Cargando…

Estimation of postpartum depression risk from electronic health records using machine learning

BACKGROUND: Postpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential v...

Descripción completa

Detalles Bibliográficos
Autores principales:	Amit, Guy, Girshovitz, Irena, Marcus, Karni, Zhang, Yiye, Pathak, Jyotishman, Bar, Vered, Akiva, Pinchas
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2021
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447665/ https://www.ncbi.nlm.nih.gov/pubmed/34535116 http://dx.doi.org/10.1186/s12884-021-04087-8

Ejemplares similares

Estimating the effect of cesarean delivery on long-term childhood health across two countries
por: Keshet, Ayya, et al.
Publicado: (2022)

Subphenotyping depression using machine learning and electronic health records
por: Xu, Zhenxing, et al.
Publicado: (2020)

Bias in Recording of Body Mass Index Data in the Electronic Health Record
por: Rea, Susan, et al.
Publicado: ( 201)

Electronic health records and blockchain interoperability requirements: a scoping review
por: Schmeelk, Suzanna, et al.
Publicado: (2022)

Automated classification of lay health articles using natural language processing: a case study on pregnancy health and postpartum depression
por: Patra, Braja Gopal, et al.
Publicado: (2023)

Using Linked Data for Mining Drug-Drug Interactions in Electronic Health Records
por: Pathak, Jyotishman, et al.
Publicado: (2013)

Using Association Rule Mining for Phenotype Extraction from Electronic Health Records
por: Li, Dingcheng, et al.
Publicado: ( 201)

Using Semantic Web Technologies for Cohort Identification from Electronic Health Records for Clinical Research
por: Pathak, Jyotishman, et al.
Publicado: (2012)

What Factors Increase the Risk of Complications in SARS-CoV-2–Infected Patients? A Cohort Study in a Nationwide Israeli Health Organization
por: Yanover, Chen, et al.
Publicado: (2020)

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records
por: Xu, Jie, et al.
Publicado: (2020)

Using electronic health records to characterize prescription patterns: focus on antidepressants in nonpsychiatric outpatient settings
por: Deferio, Joseph J, et al.
Publicado: (2018)

Detecting Associations between Major Depressive Disorder Treatment and Essential Hypertension using Electronic Health Records
por: Pathak, Jyotishman, et al.
Publicado: (2014)

Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records
por: Ryu, Euijung, et al.
Publicado: (2016)

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment
por: Zhang, Yiye, et al.
Publicado: (2020)

Bi-directional association between depression and HF: An electronic health records-based cohort study
por: Bobo, William V, et al.
Publicado: (2020)

Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank
por: Pathak, Jyotishman, et al.
Publicado: (2012)

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution
por: Breitenstein, Matthew K., et al.
Publicado: (2018)

Leveraging an Electronic Health Record-Linked Biorepository to Generate a Metformin Pharmacogenomics Hypothesis
por: Breitenstein, Matthew K., et al.
Publicado: (2015)

Portability of natural language processing methods to detect suicidality from clinical text in US and UK electronic health records
por: Cusick, Marika, et al.
Publicado: (2022)

Identification of Postpartum Depression in Electronic Health Records: Validation in a Large Integrated Health Care System
por: Slezak, Jeff, et al.
Publicado: (2023)

A Modular Architecture for Electronic Health Record-Driven Phenotyping
por: Rasmussen, Luke V., et al.
Publicado: (2015)

Machine Learning-Based Predictive Modeling of Postpartum Depression
por: Shin, Dayeon, et al.
Publicado: (2020)

An electronic health record driven algorithm to identify incident antidepressant medication users
por: Bobo, William V, et al.
Publicado: (2014)

Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum
por: Wakefield, Colin, et al.
Publicado: (2023)

Identifying Diagnostic Paths for Undifferentiated Abdominal Pain from Electronic Health Record Data
por: Zhang, Yiye, et al.
Publicado: (2018)

Health behaviors and quality of life predictors for risk of hospitalization in an electronic health record-linked biobank
por: Takahashi, Paul Y, et al.
Publicado: (2015)

The ATXN2-SH2B3 locus is associated with peripheral arterial disease: an electronic medical record-based genome-wide association study
por: Kullo, Iftikhar J., et al.
Publicado: (2014)

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study
por: Adekkanattu, Prakash, et al.
Publicado: (2023)

Predicting future onset of depression among middle-aged adults with no psychiatric history
por: Bilu, Yonatan, et al.
Publicado: (2023)

Predicting women with depressive symptoms postpartum with machine learning methods
por: Andersson, Sam, et al.
Publicado: (2021)

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review
por: Saqib, Kiran, et al.
Publicado: (2021)

Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records
por: Zheutlin, Amanda B, et al.
Publicado: (2021)

A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation
por: Bilu, Yonatan, et al.
Publicado: (2023)

Toward cross‐platform electronic health record‐driven phenotyping using Clinical Quality Language
por: Brandt, Pascal S., et al.
Publicado: (2020)

Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility
por: Upadhyaya, Sudhi G., et al.
Publicado: (2017)

Changes in Depression and Anxiety Symptoms during Antidepressant Treatment in Adults
por: Zhu, Zhenxing, et al.
Publicado: (2022)

Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization
por: Girshovitz, Pinhas, et al.
Publicado: (2012)

Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer
por: Kinar, Yaron, et al.
Publicado: (2017)

Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review
por: Nickson, David, et al.
Publicado: (2023)

Effects of Plasma Transfusion on Perioperative Bleeding Complications: A Machine Learning Approach
por: Ngufor, Che, et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_l9k6ndhpqrc4fs6enn0lqt2lkc