Classifying Short Clinical Notes: An Unsupervised Approach
Typ
Examensarbete för masterexamen
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2020
Författare
CHEN TRIEU, Kevin
NGUYEN, Long
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A mandatory task in Sweden is the reporting of clinical procedures with a specially
assigned code based on the procedure. It is both time-consuming and troublesome
for medical personnel since more than 10,000 codes exist. By automating this task,
it is possible to both save time of the personnel and money within the healthcare
industry.
This master thesis explores an alternative way of classifying short clinical notes
through unsupervised methods when quality labelled data is not available. By combining
advances within NLP, utilising word embeddings and incorporating additional
knowledge into the data, a classifier which do not rely on labelled data is presented.
Instead of learning by examples as supervised methods, the classifier manages to
find semantic similarities between clinical notes and the description of the different
codes, making it intuitively similar to how we humans would classify a code.
Beskrivning
Ämne/nyckelord
Natural language processing , text classification , unsupervised learning , word embedding , short text , self-supervised , information-retrieval , clinical text