Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study.
Citation: JMIR Medical Informatics. 10(2):e34932, 2022 Feb 10.PMID: 35142637Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXEDYear: 2022Abstract: BACKGROUND: Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual's health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States.CONCLUSIONS: Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients. Copyright (c)Nawar Shara, Kelley M Anderson, Noor Falah, Maryam F Ahmad, Darya Tavazoei, Justin M Hughes, Bethany Talmadge, Samantha Crovatt, Ramon Dempers. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 10.02.2022.METHODS: We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient's baselines to inform early interventions.OBJECTIVE: This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient's record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method.RESULTS: Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients.Fiscal year: FY2022Digital Object Identifier: ORCID:- Ahmad, Maryam F Falah, Noor Hughes, Justin M Shara, Nawar Tavazoei, Darya:
- https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 Ahmad, Maryam F Falah, Noor Hughes, Justin M Shara, Nawar Tavazoei, Darya:
- https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 Ahmad, Maryam F Falah, Noor Hughes, Justin M Shara, Nawar Tavazoei, Darya:
- https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 Ahmad, Maryam F Falah, Noor Hughes, Justin M Shara, Nawar Tavazoei, Darya:
- https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 Ahmad, Maryam F Falah, Noor Hughes, Justin M Shara, Nawar Tavazoei, Darya:
- https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291 https://orcid.org/0000-0002-6769-2107 https://orcid.org/0000-0002-7045-9162 https://orcid.org/0000-0002-3067-9902 https://orcid.org/0000-0002-9005-252X https://orcid.org/0000-0001-9728-2291
Item type | Current library | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|
Journal Article | MedStar Authors Catalog | Article | 35142637 | Available | 35142637 |
BACKGROUND: Health care data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes. However, the differences that exist in each individual's health records, combined with the lack of health data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. Although these problems exist throughout health care, they are especially prevalent within maternal health and exacerbate the maternal morbidity and mortality crisis in the United States.
CONCLUSIONS: Upon acquiring data, including their concatenation, anonymization, and normalization across multiple EHRs, the use of an ML-based tool can provide early identification of cardiovascular risk in pregnant patients. Copyright (c)Nawar Shara, Kelley M Anderson, Noor Falah, Maryam F Ahmad, Darya Tavazoei, Justin M Hughes, Bethany Talmadge, Samantha Crovatt, Ramon Dempers. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 10.02.2022.
METHODS: We outline the effort that was required to define the specifications of the computational systems, the data set, and access to relevant systems, while ensuring that data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for their use by a proprietary risk stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient's baselines to inform early interventions.
OBJECTIVE: This study aims to demonstrate that patient records extracted from the electronic health records (EHRs) of a large tertiary health care system can be made actionable for the goal of effectively using ML to identify maternal cardiovascular risk before evidence of diagnosis or intervention within the patient's record. Maternal patient records were extracted from the EHRs of a large tertiary health care system and made into patient-specific, complete data sets through a systematic method.
RESULTS: Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients.
English