Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification

Introduction: Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients’ smoking status into electronic health records (EHR) and deliver smoking cessation assistance. Methods: We analysed the r...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Eveliina Hirvonen, Antti Karlsson, Tarja Saaresranta, Tarja Laitinen
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	smoking smoking cessation smoking intervention electronic health records medical narrative natural language processing machine learning deep learning artificial intelligence language modelling transfer learning ulmfit Diseases of the respiratory system RC705-779
Acceso en línea:	https://doaj.org/article/4a18ef6a21bb414c9a16538ed3d123f2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4a18ef6a21bb414c9a16538ed3d123f2
record_format	dspace
spelling	oai:doaj.org-article:4a18ef6a21bb414c9a16538ed3d123f22021-11-26T11:19:49ZDocumentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification2001-852510.1080/20018525.2021.2004664https://doaj.org/article/4a18ef6a21bb414c9a16538ed3d123f22021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/20018525.2021.2004664https://doaj.org/toc/2001-8525Introduction: Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients’ smoking status into electronic health records (EHR) and deliver smoking cessation assistance. Methods: We analysed the results using a combination of rule and deep learning-based algorithms. Narrative reports of all adult patients, whose treatment started between years 2010 and 2016 for one of seven common chronic diseases, were followed for two years. Smoking related sentences were first extracted with a rule-based algorithm. Subsequently, pre-trained ULMFiT-based algorithm classified each patient’s smoking status as a current smoker, ex-smoker, or never smoker. A rule-based algorithm was then again used to analyse the physician-patient discussions on smoking cessation among current smokers. Results: A total of 35,650 patients were studied. Of all patients, 60% were found to have a smoking status in EHR and the documentation improved over time. Smoking status was documented more actively among COPD (86%) and sleep apnoea (83%) patients compared to patients with asthma, type 1&2 diabetes, cerebral infarction and ischemic heart disease (range 44-61%). Of the current smokers (N=7,105), 49% had discussed smoking cessation with their physician. The performance of ULMFiT-based classifier was good with F-scores 79-92. Conclusion: Ee found that smoking status was documented in 60% of patients with chronic disease and that the clinician had discussed smoking cessation in 49% of patients who were current smokers. ULMFiT-based classifier showed good/excellent performance and allowed us to efficiently study a large number of patients’ medical narratives.Eveliina HirvonenAntti KarlssonTarja SaaresrantaTarja LaitinenTaylor & Francis Grouparticlesmokingsmoking cessationsmoking interventionelectronic health recordsmedical narrativenatural language processingmachine learningdeep learningartificial intelligencelanguage modellingtransfer learningulmfitDiseases of the respiratory systemRC705-779ENEuropean Clinical Respiratory Journal, Vol 8, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smoking smoking cessation smoking intervention electronic health records medical narrative natural language processing machine learning deep learning artificial intelligence language modelling transfer learning ulmfit Diseases of the respiratory system RC705-779
spellingShingle	smoking smoking cessation smoking intervention electronic health records medical narrative natural language processing machine learning deep learning artificial intelligence language modelling transfer learning ulmfit Diseases of the respiratory system RC705-779 Eveliina Hirvonen Antti Karlsson Tarja Saaresranta Tarja Laitinen Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
description	Introduction: Smoking cessation is essential part of a successful treatment in many chronic diseases. Our aim was to analyse how actively clinicians discuss and document patients’ smoking status into electronic health records (EHR) and deliver smoking cessation assistance. Methods: We analysed the results using a combination of rule and deep learning-based algorithms. Narrative reports of all adult patients, whose treatment started between years 2010 and 2016 for one of seven common chronic diseases, were followed for two years. Smoking related sentences were first extracted with a rule-based algorithm. Subsequently, pre-trained ULMFiT-based algorithm classified each patient’s smoking status as a current smoker, ex-smoker, or never smoker. A rule-based algorithm was then again used to analyse the physician-patient discussions on smoking cessation among current smokers. Results: A total of 35,650 patients were studied. Of all patients, 60% were found to have a smoking status in EHR and the documentation improved over time. Smoking status was documented more actively among COPD (86%) and sleep apnoea (83%) patients compared to patients with asthma, type 1&2 diabetes, cerebral infarction and ischemic heart disease (range 44-61%). Of the current smokers (N=7,105), 49% had discussed smoking cessation with their physician. The performance of ULMFiT-based classifier was good with F-scores 79-92. Conclusion: Ee found that smoking status was documented in 60% of patients with chronic disease and that the clinician had discussed smoking cessation in 49% of patients who were current smokers. ULMFiT-based classifier showed good/excellent performance and allowed us to efficiently study a large number of patients’ medical narratives.
format	article
author	Eveliina Hirvonen Antti Karlsson Tarja Saaresranta Tarja Laitinen
author_facet	Eveliina Hirvonen Antti Karlsson Tarja Saaresranta Tarja Laitinen
author_sort	Eveliina Hirvonen
title	Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
title_short	Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
title_full	Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
title_fullStr	Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
title_full_unstemmed	Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification
title_sort	documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ulmfit based text classification
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/4a18ef6a21bb414c9a16538ed3d123f2
work_keys_str_mv	AT eveliinahirvonen documentationofthepatientssmokingstatusincommonchronicdiseasesanalysisofmedicalnarrativereportsusingtheulmfitbasedtextclassification AT anttikarlsson documentationofthepatientssmokingstatusincommonchronicdiseasesanalysisofmedicalnarrativereportsusingtheulmfitbasedtextclassification AT tarjasaaresranta documentationofthepatientssmokingstatusincommonchronicdiseasesanalysisofmedicalnarrativereportsusingtheulmfitbasedtextclassification AT tarjalaitinen documentationofthepatientssmokingstatusincommonchronicdiseasesanalysisofmedicalnarrativereportsusingtheulmfitbasedtextclassification
_version_	1718409466380025856

Documentation of the patient’s smoking status in common chronic diseases – analysis of medical narrative reports using the ULMFiT based text classification

Ejemplares similares