ArticleProcedia computer science2022
Identification of maternal depression risk from natural language collected in a mobile health app.
Article in Procedia computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Language-based detection of depression with machine learning: systematic review and meta-analysis.NPJ digital medicine · 2026Article
- AI for Detecting and Predicting Postpartum Depression: Scoping Review.Journal of medical Internet research · 2026Article
- Language-Based Detection of Depression with Machine Learning: Systematic Review and Meta- Analysis.Research square · 2025Article
- A novel spatiotemporal model with advanced feature extraction and unified brain network for depression detection using electroencephalogram signals.Royal Society open science · 2025Article
- Incorporating end-user perspectives into the development of a machine learning algorithm for first time perinatal depression prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.Journal of affective disorders · 2025Article
- Integrating Infant Safe Sleep and Breastfeeding Education Into an App in a Novel Approach to Reaching High-Risk Populations: Prospective Observational Study.JMIR pediatrics and parenting · 2025Article
- Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.Archives of women's mental health · 2024Observational
- Digital phenotyping of depression during pregnancy using self-report data.Journal of affective disorders · 2024Observational
- Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure.Frontiers in psychology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Depression is one of the most common pregnancy complications, affecting approximately 15% of pregnant people. While valid psychometric measures of depression risk exist, they are not consistently administered at routine prenatal care, exacerbating the problem of adequate detection. The language we use in daily life offers a window into our psychological wellbeing. In this longitudinal observational cohort study of prenatal patients using a prenatal care mobile health app, we examine how features of app-entered natural language and other app-entered patient-reported data may be used as indicators for validated depression risk measures. Patient participants (n=1091) were prescribed a prenatal care app as part of a quality improvement initiative in the UPMC healthcare system from September 2019 - May 2022. Natural language from open-ended writing prompts in the app and self-reported daily mood, were entered by patients using the tool. Participants also completed a validated measure of depression risk - the Edinburgh Postnatal Depression Scale (EPDS) - at least once in their pregnancy. A variety of natural language processing tools were used to score sentiment, categorize topics, and capture other semantic and syntactic information from text entries. LASSO was used to model the relationship between the natural language features and depression risk. Open-ended text within a 30-day and 60-day timeframe of completing an EPDS was found to be moderately predictive of moderate to severe depression risk (AUROC=0.66 and 0.67, for each respective timeframe). When combined with average daily reported mood, open-ended text showed good predictive power (AUROC=0.87). Consistently predictive language features across all models included themes of "money" and "sadness." The combination of natural language and other user-reported data collected through a mobile health app offers an opportunity for identifying depression risk among a pregnant population.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.