Natural language processing to automate a web-based model of care and modernize skin cancer multidisciplinary team meetings.

Autor:	Ali SR; Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK.; Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK., Dobbs TD; Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK.; Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK., Tarafdar A; Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK.; Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK., Strafford H; Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.; Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK., Fonferko-Shadrach B; Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.; Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK., Lacey AS; Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.; Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, UK., Pickrell WO; Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, UK.; Department of Neurology, Morriston Hospital, Swansea, UK., Hutchings HA; Faculty of Medicine, Health and Life Science, Swansea University Medical School, Swansea, UK., Whitaker IS; Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, UK.; Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, UK.
Jazyk:	angličtina
Zdroj:	The British journal of surgery [Br J Surg] 2024 Jan 03; Vol. 111 (1).
DOI:	10.1093/bjs/znad347
Abstrakt:	Background: Cancer multidisciplinary team (MDT) meetings are under intense pressure to reform given the rapidly rising incidence of cancer and national mandates for protocolized streaming of cases. The aim of this study was to validate a natural language processing (NLP)-based web platform to automate evidence-based MDT decisions for skin cancer with basal cell carcinoma as a use case. Methods: A novel and validated NLP information extraction model was used to extract perioperative tumour and surgical factors from histopathology reports. A web application with a bespoke application programming interface used data from this model to provide an automated clinical decision support system, mapped to national guidelines and generating a patient letter to communicate ongoing management. Performance was assessed against retrospectively derived recommendations by two independent and blinded expert clinicians. Results: There were 893 patients (1045 lesions) used to internally validate the model. High accuracy was observed when compared against human predictions, with an overall value of 0.92. Across all classifiers the virtual skin MDT was highly specific (0.96), while sensitivity was lower (0.72). Conclusion: This study demonstrates the feasibility of a fully automated, virtual, web-based service model to host the skin MDT with good system performance. This platform could be used to support clinical decision-making during MDTs as 'human in the loop' approach to aid protocolized streaming. Future prospective studies are needed to validate the model in tumour types where guidelines are more complex. (© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.)
Databáze:	MEDLINE
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