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DC Field | Value | Language |
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dc.date.accessioned | 2020-11-18T14:09:40Z | - |
dc.date.available | 2020-11-18T14:09:40Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Holomjova, V. (2020). Remedi: a medical information extraction system (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/64116 | - |
dc.description | B.SC.ICT(HONS)ARTIFICIAL INTELLIGENCE | en_GB |
dc.description.abstract | Novel articles containing modern advancements and discoveries in the medical industry are being published online daily. Due to privacy issues, there is a lack of data available to build knowledge extraction tools specialized in the medical industry. This study presents a biomedical information extraction tool called Remedi, specialized in identifying ‘medical problem’ and ‘treatment’ entities from unstructured text in the medical domain, as well as the relations between them. The tool consists of a Biomedical Named Entity Recognition (BM-NER) model which employs a Bidirectional-Long Short-Term Memory Conditional Random Field (Bi-LSTM-CRF) model, as well as a Biomedical Relation Extraction (BMRE) model which consists of a Bi-LSTM model. A subset of the i2b2 2010 challenge dataset has been acquired to train the BM-NER and BM-RE components. The dataset includes annotated de-identified medical reports that can be used for concept extraction and relation classification tasks. We evaluated the performance of the Bi-LSTM and Bi-LSTM-CRF model when given additional external features such as the UMLS and the GENIA tagger. Results showed that with the addition of external features, both models outperformed similar models that did not leverage on such features. The BM-NER system achieved a micro-average F1 score of 0.846 which can be ranked second amongst top-performing models presented in the i2b2 2010 concept extraction challenge. On the other hand, the BM-RE system achieved a micro-average F1 score of 0.652 and needs further improvements to reach the performance of the best models in the i2b2 2010 relation extraction challenge. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Medicine -- Data processing | en_GB |
dc.subject | Pattern recognition systems | en_GB |
dc.title | Remedi : a medical information extraction system | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Artificial Intelligence | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Holomjova, Valerija | - |
Appears in Collections: | Dissertations - FacICT - 2020 Dissertations - FacICTAI - 2020 |
Files in This Item:
File | Description | Size | Format | |
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20BITAI006 - Holomjova Valerija.pdf Restricted Access | 1.37 MB | Adobe PDF | View/Open Request a copy |
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