ArticleFrontiers in artificial intelligence2019
Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression.
Article in Frontiers in artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed, 43 citations in OpenAlex.
- Major Depressive Disorder Signatures: A Review of Artificial Intelligence (AI)-Powered Insights Into Gut Dysbiosis.Health science reports · 2026Article
- Phenotyping antidepressant treatment response with deep learning in electronic health records.Translational psychiatry · 2026Article
- Development of the treatment prediction model in the artificial intelligence in depression - medication enhancement study.Npj mental health research · 2025Article
- Identifying risk factors for depression and positive/negative mood changes in college students using machine learning.Frontiers in public health · 2025Article
- Models for depression recognition and efficacy assessment based on clinical and sequencing data.Heliyon · 2024Article
- Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.Translational psychiatry · 2024Article
- Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.Physical and engineering sciences in medicine · 2024Article
- Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention.Journal of affective disorders · 2024Observational
- Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors.Frontiers in psychiatry · 2024Article
- Editorial: Methods and interventions to predict and tackle suicide risk.Frontiers in psychiatry · 2024Article
- Review
- Barriers and solutions to the adoption of translational tools for computational psychiatry.Molecular psychiatry · 2023Review
- AI-assisted prediction of differential response to antidepressant classes using electronic health records.NPJ digital medicine · 2023Article
- Taking modern psychiatry into the metaverse: Integrating augmented, virtual, and mixed reality technologies into psychiatric care.Frontiers in digital health · 2023Article
- A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process.PharmacoEconomics · 2022Review
- Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study.JMIR formative research · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.