sidney2008)0.1orf109MGYINVFAF477/480 (99.38)–0.09452NontoxicB35:01Consensus(ann/smm/comblib_sidney2008)0.1orf1010GYINVFAFPFe232

Sukrit Srivastava, Sonia Verma, Mohit Kamthania, Rupinder Kaur, Ruchi Kiran Badyal, Ajay Kumar Saxena, Ho-Joon Shin, Michael Kolbe, Kailash C Pandey

JMIR Bioinformatics Biotechnol 2020;1(1):e19371

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Neural Network–Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

An artificial neural network (ANN) machine learning model [16] was developed using MATLAB (version R2018b; MathWorks Inc) software and was used to classify patients with asthma.

Seyed Ahmad Hosseini, Amir Jamshidnezhad, Marzie Zilaee, Behzad Fouladi Dehaghi, Abbas Mohammadi, Seyed Mohsen Hosseini

JMIR Med Inform 2020;8(7):e17580

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Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

The study used 6 indicators, that is ozone, carbon monoxide, PM10 (particulate matter of 10 μm in diameter or smaller), PM25 (particulate matter less than 2.5 μm in diameter), and sulfur dioxide, from Athens, Greece to train the ANN model.

Junfeng Peng, Chuan Chen, Mi Zhou, Xiaohua Xie, Yuqi Zhou, Ching-Hsing Luo

JMIR Med Inform 2020;8(3):e13075

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