SynthesisArtificial intelligence in medicine2023
Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.
Synthesis in Artificial intelligence in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 3 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Applications of natural language processing and large language models in sports injury assessment and rehabilitation decision-making: a scoping review.Frontiers in medicine · 2026Pooled it
- AAV Gene Therapy Drug Development and Translation of Engineered Ocular and Neurotropic Capsids: A Systematic Review Using Natural Language Processing.Clinical and translational science · 2025Pooled it
- Advancements in Herpes Zoster Diagnosis, Treatment, and Management: Systematic Review of Artificial Intelligence Applications.Journal of medical Internet research · 2025Pooled it
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.JMIR medical informatics · 2026Article
- The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.Maternal and child health journal · 2026Observational
- Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.Prostate cancer and prostatic diseases · 2026Article
- Review
- Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.Communications medicine · 2026Article
- Enhancing bone metastasis CT report analysis: a comparison of local and proprietary large language models for privacy and resource efficiency.BMC health services research · 2026Article
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- Threading the Needle: Practical Considerations for Merging Theory-Driven Computational Psychiatry With Data-Driven Analytics to Enhance Precision Health at Scale.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- TheraMind: a multi-LLM ensemble for accelerating drug repurposing in lung cancer via case report mining.NPJ precision oncology · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Pharmacovigilance in Cell and Gene Therapy: Evolving Challenges in Risk Management and Long-Term Follow-Up.Drug safety · 2026Review
- Integrating patient-reported weight gain cause narratives into personalized obesity management: a data-driven approach with natural language processing and machine learning.Frontiers in nutrition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveNatural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.
methodsWe searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.
resultsMost of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.
conclusionsThis study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
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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.