The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

Alkhodari, Mohanad; Xiong, Zhaohan; Khandoker, Ahsan H.; Hadjileontiadis, Leontios J.; Leeson, Paul; Lapidaire, Winok

doi:10.1080/14779072.2023.2223978

Cited by 9 publications

(11 citation statements)

References 119 publications

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“…In obstetrics, ML has successfully been used to determine the clinical parameters most useful for predicting preeclampsia and hypertensive disorders of pregnancy [ 12 , 26 , 27 ]. Our study provides additional evidence to support the use of ML in the field of obstetrics.…”

Section: Discussionmentioning

confidence: 99%

“…Complete EHRs were not always available for proper analysis and we did not have complete access to patients’ full health history [ 29 ]. Compared other studies applying ML to big data [ 27 ], we relied on EHRs and did not include other types of unstructured data (eg, clinical notes) in this analysis. This may have created limitations when interpreting results.…”

Section: Discussionmentioning

confidence: 99%

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Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

Shara,

Mirabal-Beltran,

Talmadge

et al. 2024

JMIR Cardio

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Background Cardiovascular conditions (eg, cardiac and coronary conditions, hypertensive disorders of pregnancy, and cardiomyopathies) were the leading cause of maternal mortality between 2017 and 2019. The United States has the highest maternal mortality rate of any high-income nation, disproportionately impacting those who identify as non-Hispanic Black or Hispanic. Novel clinical approaches to the detection and diagnosis of cardiovascular conditions are therefore imperative. Emerging research is demonstrating that machine learning (ML) is a promising tool for detecting patients at increased risk for hypertensive disorders during pregnancy. However, additional studies are required to determine how integrating ML and big data, such as electronic health records (EHRs), can improve the identification of obstetric patients at higher risk of cardiovascular conditions. Objective This study aimed to evaluate the capability and timing of a proprietary ML algorithm, Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-Risk Assessment Technology (HOPE-CAT), to detect maternal-related cardiovascular conditions and outcomes. Methods Retrospective data from the EHRs of a large health care system were investigated by HOPE-CAT in a virtual server environment. Deidentification of EHR data and standardization enabled HOPE-CAT to analyze data without pre-existing biases. The ML algorithm assessed risk factors selected by clinical experts in cardio-obstetrics, and the algorithm was iteratively trained using relevant literature and current standards of risk identification. After refinement of the algorithm’s learned risk factors, risk profiles were generated for every patient including a designation of standard versus high risk. The profiles were individually paired with clinical outcomes pertaining to cardiovascular pregnancy conditions and complications, wherein a delta was calculated between the date of the risk profile and the actual diagnosis or intervention in the EHR. Results In total, 604 pregnancies resulting in birth had records or diagnoses that could be compared against the risk profile; the majority of patients identified as Black (n=482, 79.8%) and aged between 21 and 34 years (n=509, 84.4%). Preeclampsia (n=547, 90.6%) was the most common condition, followed by thromboembolism (n=16, 2.7%) and acute kidney disease or failure (n=13, 2.2%). The average delta was 56.8 (SD 69.7) days between the identification of risk factors by HOPE-CAT and the first date of diagnosis or intervention of a related condition reported in the EHR. HOPE-CAT showed the strongest performance in early risk detection of myocardial infarction at a delta of 65.7 (SD 81.4) days. Conclusions This study provides additional evidence to support ML in obstetrical patients to enhance the early detection of cardiovascular conditions during pregnancy. ML can synthesize multiday patient presentations to enhance provider decision-making and potentially reduce maternal health disparities.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

Shara,

Mirabal-Beltran,

Talmadge

et al. 2024

JMIR Cardio

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“…Each scenario included a detailed patient history, presenting symptoms, and potential questions and answers to simulate a realistic patient-doctor interaction. (10).…”

Section: Scenario Developmentmentioning

confidence: 99%

Using of AI as standardized patients in Pediatric Surgeon Training Program： A tentative exploration

Shen,

Liang,

Xiang

2024

Preprint

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Purpose: Traditional standardized patients (SP) are expensive and not so feasible. We tried to explore the prospective capacity of a natural language processing model (ChatGPT 4.0) as SP in pediatric surgeon training. Methods: Using AI as a SP involves creating scenarios and interacting with the AI model to simulate patient-doctor interactions, developing conversational scripts, integrating medical knowledge, incorporating feedback mechanisms, considering ethical and privacy Issues. Results: We successfully create standardized patients for pediatric surgeon training with ChatGPT 4.0. Conclusion: Using ChatGPT as a standardized patient for pediatric surgeon training can be a feasible and valuable approach, but it comes with certain considerations and limitations.

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“…If all the available data are typed in, the program gives the approximate risk to the doctor for the development of preeclampsia later during the pregnancy. Some of these programs are also available in the form of a mobile phone application, this way being more accessible to professionals [ 131 , 132 , 133 ]. Grünebaum et al also claim that ChatGPT could have a massive educational role in obstetrics and gynecology as well, both on the patients’ and professionals’ side [ 134 ].…”

Section: Early Detection and Monitoring With The Help Of Rapidly Adva...mentioning

confidence: 99%

“…mobile phone application, this way being more accessible to professionals [131][132][133]. Grünebaum et al also claim that ChatGPT could have a massive educational role in obstetrics and gynecology as well, both on the patients' and professionals' side [134].…”

Section: Early Detection and Monitoring With The Help Of Rapidly Adva...mentioning

confidence: 99%

Recent Advances in the Prevention and Screening of Preeclampsia

Mészáros,

Kukor,

Valent

2023

JCM

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Throughout the history of medicine, preeclampsia has remained an enigmatic field of obstetrics. In 2023, despite its prevalence and impact, preeclampsia’s exact cause and effective treatment remain elusive; the current options are limited to delivery. The purpose of this review is to summarize the knowledge of the possible novel prophylactic therapies and screening methods for preeclampsia, thereby providing valuable insights for healthcare professionals and researchers. Aspirin and LMWH have already been widely used; meanwhile, calcium, vitamin D, and pravastatin show promise, and endothelin receptor antagonists are being explored. Stress reduction, dietary changes, and lifestyle modifications are also being investigated. Another interesting and fast-growing area is AI- and software-based screening methods. It is also key to find novel biomarkers, which, in some cases, are not only able to predict the development of the disease, but some of them hold promise to be a potential therapeutic target. We conclude that, while a definitive cure for preeclampsia may not be eligible in the near future, it is likely that the assessment and enhancement of preventive methods will lead to the prevention of many cases. However, it is also important to highlight that more additional research is needed in the future to clarify the exact pathophysiology of preeclampsia and to thus identify potential therapeutic targets for more improved treatment methods.

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The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare

Cited by 9 publications

References 119 publications

Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

Use of Machine Learning for Early Detection of Maternal Cardiovascular Conditions: Retrospective Study Using Electronic Health Record Data

Using of AI as standardized patients in Pediatric Surgeon Training Program： A tentative exploration

Recent Advances in the Prevention and Screening of Preeclampsia

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