Prediction of the Most Common Symptoms in Psychological Illnesses with Language Representation Models
| dc.contributor.author | Aygun I. | |
| dc.contributor.author | Kaya M. | |
| dc.date.accessioned | 2025-04-10T11:02:42Z | |
| dc.date.available | 2025-04-10T11:02:42Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | It is a known fact as a result of researches that psychological disorders are seen more frequently in society day by day and early diagnosis of these disorders is very important. To detect psychological disorders, it is an important achievement to identify the symptoms in the sentences of potential patients. In the present study, the most frequently used symptoms in the sentences of past psychiatric patients were investigated. The deep learning supported BERT model was used to analyze the texts and the Named Entity Recognition (NER) method was used for symptom detection. Thus, a model is proposed that enables the detection of symptoms even when they are expressed in different ways. The success of the proposed model in detecting the symptoms is between 83.6 and 86.2% and the most common symptoms are shortness of breath, loss of attention and loss of appetite. © 2024 IEEE. | |
| dc.identifier.DOI-ID | 10.1109/ICSCC62041.2024.10690750 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14701/44202 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.title | Prediction of the Most Common Symptoms in Psychological Illnesses with Language Representation Models | |
| dc.type | Conference paper |