ArticleScientific reports2017
Analysis of free text in electronic health records for identification of cancer patient trajectories.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 5 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
52 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.Expert review of pharmacoeconomics & outcomes research · 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
- Clinical and operational insights from data-driven care pathway mapping: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Generalizable multilingual medical text anonymization using generative instruction tuning.Communications medicine · 2026Article
- Data harmonization processes of cancer data into the observational medical outcomes partnership common data model.Scientific reports · 2026Article
- Multimodal data for predictive medicine: algorithmic fusion of clinical data in anesthesiology and intensive care.Frontiers in medicine · 2026Article
- IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2025Article
- Automated extraction of functional biomarkers of verbal and ambulatory ability from multi-institutional clinical notes using large language models.Journal of neurodevelopmental disorders · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Mobile App-Based Interactive Care Plan for Migraine: Survey Study of Usability and Improvement Opportunities.JMIR formative research · 2025Article
- Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.JAMIA open · 2025Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A comparison of two user-friendly methods to identify and support correction of misspelled medications.Preventive medicine reports · 2024Article
- Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.JAMIA open · 2024Article
- Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.JCO clinical cancer informatics · 2024Article
- Identification and Characterization of Immune Checkpoint Inhibitor-Induced Toxicities From Electronic Health Records Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.Journal of intensive medicine · 2024Review
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004-2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.
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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.