Article

Developing and Testing a Framework for Coding General Practitioners' Free-Text Diagnoses in Electronic Medical Records - A Reliability Study for Generating Training Data in Natural Language Processing

2024-04-12

Abstract excerpt

<title>Abstract</title> <p><bold>Background</bold> Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim o...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3305fc7f-f80e-55de-8ecb-44afacf8da61
DOI: 10.21203/rs.3.rs-4131283/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Developing and Testing a Framework for Coding General Practitioners' Free-Text Diagnoses in Electronic Medical Records - A Reliability Study for Generating Training Data in Natural Language ProcessingDOI 10.21203/rs.3.rs-4131283/v1Read

Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR dataSemantic proximity Read

Automating Clinical Phenotyping Using Natural Language Processing: An Application for Crohn’s DiseaseSemantic proximity Read

The Transformative Potential of Large Language Models in Mining Electronic Health Records DataSemantic proximity Read

Large language models for accurate disease detection in electronic health recordsSemantic proximity Read

Automated Derivation of Diagnostic Criteria for Lung Cancer using Natural Language Processing on Electronic Health Records: A pilot studySemantic proximity Read

Developing an open-source framework for LLM evaluation of patients using EHR clinical documentation; performance of LLMs relative to medical professionalsSemantic proximity Read

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read

Large language models for automatable real-world performance monitoring of diagnostic decision support systems: a comparison to manual doctor panel review in a prospective clinical studySemantic proximity Read

Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.Semantic proximity Read

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read

On the limitations of large language models in clinical diagnosisSemantic proximity Read