Peter Bath

Professor of Health Data Analytics

University of Sheffield

United Kingdom

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Peter Bath is a Professor of Health Data Analytics at the University of Sheffield, UK. His research focuses on health data analytics, including the role of online groups in autism information journeys, the quality of published medical research using AI tools, and the needs of patients with incurable conditions. He has also explored the relationship between environmental inequalities and multimorbidity, as well as assessing the accuracy of triage tools in emergency settings during the COVID-19 pandemic.

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Collaborators (13)

Mike Thelwall

Professor of Data Science

University of Sheffield

UNITED KINGDOM

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Annabel Crum

University of Sheffield

UNITED KINGDOM

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Suzanne Duffin

University of Sheffield

UNITED KINGDOM

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Daniel Green

Aston University

UNITED KINGDOM

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Ravi Maheswaran

Professor of Epidemiology and Public Health

University of Sheffield

UNITED KINGDOM

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Janette Turner

Reader in Emergency & Urgent Care Research

University of Sheffield

UNITED KINGDOM

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Tony Stone

University of Sheffield

UNITED KINGDOM

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Catriona Mayland

University of Sheffield

UNITED KINGDOM

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Fiona Bell

Head of Research

UNITED KINGDOM

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Philip Bath

Stroke Association Professor of Stroke Medicine

University of Nottingham

UNITED KINGDOM

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Frank Hopfgartner

Professor

University of Koblenz

GERMANY

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Elizabeth Goyder

University of Sheffield

UNITED KINGDOM

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David Hamilton

Consultant Otolaryngology, Head \and Neck Surgery

Newcastle upon Tyne Hospitals NHS Foundation Trust

UNITED KINGDOM

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Peter Bath

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Articles (21)

A network approach to addressing the needs of patients with incurable head and neck cancer and their families

The role of online groups for autistic people in users’ autism information journeys

Estimating the quality of published medical research with ChatGPT

A network approach to addressing the needs of patients with incurable head and neck cancer and their families

The role of paralinguistic digital affordances (PDAs) in online mental health communities (OMHCs) in Saudi Arabia

Accuracy of telephone triage for predicting adverse outcomes in suspected COVID-19: An observational cohort study

Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study

Insights into relationship of environmental inequalities and multimorbidity: a population-based study

Can all healthy adults use the current evidential breath alcohol analysers? An investigation using a large spirometry database.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting

An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans

Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA)

Use of Online Health Forums by People Living With Breast Cancer During the COVID-19 Pandemic: Thematic Analysis

Social Support in a Diabetes Online Community: Mixed Methods Content Analysis

Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods

Association between Internet Usage and Quality of Life of Elderly People in England: Evidence from the English Longitudinal Study of Ageing (ELSA)

Accuracy of emergency medical service telephone triage of need for an ambulance response in suspected COVID-19 : an observational cohort study

Prognostic accuracy of triage tools for adults with suspected COVID-19 in a prehospital setting : an observational cohort study

965 Accuracy of emergency (999) telephone triage for predicting adverse outcomes in suspected COVID-19 : an observational cohort study

Collaborators (13)

Mike Thelwall

Annabel Crum

Suzanne Duffin

Daniel Green

Ravi Maheswaran

Janette Turner

Tony Stone

Catriona Mayland

Fiona Bell

Philip Bath

Frank Hopfgartner

Elizabeth Goyder

David Hamilton

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