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

Purpose of Health Records I01:11

Purpose of Health Records I

1.7K
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
1.7K
Purpose of Health Records II01:19

Purpose of Health Records II

1.4K
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
1.4K
Long-term Depression01:05

Long-term Depression

33.2K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
33.2K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.4K
VSEPR Theory for Determination of Electron Pair Geometries
45.4K
Freezing Point Depression and Boiling Point Elevation03:12

Freezing Point Depression and Boiling Point Elevation

39.6K
Boiling Point Elevation
The boiling point of a liquid is the temperature at which its vapor pressure is equal to ambient atmospheric pressure. Since the vapor pressure of a solution is lowered due to the presence of nonvolatile solutes, it stands to reason that the solution’s boiling point will subsequently be increased. Vapor pressure increases with temperature, and so a solution will require a higher temperature than will pure solvent to achieve any given vapor pressure, including one...
39.6K
Depressants01:28

Depressants

384
Depressant drugs, including alcohol and sedative-hypnotics, diminish central nervous system activity by enhancing the action of gamma-aminobutyric acid (GABA), a neurotransmitter that reduces brain activity and promotes relaxation. These substances can have various therapeutic uses but also pose significant risks, especially when misused or combined.
Alcohol is a common depressant that can induce a sense of relaxation and reduced inhibition at low doses. Contrary to its occasional...
384

You might also read

Related Articles

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

Sort by
Same author

Reply to: Reassessing the evidence linking clinical leadership to AI deployment outcomes.

NPJ digital medicine·2026
Same author

Role of electronic health record in stigma experiences, trust, and care decisions by women with history of pregnancy and substance use disorder: a qualitative study of perceptions of clinical note language.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.

Research square·2026
Same author

Outcomes of 72-hour emergency department return visits requiring hospital admission in older adults: a nationally representative analysis.

BMC geriatrics·2026
Same author

Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial.

BMJ open·2026
Same author

A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems.

NPJ digital medicine·2026

Related Experiment Video

Updated: Jan 20, 2026

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents
07:30

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Published on: June 10, 2013

25.5K

Using Electronic Health Records and Machine Learning to Predict Postpartum Depression.

Shuojia Wang1,2, Jyotishman Pathak1, Yiye Zhang1

  • 1Weill Cornell Medicine, Cornell University, New York, New York, USA.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary

Researchers developed a machine learning model using electronic health records to predict postpartum depression (PPD). The model identified key risk factors, offering potential for early intervention and improved maternal healthcare.

Keywords:
DepressionElectronic Health RecordsMachine LearningPostpartum

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

7.5K
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

8.7K

Related Experiment Videos

Last Updated: Jan 20, 2026

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents
07:30

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Published on: June 10, 2013

25.5K
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

7.5K
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

8.7K

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Reproductive Health

Background:

  • Postpartum depression (PPD) is a common maternal morbidity with significant health implications.
  • Current screening methods for PPD are insufficient, highlighting a need for improved detection strategies.
  • Electronic health records (EHRs) offer a rich data source for developing predictive models.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting postpartum depression (PPD) using EHR data.
  • To identify significant predictors of PPD from a comprehensive set of patient information.
  • To assess the feasibility of integrating predictive models into clinical decision support systems.

Main Methods:

  • Utilized EHR data from 9,980 pregnancy episodes (2015-2017) from Weill Cornell Medicine and NewYork-Presbyterian Hospital.
  • Constructed and compared six machine learning algorithms: Logistic Regression, Support Vector Machine, Decision Tree, Naïve Bayes, XGBoost, and Random Forest.
  • Evaluated model performance using Area Under the Curve (AUC).

Main Results:

  • The best performing model achieved an AUC of 0.79.
  • Significant predictors of PPD included race, obesity, anxiety, depression, pain, and the use of antidepressants and anti-inflammatory drugs during pregnancy.
  • The study demonstrated the potential of machine learning in predicting PPD.

Conclusions:

  • Machine learning models applied to EHR data show promise for predicting postpartum depression.
  • Identifying key risk factors can inform targeted interventions and improve maternal health outcomes.
  • This approach could enhance clinical decision support for PPD management.