Cargando…

The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records

BACKGROUND: Electronic health records are invaluable for medical research, but much information is stored as free text rather than in a coded form. For example, in the UK General Practice Research Database (GPRD), causes of death and test results are sometimes recorded only in free text. Free text c...

Descripción completa

Detalles Bibliográficos
Autores principales:	Shah, Anoop D, Martinez, Carlos, Hemingway, Harry
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2012
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3483188/ https://www.ncbi.nlm.nih.gov/pubmed/22870911 http://dx.doi.org/10.1186/1472-6947-12-88

Ejemplares similares

Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic Health Records by Semi-Supervised Machine Learning
por: Wang, Zhuoran, et al.
Publicado: (2012)

Learning Signals of Adverse Drug-Drug Interactions from the Unstructured Text of Electronic Health Records
por: Iyer, Srinivasan V, et al.
Publicado: ( 201)

Learning Signals of Adverse Drug-Drug Interactions from the Unstructured Text of Electronic Health Records
por: Iyer, Srinivasan V, et al.
Publicado: (2013)

Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression
por: Geraci, Joseph, et al.
Publicado: (2017)

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method
por: Bright, Roselie A, et al.
Publicado: (2021)

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study
por: Kosowan, Leanne, et al.
Publicado: (2022)

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records
por: Baughman, Douglas M., et al.
Publicado: (2017)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
por: Yu, Haiyan
Publicado: (2021)

Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
por: Antoniou, Mark
Publicado: (2021)

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records
por: Assale, Michela, et al.
Publicado: (2019)

Authors’ Response to Peer Reviews of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”
por: Bright, Roselie A, et al.
Publicado: (2021)

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records
por: González-Castro, Lorena, et al.
Publicado: (2023)

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records
por: Brouwer, Emily S., et al.
Publicado: (2021)

Learning the Structure of Biomedical Relationships from Unstructured Text
por: Percha, Bethany, et al.
Publicado: (2015)

Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data
por: Sun, Boguang, et al.
Publicado: (2023)

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data
por: Sun, Boguang, et al.
Publicado: (2023)

Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death
por: Shah, Anoop D., et al.
Publicado: (2019)

FasTag: Automatic text classification of unstructured medical narratives
por: Venkataraman, Guhan Ram, et al.
Publicado: (2020)

Bleeding in cardiac patients prescribed antithrombotic drugs: electronic health record phenotyping algorithms, incidence, trends and prognosis
por: Pasea, Laura, et al.
Publicado: (2019)

Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data
por: Bustamante, Ranier, et al.
Publicado: (2019)

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records
por: Shao, Yijun, et al.
Publicado: (2019)

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records
por: Wang, Mengying, et al.
Publicado: (2022)

Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data
por: Nagamine, Tasha, et al.
Publicado: (2020)

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
por: Steele, Andrew J., et al.
Publicado: (2018)

White cell count in the normal range and short-term and long-term mortality: international comparisons of electronic health record cohorts in England and New Zealand
por: Shah, Anoop Dinesh, et al.
Publicado: (2017)

Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts
por: Wang, Yanshan, et al.
Publicado: (2017)

Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits †
por: Mattfeld, Ryan, et al.
Publicado: (2021)

Use of unstructured text in prognostic clinical prediction models: a systematic review
por: Seinen, Tom M, et al.
Publicado: (2022)

A configurable software platform for creating, reviewing and adjudicating annotation of unstructured text.
por: Beare, Richard, et al.
Publicado: (2022)

Using electronic health records to predict costs and outcomes in stable coronary artery disease
por: Asaria, Miqdad, et al.
Publicado: (2016)

Ethnicity and the first diagnosis of a wide range of cardiovascular diseases: Associations in a linked electronic health record cohort of 1 million patients
por: George, Julie, et al.
Publicado: (2017)

Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes
por: Krishnan, Gokul S., et al.
Publicado: (2020)

MLM-based typographical error correction of unstructured medical texts for named entity recognition
por: Lee, Eun Byul, et al.
Publicado: (2022)

A scoping review of preprocessing methods for unstructured text data to assess data quality
por: Nesca, Marcello, et al.
Publicado: (2022)

Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis
por: Banerjee, Amitava, et al.
Publicado: (2019)

Automated Extraction and Classification of Cancer Stage Mentions fromUnstructured Text Fields in a Central Cancer Registry
por: AAlAbdulsalam, Abdulrahman K., et al.
Publicado: (2018)

Identifying individual social risk factors using unstructured data in electronic health records and their relationship with adverse clinical outcomes
por: Rikard, S. Michaela, et al.
Publicado: (2022)

Data Resource Profile: Cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER)
por: Denaxas, Spiros C, et al.
Publicado: (2012)

Cannot write session to /tmp/vufind_sessions/sess_6q20cc31fv4rncu49hnpbqdfl8