SynthesisJournal of the American Medical Informatics Association : JAMIA2022
Use of unstructured text in prognostic clinical prediction models: a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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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.
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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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.Journal of magnetic resonance imaging : JMRI · 2026Article
- SCRIPT: Stratified clinical risk prediction from pathology reports using large language models.Journal of pathology informatics · 2026Article
- Clinical pathways matter for multimodal deep learning in early Alzheimer's disease detection.Scientific reports · 2026Article
- Human-AI co-design for clinical prediction models.NPJ digital medicine · 2026Article
- Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents.ESMO real world data and digital oncology · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning, Clinical Summary Notes, and Vital Signs: A Single-Center Retrospective Cohort Study in the United States.Advances in respiratory medicine · 2026Article
- Comparison of local large language models for extraction of signs and symptoms data from electronic health records.PloS one · 2026Article
- Early prediction of postoperative delirium in major surgery patients using multimodal preoperative EHR and medical notes: a retrospective cohort study in the Indiana network for patient care.BMJ public health · 2026Article
- Explaining alerts from a pediatric risk prediction model using clinical text.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Evaluation of changes in prediction modelling in biomedicine using systematic reviews.BMC medical research methodology · 2025Article
- Big Data-Driven Health Portraits for Personalized Management in Noncommunicable Diseases: Scoping Review.Journal of medical Internet research · 2025Article
- Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study.JMIR medical informatics · 2025Article
- Article
- Natural language processing in critical care: opportunities, challenges, and future directions.Intensive care medicine · 2025Article
- Identifying Patient-Reported Care Experiences in Free-Text Survey Comments: Topic Modeling Study.JMIR medical informatics · 2025Article
- Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study.Journal of medical Internet research · 2025Article
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveThis systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance. MATERIALS AND
methodsWe searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.
resultsWe identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited.
conclusionThe use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.
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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.