Methodical Use Case PheP

Through the Medical Informatics Initiative (MII) and the development of the Data Integration Centers (DIC), clinical health-care data from various sources of the Hospital Information System (HIS) are made available for medical research. This creates a unique and rich repository of clinical data that is precisely defined across all participating sites. With the methodical Use Case Phenotyping Pipeline, PheP for short, the SMITH Consortium supports the construction, qualitative enrichment and evaluation of the data stock. The University of Leipzig is in charge of the project.

PheP is a platform that enables clinical researchers to work together with statisticians and computer scientists in interdisciplinary collaboration to pursue scientific issues that previously seemed economically and technologically unthinkable. For this purpose, it is necessary to build data sets that can be used for clinical-epidemiological and health-economic issues.From phenotypes, i.e. determinable characteristics of patients, further characteristics can be derived and provided via phenotyping. PheP also supports the record linkage procedure, which is used to combine data on a patient from different information sources, for example from health insurance companies or death data from civil registers.Through the Medical Informatics Initiative (MII) and the development of the Data Integration Centers (DIC), clinical health-care data from various sources of the Hospital Information System (HIS) are made available for medical research. This creates a unique and rich repository of clinical data that is precisely defined across all participating sites. With the methodical Use Case Phenotyping Pipeline, PheP for short, the SMITH Consortium supports the construction, qualitative enrichment and evaluation of the data stock. The University of Leipzig is in charge of the project.One of the challenges in this context is that too little clinical information is available as machine-readable data sets. Admission letters, findings and operating room reports in particular contain valuable information such as diagnoses, medications, side effects and laboratory data that can only be extracted using methods of natural language processing and semantic text analysis methods. Natural Language Processing (NLP) is used to process documents from the Hospital Information System (HIS). The process is academically led by the Jena University Language & Information Engineering Lab (JULIE Lab) in collaboration with leading companies in the field of language processing.