Predicting Disease Severity using Novel Digital Mobility Outcomes: A Deep Learning Approach in COPD Patients - Vienna 2024

Respiratory care in the digital age: innovative applications and their evidence

Chair(s): Io Hui, Niels H. Chavannes

Session type: Oral presentation

Predicting Disease Severity using Novel Digital Mobility Outcomes: A Deep Learning Approach in COPD Patients

Dimitrios Megaritis

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Session content

Late Breaking Abstract - Assessment of an electronic system on the impact on inhaler skills and technique ASSIST study

Clare Murray

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Poster

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Clinical scenarios in paediatric pulmonology: Can large language models fare better than trainee doctors?

Manjith Narayanan

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Poster

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Application of ChatGPT for clinical decision-making in patients with community-acquired pneumonia.

Vitalii Poberezhets

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Poster

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Using language models to integrate clinical decision support and note taking: a qualitative study

Amin Adibi

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Poster

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Predicting Disease Severity using Novel Digital Mobility Outcomes: A Deep Learning Approach in COPD Patients

Dimitrios Megaritis

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Poster

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Participant experience of Scottish Ballets online dance based long COVID support programme: A mixed methods study

Keir E. J. Philip

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Analysis of Instagram publications on bronchial asthma and their compliance with GINA

Mariia Tsyben

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Poster

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Retrospective study on early prediction of major adverse events in general ward with AI algorithm

Ki-byung Lee

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Poster

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Uptake and effectiveness of a Virtual Model of Antenatal Asthma Care (VMAC) compared to a face-to-face model of care: a randomised controlled tele-trial

Dennis Thomas

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Co-developing and operationalising machine learning models for COPD care in a clinical setting: a workflow framework

Chris Carlin

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Discussion and Q&A

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CDN Connection Issue

The ERS Congress 2024, Vienna

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