Master Assignment
Linking clinical codes to Dutch medical text
Type: Master CS
Location: CTcue
Period: TBD
Student: (Unassigned)
If you are interested please contact :
Introduction of company:
CTcue is a small company that builds a search engine for patient populations and provides an easy way to collect data about that population. Some examples for use cases for hospitals are clinical trials and Quality indicators (statistics about hospital performance for insurance companies, government etc.). A major challenge is that valuable information is archived as text (e.g. reports or notes), making it unavailable for analysis without using natural language processing. We have created a pipeline that analyses dutch medical text, such that the full scope of patient health records can be used, both structured and unstructured data. The steps in the pipeline include (but are not limited to): measurement extraction, concept extraction, context classification, temporal classification. Currently our solution is implemented in 25 hospitals. The company consists of a team of 14 people and is located in Amsterdam on Science Park.
Project description:
Linking codes from medical ontologies normalizes the medical concepts mentioned in text. This can improve the quality of our search results and make queries more exchangeable between hospitals. The ontology that we would like to link to text concepts for this project is ICD10, given the availability of data. ICD10 is a hierarchical categorization of disorders.
Expected product:
A python module that takes a tokenized text with tagged concepts and then classifies the ICD10 codes that are present in the medical text and links them to the related concepts.
Available resources:
A number of resources such as concept detection and context classification are available, as well as codes linked on a patient level for training and testing.