Alzheimer's Diagnosis and Generation-Based Chatbot Using Hierarchical Attention and Transformer
Park Jun Yeong, Shin Su Jong, Choi Chang Hwan, Lee Jung Jae, Choi, Sang-il

TL;DR
This paper introduces a unified transformer-based model that simultaneously classifies Alzheimer's patients and generates chatbot responses, aiding early diagnosis and reducing reliance on expert questionnaires.
Contribution
It presents a novel single-model architecture combining classification and generation tasks for Alzheimer's patient analysis using hierarchical attention and transformers.
Findings
Model reduces loss function significantly, indicating effective pattern capture.
The approach can assist early diagnosis and longitudinal studies.
Demonstrated on DmentiaBank corpus with promising results.
Abstract
In this paper, we propose a natural language processing architecture that can handle tasks that previously required two models as one model. With a single model, we analyze the language patterns and conversational context of Alzheimer's patients and derive answers from two results: patient classification and chatbot. If the patient's language characteristics are identified by chatbots in daily life, doctors can plan more precise diagnosis and treatment for early diagnosis. The proposed model is used to develop chatbots that replace questionnaires that required experts. There are two natural language processing tasks performed by the model. The first is a 'natural language classification' that indicates with probability whether the patient has an illness, and the second is to generate the next 'answer' of the chatbot to the patient's answer. In the first half, a context vector, which is…
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Taxonomy
TopicsMachine Learning in Healthcare · Mental Health via Writing · Topic Modeling
