ArticleEvidence-based mental health2020
Natural language processing for structuring clinical text data on depression using UK-CRIS.
Article in Evidence-based mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Natural Language Response Formats for Assessing Depression and Worry With Large Language Models: A Sequential Evaluation With Model Pre-Registration.Assessment · 2026Article
- Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.Interactive journal of medical research · 2026Article
- Identifying postpartum depression subtypes using natural language processing and clinical notes.BMJ mental health · 2026Article
- AI assisted triage of UK patients in mental health care services: a qualitative focus group study of patients' attitudes.BMC psychiatry · 2026Article
- Brain morphological characteristics predicting clinical response to selective serotonin reuptake inhibitors or cholinesterase inhibitors: A study of electronic medical records in patients with cognitive disorders.International psychogeriatrics · 2025Observational
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.BMC medical informatics and decision making · 2025Article
- Annotation of epilepsy clinic letters for natural language processing.Journal of biomedical semantics · 2024Article
- Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records.PloS one · 2024Article
- Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text.Methods of information in medicine · 2023Article
- Artificial intelligence for dementia-Applied models and digital health.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Artificial intelligence for dementia prevention.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Few-shot learning for medical text: A review of advances, trends, and opportunities.Journal of biomedical informatics · 2023Review
- Translating clinical notes into quantitative measures-a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records.Frontiers in pharmacology · 2023Article
- Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.Wireless personal communications · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundUtilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information.
objectiveOur aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research.
methodsWe used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures.
findingsResults show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably.
conclusionsThis study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. CLINICAL IMPLICATIONS: Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.
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