Tyko Niemi


Clinical Data Classification using Conditional Random Fields and Neural Parsing for Morphologically Rich Languages
Razieh Ehsani | Tyko Niemi | Gaurav Khullar | Tiina Leivo
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Past prescriptions constitute a central element in patient records. These are often written in an unstructured and brief form. Extracting information from such prescriptions enables the development of automated processes in the medical data mining field. This paper presents a Conditional Random Fields (CRFs) based approach to extract relevant information from prescriptions. We focus on Finnish language prescriptions and make use of Finnish language specific features. Our labeling accuracy is 95%, which compares favorably to the current state-of-the-art in English language prescriptions. This, to the best of our knowledge, is the first such work for the Finnish language.