ReviewAdvances in experimental medicine and biology2016
Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.
Review in Advances in experimental medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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
26 citing papers in PubMed, 64 citations in OpenAlex.
- Trial
- Personalized insights into liver disease management: a text mining analysis of online consultation data.Frontiers in public health · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- A simplified retriever to improve accuracy of phenotype normalizations by large language models.Frontiers in digital health · 2025Article
- Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among people with HIV.AIDS (London, England) · 2024Article
- The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.Database : the journal of biological databases and curation · 2024Article
- Exposure to angiotensin-converting enzyme inhibitors that cross the blood-brain barrier and the risk of dementia among patients with human immunodeficiency virus.medRxiv : the preprint server for health sciences · 2024Article
- Clinical Prediction Models for Hospital-Induced Delirium Using Structured and Unstructured Electronic Health Record Data: Protocol for a Development and Validation Study.JMIR research protocols · 2023Article
- Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation.BMJ mental health · 2023Article
- Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.Life (Basel, Switzerland) · 2022Review
- Exploring the Association of Cancer and Depression in Electronic Health Records: Combining Encoded Diagnosis and Mining Free-Text Clinical Notes.JMIR cancer · 2022Article
- Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.BMC medical informatics and decision making · 2022Article
- Diseases 2.0: a weekly updated database of disease-gene associations from text mining and data integration.Database : the journal of biological databases and curation · 2022Article
- Macrophage Identification In Situ.Biomedicines · 2021Review
- Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts.JMIR medical informatics · 2021Article
- Biomedical document triage using a hierarchical attention-based capsule network.BMC bioinformatics · 2020Article
- A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Drug Abuse Research Trend Investigation with Text Mining.Computational and mathematical methods in medicine · 2020Article
- Impact of Automatic Query Generation and Quality Recognition Using Deep Learning to Curate Evidence From Biomedical Literature: Empirical Study.JMIR medical informatics · 2019Article
- Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning.JMIR medical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.
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.