In this study, we present a fuzzy contrast-based model that classifies mental patient authored text into different symptoms by using an attention network for position-weighted words. Then, the mental data are labeled using the trained embedding. After that, the lexicons of the attention network are extended to allow the use of transfer learning methods. Our proposed approach classifies weighted attention words using similarity as well as contrast sets. The fuzzy model then classifies mental health data into different groups. To illustrate the performance of the proposed model, the approach is compared with the non-embedding as well as standard approaches. From the demonstrated results, the feature vector has a high Receiver Operating Characteristic Curve (ROC)-curve of 0.82 for 9 different symptom problems.

An Explainable Mental Health Fuzzy Deep Active Learning Technique

Tomasiello S.;
2022-01-01

Abstract

In this study, we present a fuzzy contrast-based model that classifies mental patient authored text into different symptoms by using an attention network for position-weighted words. Then, the mental data are labeled using the trained embedding. After that, the lexicons of the attention network are extended to allow the use of transfer learning methods. Our proposed approach classifies weighted attention words using similarity as well as contrast sets. The fuzzy model then classifies mental health data into different groups. To illustrate the performance of the proposed model, the approach is compared with the non-embedding as well as standard approaches. From the demonstrated results, the feature vector has a high Receiver Operating Characteristic Curve (ROC)-curve of 0.82 for 9 different symptom problems.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4870695
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