Data within the medical sector is often stored as free text entries. This is especially true for report texts, which are written after an examination. To be able to automatically gather data from these texts they need to be analyzed and classified to show what findings the examinations had. This thesis compares three state of the art deep learning approaches to classify short medical report texts. This is done for two types of examinations, so the concept of transfer learning plays a role in the evaluation. An optimal model should learn concepts that are applicable for more than one type of examinations, since we can expect the texts to be similar. The two data set from the examinations are also of different sizes, and both have an uneven distribution among the target classes. One of the models is based on techniques traditionally used for language processing using deep learning. The two other models are based on techniques usually used for image recognition and classification. The latter models proves to be the best across the different metrics, not least in the sense of transfer learning as they improve the results when learning from both types of examinations. This becomes especially apparent for the lowest frequent class from the smaller data set as none of the models correctly predict this class without using transfer learning.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-356140 |
| Date | January 2018 |
| Creators | Nelsson, Mikael |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 18032 |
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