SynthesisExpert review of pharmacoeconomics & outcomes research2024
Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.
Synthesis in Expert review of pharmacoeconomics & outcomes research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Electronic health records: managerial insights from an umbrella review.BMC health services research · 2026Pooled it
- Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.Journal of patient-reported outcomes · 2024Pooled it
- Harnessing Artificial Intelligence in Health Research in Low-Income and Middle-Income Countries: Potential and Caution.Mayo Clinic proceedings. Digital health · 2026Review
- AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.JMIR formative research · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Optimizing prompting strategies improves large language model classification of pain- and fatigue-related functional impact in childhood cancer survivors.Communications medicine · 2026Article
- Patient Voice and Treatment Nonadherence in Cancer Care: A Scoping Review of Sentiment Analysis.Nursing reports (Pavia, Italy) · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Review
- Review
- Article
- Identifying Patient-Reported Outcome Measure Documentation in Veterans Health Administration Chiropractic Clinic Notes: Natural Language Processing Analysis.JMIR medical informatics · 2025Article
- Enhancing the Utility of Health Related Quality of Life (HRQoL) Assessment Tools in Abdominal Wall Hernia (AWH) Surgery Through Artificial Intelligence (AI): A Framework Proposal.Journal of abdominal wall surgery : JAWS · 2025Article
- Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan.Healthcare informatics research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
introductionPatient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. AREAS COVERED: This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. EXPERT OPINION: Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.
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.