Source: Journal of medical Internet research [J Med Internet Res] 2022 Oct 14; Vol. 24 (10), pp. e38472. Date of Electronic Publication: 2022 Oct 14.Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium:

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Academic Journal

Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning

Authors : Alexandre Infanti; Vladan Starcevic; Adriano Schimmenti

Subjects: Medicine

Source: JMIR Formative Research, Vol 7, p e42206 (2023)

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Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study

Authors : Luke Winston; Michael McCann; George Onofrei

Subjects: Medicine

Source: JMIR Formative Research, Vol 6, Iss 9, p e35114 (2022)

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Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Authors : Marie Humbert-Droz; Pritam Mukherjee; Olivier Gevaert

Subjects: Computer applications to medicine. Medical informatics; R858-859.7

Source: JMIR Medical Informatics, Vol 10, Iss 3, p e32903 (2022)

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Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

Authors : Gibbons C; Centre for Health Services Research, University of Cambridge, Cambridge, United Kingdom.; The Psychometrics Centre, University of Cambridge, Cambridge, United Kingdom.

Subjects: Algorithms* ; Clinical Competence* ; Supervised Machine Learning*

Source: Journal of medical Internet research [J Med Internet Res] 2017 Mar 15; Vol. 19 (3), pp. e65. Date of Electronic Publication: 2017 Mar 15.Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium:

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Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing

Authors : Rankin, Debbie; Black, Michaela; Bond, Raymond

Subjects: Computer applications to medicine. Medical informatics; R858-859.7

Source: JMIR Medical Informatics, Vol 8, Iss 7, p e18910 (2020)

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Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Authors : Rankin, Debbie; Black, Michaela; Flanagan, Bronac

Subjects: Computer applications to medicine. Medical informatics; R858-859.7

Source: JMIR Medical Informatics, Vol 8, Iss 9, p e20995 (2020)

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Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.

Authors : Infanti, Alexandre; Starcevic, Vladan; Schimmenti, Adriano

Subjects: 610 Medicine & health; 360 Social problems & social services

Source: Infanti, Alexandre; Starcevic, Vladan; Schimmenti, Adriano; Khazaal, Yasser; Karila, Laurent; Giardina, Alessandro; Flayelle, Maèva; Hedayatzadeh Razavi, Seyedeh Boshra; Baggio, Stéphanie; Vögele,

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Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

Authors : Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy

Subjects: Computer applications to medicine. Medical informatics; R858-859.7; Public aspects of medicine

Source: Journal of Medical Internet Research, Vol 17, Iss 8, p e208 (2015)

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فلترة النتائج

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study

The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis.

Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning

Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

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