ArticleFrontiers in psychiatry2020
Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05109845 (Virtual Reality and Speech Analysis for the Assessment of Impulsivity, Decision-making and Jumping to Conclusions), which is not on this map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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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.
Virtual Reality and Speech Analysis for the Assessment of Impulsivity, Decision-making and Jumping to Conclusions: Comparison With Neuropsychological Tasks and Self-administered Questionnaires
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Text-Based Depression Estimation Using Machine Learning With Standard Labels: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Assisting patients with reliable information gathering: A guide to building efficient QA systems for medical use.PLOS digital health · 2026Article
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Large Language Models for Cardiovascular Disease, Cancer, and Mental Disorders: A Review of Systematic Reviews.Healthcare (Basel, Switzerland) · 2025Review
- AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System.MIS quarterly : management information systems · 2025Article
- The Applications of Large Language Models in Mental Health: Scoping Review.Journal of medical Internet research · 2025Article
- Predicting adolescent depressive symptoms using teacher-reported textual descriptions of abnormal behaviors: a study based on machine learning.Frontiers in artificial intelligence · 2025Article
- Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.Computational and structural biotechnology journal · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.Journal of medical Internet research · 2024Article
- Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model.Psychiatry and clinical neurosciences · 2023Article
- RECAP-KG: Mining Knowledge Graphs from Raw Primary Care Physician Notes for Remote COVID-19 Assessment in Primary Care.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression.PloS one · 2023Article
- A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster.International journal of environmental research and public health · 2022Article
- Natural Language Processing Methods and Bipolar Disorder: Scoping Review.JMIR mental health · 2022Article
- Transfer learning for non-image data in clinical research: A scoping review.PLOS digital health · 2022Article
- Natural Language Processing as an Emerging Tool to Detect Late-Life Depression.Frontiers in psychiatry · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The introduction of pre-trained language models in natural language processing (NLP) based on deep learning and the availability of electronic health records (EHRs) presents a great opportunity to transfer the "knowledge" learned from data in the general domain to enable the analysis of unstructured textual data in clinical domains. This study explored the feasibility of applying NLP to a small EHR dataset to investigate the power of transfer learning to facilitate the process of patient screening in psychiatry. A total of 500 patients were randomly selected from a medical center database. Three annotators with clinical experience reviewed the notes to make diagnoses for major/minor depression, bipolar disorder, schizophrenia, and dementia to form a small and highly imbalanced corpus. Several state-of-the-art NLP methods based on deep learning along with pre-trained models based on shallow or deep transfer learning were adapted to develop models to classify the aforementioned diseases. We hypothesized that the models that rely on transferred knowledge would be expected to outperform the models learned from scratch. The experimental results demonstrated that the models with the pre-trained techniques outperformed the models without transferred knowledge by micro-avg. and macro-avg. F-scores of 0.11 and 0.28, respectively. Our results also suggested that the use of the feature dependency strategy to build multi-labeling models instead of problem transformation is superior considering its higher performance and simplicity in the training process.
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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.