Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study. (Record no. 10754)

MARC details
000 -LEADER
fixed length control field 10072nam a22004697a 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220222s20222022 xxu||||| |||| 00| 0 eng d
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code 10.2196/34932 [doi]
024 ## - OTHER STANDARD IDENTIFIER
Standard number or code v10i2e34932 [pii]
040 ## - CATALOGING SOURCE
Original cataloging agency Ovid MEDLINE(R)
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
PMID 35142637
245 ## - TITLE STATEMENT
Title Early Identification of Maternal Cardiovascular Risk Through Sourcing and Preparing Electronic Health Record Data: Machine Learning Study.
251 ## - Source
Source JMIR Medical Informatics. 10(2):e34932, 2022 Feb 10.
252 ## - Abbreviated Source
Abbreviated source JMIR Med Inform. 10(2):e34932, 2022 Feb 10.
253 ## - Journal Name
Journal name JMIR medical informatics
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Year 2022
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Manufacturer FY2022
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Publication date 2022 Feb 10
265 ## - SOURCE FOR ACQUISITION/SUBSCRIPTION ADDRESS [OBSOLETE]
Publication status epublish
266 ## - Date added to catalog
Date added to catalog 2022-02-22
520 ## - SUMMARY, ETC.
Abstract 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.
520 ## - SUMMARY, ETC.
Abstract 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.
520 ## - SUMMARY, ETC.
Abstract 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.
520 ## - SUMMARY, ETC.
Abstract 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.
520 ## - SUMMARY, ETC.
Abstract RESULTS: Patient records can be made actionable for the goal of effectively using ML, specifically to identify cardiovascular risk in pregnant patients.
546 ## - LANGUAGE NOTE
Language note English
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element IN PROCESS -- NOT YET INDEXED
651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME
Institution MedStar Health Research Institute
657 ## - INDEX TERM--FUNCTION
Medline publication type Journal Article
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Ahmad, Maryam F
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Falah, Noor
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Hughes, Justin M
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Shara, Nawar M
700 ## - ADDED ENTRY--PERSONAL NAME
Local Authors Tavazoei, Darya
790 ## - Authors
All authors Ahmad MF, Anderson KM, Crovatt S, Dempers R, Falah N, Hughes JM, Shara N, Talmadge B, Tavazoei D
856 ## - ELECTRONIC LOCATION AND ACCESS
DOI <a href="https://dx.doi.org/10.2196/34932">https://dx.doi.org/10.2196/34932</a>
Public note https://dx.doi.org/10.2196/34932
858 ## - ORCID
ORCID text Ahmad, Maryam F
-- Falah, Noor
-- Hughes, Justin M
-- Shara, Nawar
-- Tavazoei, Darya
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858 ## - ORCID
ORCID text Ahmad, Maryam F
-- Falah, Noor
-- Hughes, Justin M
-- Shara, Nawar
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858 ## - ORCID
ORCID text Ahmad, Maryam F
-- Falah, Noor
-- Hughes, Justin M
-- Shara, Nawar
-- Tavazoei, Darya
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858 ## - ORCID
ORCID text Ahmad, Maryam F
-- Falah, Noor
-- Hughes, Justin M
-- Shara, Nawar
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858 ## - ORCID
ORCID text Ahmad, Maryam F
-- Falah, Noor
-- Hughes, Justin M
-- Shara, Nawar
-- Tavazoei, Darya
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Journal Article
Item type description Article
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Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
          MedStar Authors Catalog MedStar Authors Catalog 02/22/2022   35142637 35142637 02/22/2022 02/22/2022 Journal Article

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