ArticleBMC medical informatics and decision making2019
Developing a portable natural language processing based phenotyping system.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 46 citations in OpenAlex.
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
- Experiences With Integrating Medical Terminologies Into User Interfaces for a Decision Support System for Primary Care: Conceptual and Development Study.JMIR medical informatics · 2026Article
- PheNormGPT: a framework for extraction and normalization of key medical findings.Database : the journal of biological databases and curation · 2024Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.NPJ digital medicine · 2023Article
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- Enhancing an enterprise data warehouse for research with data extracted using natural language processing.Journal of clinical and translational science · 2023Article
- Design and validation of a FHIR-based EHR-driven phenotyping toolbox.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers.JMIR formative research · 2022Article
- Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data.Yonsei medical journal · 2022Article
- Data Consult Service: Can we use observational data to address immediate clinical needs?Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The Role of Electronic Health Records in Advancing Genomic Medicine.Annual review of genomics and human genetics · 2021Review
- Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI.Yearbook of medical informatics · 2021Article
- A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study.JMIR medical informatics · 2021Article
- Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.Journal of medical Internet research · 2020Article
- Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.Learning health systems · 2020Article
- Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case.Learning health systems · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundThis paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.
methodsOur system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating OHDSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e.g., regular expression matches) in the format of OMOP CDM, thus enabling the reuse, adaptation and extension of many existing rule-based clinical NLP systems. We experimented with our system on the corpus from i2b2's Obesity Challenge as a pilot study.
resultsOur system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
conclusionOur system of standardization enables a consistent application of numerous rule-based and machine learning based classification techniques downstream across disparate datasets which may originate across different institutions and data systems.
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.