The NLP4CR project was initiated as part of Colleen Goldberg's master thesis. Medical information is not always available in a structured form, but as free text documentation. NLP techniques make it possible to analyse, annotate and extract these free texts. In this way, medical data can be made accessible for machine processing and for clinical research.
Programme: This Project is not associated with a Programme
BACKGROUND: Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts … in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. OBJECTIVES: This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals. METHOD: Based on the inclusion/exclusion criteria of 5 sample studies and a text corpus consisting of 212 doctor's letters and medical follow-up documentation from a university cancer center, a prototype was developed and technically evaluated using NLP procedures (UIMA) for the extraction of facts from medical free texts. RESULTS: It was found that although the extracted entities are not always correct (precision between 23% and 96%), they provide a decisive indication as to which patient file should be read preferentially. CONCLUSION: The prototype presented here demonstrates the technical feasibility. In order to find available, lucrative phenotypes, an in-depth evaluation is required.
The Leipzig Health Atlas is an alliance of medical ontologists, medical systems
biologists and clinical trials groups to design and implement a multi-functional and quality-assured
atlas. It provides models, data and metadata on specific use cases from medical research fields.