Merhbene, Ghofrane; Puttick, Alexandre Riemann; Kurpicz-Briki, Mascha (21 September 2023). BFH-AMI at eRisk@ CLEF 2023 In: CLEF 2023: Conference and Labs of the Evaluation Forum: Working Notes of CLEF. Thessaloniki, Greece. September 18-21, 2023.
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Mental health problems are a rising problem of today’s society. Methods of machine learning and natural language processing provide interesting new possibilities for psychology and psychiatry. In particular, eating disorders (ED) are widespread and can be life-threatening if untreated. This paper describes the approach to Task 3 of the eRisk 2023 challenge of the BFH-AMI team. The task concerned the prediction of patients’ answers to the Eating Disorder Examination Questionnaire (EDE-Q) based on their social media writing history. In our approach, we used a logistic regression model that was fed with a combination of user and question embeddings from the GPT-2 Large model.
Item Type: |
Conference or Workshop Item (Paper) |
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Division/Institute: |
School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) > IDAS / Applied Machine Intelligence School of Engineering and Computer Science |
Name: |
Merhbene, Ghofrane; Puttick, Alexandre Riemann and Kurpicz-Briki, Mascha |
Subjects: |
B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
Language: |
English |
Submitter: |
Mascha Kurpicz-Briki |
Date Deposited: |
18 Dec 2023 13:41 |
Last Modified: |
18 Dec 2023 13:44 |
Related URLs: |
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Uncontrolled Keywords: |
Early Detection System, Natural Language Processing, Machine Learning, Eating Disorder, Mental Health |
ARBOR DOI: |
10.24451/arbor.20736 |
URI: |
https://arbor.bfh.ch/id/eprint/20736 |
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