Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910726859259904 |
|---|---|
| author | Torre, Leon-Paul Schaub Quiros, Pelayo Mieres, Helena Garcia |
| author_facet | Torre, Leon-Paul Schaub Quiros, Pelayo Mieres, Helena Garcia |
| contents | In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching the model to learn the type, severity and location on the body of a dermatological pathology, as well as in which order it has to learn these three features, significantly increases its accuracy. The article presents the demonstration of state-of-the-art results for classification of medical texts with a precision of 0.84, micro and macro F1-score of 0.82 and 0.75, and makes both the method and the data set used available to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies Torre, Leon-Paul Schaub Quiros, Pelayo Mieres, Helena Garcia Computation and Language In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching the model to learn the type, severity and location on the body of a dermatological pathology, as well as in which order it has to learn these three features, significantly increases its accuracy. The article presents the demonstration of state-of-the-art results for classification of medical texts with a precision of 0.84, micro and macro F1-score of 0.82 and 0.75, and makes both the method and the data set used available to the community. |
| title | Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.03176 |