Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

148
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
148
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

426
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
426

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Long-Term Outcomes in Aortic Stenosis: Mortality Analysis in a Selected Patient Group.

Journal of personalized medicine·2025
Same author

The Proteomic and Peptidomic Response of Wheat (<i>Triticum aestivum</i> L.) to Drought Stress.

Plants (Basel, Switzerland)·2025
Same author

Multi-agent norm perception and induction in distributed healthcare.

Journal of biomedical informatics·2025
Same author

Modelling diversity in hospital strategies in city-scale ambulance dispatching with coupled game-theoretic model and discrete-event simulation.

Journal of biomedical informatics·2025
Same author

Prognostic Value of Ceramide Dynamics in Patients with Acute Coronary Syndrome.

Studies in health technology and informatics·2024
Same author

Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.

Studies in health technology and informatics·2024

Related Experiment Video

Updated: Jul 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Machine Learning Methods for Pregnancy and Childbirth Risk Management.

Georgy Kopanitsa1,2, Oleg Metsker2, Sergey Kovalchuk1

  • 1Faculty of Digital Transformations, ITMO University, 4 Birzhevaya Liniya, 199034 Saint-Petersburg, Russia.

Journal of Personalized Medicine
|June 28, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict pregnancy and childbirth risks using electronic health records. This technology aids in managing perinatal care and improving outcomes for mothers and infants.

Keywords:
childbirthdelivery datemachine learningpredictionrisk factors

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Jul 25, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Medical Informatics
  • Public Health
  • Machine Learning in Healthcare

Background:

  • Machine learning (ML) offers data-driven decision support in healthcare, reducing reliance on explicit rule design.
  • Identifying and managing pregnancy and childbirth risks early is crucial for preventing adverse perinatal outcomes.
  • Clinical decision support systems (CDSSs) can alleviate medical professional burden but require high-quality, interpretable models.

Purpose of the Study:

  • To investigate the application of ML methods for predicting childbirth risks and due dates.
  • To develop and validate interpretable ML models using real-world perinatal data.
  • To assess the potential of ML-driven CDSSs in enhancing perinatal care.

Main Methods:

  • Retrospective analysis of electronic health records from 12,989 female patients at the Almazov Specialized Medical Center.
  • Utilized structured and semi-structured data comprising 73,115 records.
  • Developed and evaluated predictive models focusing on performance and clinical interpretability.

Main Results:

  • Achieved high predictive performance in models designed for childbirth risk and due date prediction.
  • Demonstrated the clinical interpretability of the developed ML models.
  • Validated the effectiveness of ML in supporting perinatal care decisions.

Conclusions:

  • ML methods provide powerful tools for data-driven decision support in perinatal care.
  • Accurate and interpretable ML models can significantly enhance individual patient management and health system organization.
  • This approach offers opportunities for improved risk management, mitigation, and prevention in pregnancy and childbirth.