Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.
Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.
- 2019
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
1559-4076
PMC7153087 [pmc]
*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 Factors
MedStar Washington Hospital Center
Emergency Medicine
Journal Article
Research Support, N.I.H., Extramural
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
1559-4076
PMC7153087 [pmc]
*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 Factors
MedStar Washington Hospital Center
Emergency Medicine
Journal Article
Research Support, N.I.H., Extramural