Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Abstract Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patient...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Juan Lu, Ling Wang, Mohammed Bennamoun, Isaac Ward, Senjian An, Ferdous Sohel, Benjamin J. W. Chow, Girish Dwivedi, Frank M. Sanfilippo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4932909e049644539ef7902709d72dbc
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4932909e049644539ef7902709d72dbc
record_format	dspace
spelling	oai:doaj.org-article:4932909e049644539ef7902709d72dbc2021-12-02T18:02:31ZMachine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data10.1038/s41598-021-97643-32045-2322https://doaj.org/article/4932909e049644539ef7902709d72dbc2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97643-3https://doaj.org/toc/2045-2322Abstract Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.Juan LuLing WangMohammed BennamounIsaac WardSenjian AnFerdous SohelBenjamin J. W. ChowGirish DwivediFrank M. SanfilippoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Juan Lu Ling Wang Mohammed Bennamoun Isaac Ward Senjian An Ferdous Sohel Benjamin J. W. Chow Girish Dwivedi Frank M. Sanfilippo Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
description	Abstract Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.
format	article
author	Juan Lu Ling Wang Mohammed Bennamoun Isaac Ward Senjian An Ferdous Sohel Benjamin J. W. Chow Girish Dwivedi Frank M. Sanfilippo
author_facet	Juan Lu Ling Wang Mohammed Bennamoun Isaac Ward Senjian An Ferdous Sohel Benjamin J. W. Chow Girish Dwivedi Frank M. Sanfilippo
author_sort	Juan Lu
title	Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
title_short	Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
title_full	Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
title_fullStr	Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
title_full_unstemmed	Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
title_sort	machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4932909e049644539ef7902709d72dbc
work_keys_str_mv	AT juanlu machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT lingwang machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT mohammedbennamoun machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT isaacward machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT senjianan machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT ferdoussohel machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT benjaminjwchow machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT girishdwivedi machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata AT frankmsanfilippo machinelearningriskpredictionmodelforacutecoronarysyndromeanddeathfromuseofnonsteroidalantiinflammatorydrugsinadministrativedata
_version_	1718378864785227776

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Ejemplares similares