Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

MedStar author(s):
Citation: AMIA ... Annual Symposium Proceedings/AMIA Symposium. 2019:228-237, 2019.PMID: 32308815Institution: MedStar Washington Hospital CenterDepartment: Emergency MedicineForm of publication: Journal ArticleMedline article type(s): Journal Article | Research Support, N.I.H., ExtramuralSubject headings: *Decision Support Systems, Clinical | *Deep Learning | *Electronic Health Records | *Medical History Taking/mt [Methods] | *Respiratory Distress Syndrome, Adult | Comorbidity | Hospitalization | Humans | Prognosis | Respiratory Distress Syndrome, Adult/co [Complications] | Respiratory Distress Syndrome, Adult/mo [Mortality] | Risk FactorsYear: 2019ISSN:
  • 1559-4076
Name of journal: AMIA ... Annual Symposium proceedings. AMIA SymposiumAbstract: In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARDS risk factors, utilizing deep-learning techniques on ICD and free-text clinical notes data. The Patient Context Vectors were combined with structured data from the first 24 hours of admission, such as vital signs and lab results, to build an ARDS patient prediction model and an ARDS patient mortality prediction model achieving AUC of 90.16 and 81.01 respectively. The ability of Patient Context Vectors to summarize patients' medical history and current conditions is also demonstrated by the automatic clustering of ARDS patients into clinically meaningful phenotypes based on comorbidities, patient history, and presenting conditions. To our knowledge, this is the first study to successfully combine free-text and structured data, without any manual patient risk factor curation, to build real-time ARDS prediction models. Copyright (c)2019 AMIA - All rights reserved.All authors: Apostolova E, Galarraga JE, Koutroulis I, Tschampel T, Uppal A, Velez T, Wang TOriginally published: AMIA ... Annual Symposium Proceedings/AMIA Symposium. 2019:228-237, 2019.Fiscal year: FY2020Date added to catalog: 2020-07-09
Holdings
Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 32308815 Available 32308815

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS phenotype clusters. We derived 'Patient Context Vectors' representing patientspecific contextual ARDS risk factors, utilizing deep-learning techniques on ICD and free-text clinical notes data. The Patient Context Vectors were combined with structured data from the first 24 hours of admission, such as vital signs and lab results, to build an ARDS patient prediction model and an ARDS patient mortality prediction model achieving AUC of 90.16 and 81.01 respectively. The ability of Patient Context Vectors to summarize patients' medical history and current conditions is also demonstrated by the automatic clustering of ARDS patients into clinically meaningful phenotypes based on comorbidities, patient history, and presenting conditions. To our knowledge, this is the first study to successfully combine free-text and structured data, without any manual patient risk factor curation, to build real-time ARDS prediction models. Copyright (c)2019 AMIA - All rights reserved.

English

Powered by Koha