Classifying Empathy in Textual Anamnesis using Single Layered LSTM Performance
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Abstract
This study explores the challenges of integrating artificial intelligence (AI) in healthcare 5.0, particularly the perceived lack of empathy in AI applications and its potential impact on human connections in healthcare services. Patient-centered healthcare relies on effective communication, and the paper highlights the absence of computational studies detecting empathetic support in clinical interviews. The research distinguishes between cognitive and affective empathy, noting the medical emphasis on cognitive empathy for objectivity and detachment, while cautioning against affective empathy due to concerns about emotional strain. The proposed solution involves employing Long Short-Term Memory (LSTM) for sentiment analysis in clinical conversations, citing its accuracy and efficiency. Methodologically, the study annotates conversations for affective and cognitive empathy, performs word embedding using word2vec and GloVe, and trains the LSTM model on a dataset of medical interviews. Results suggest promising outcomes in classifying empathy levels, though limitations are acknowledged, such as the simplified empathy aspects and the need for more robust validation methods. The study contributes to the development of Vidya Medic, an AI-assisted intelligent healthcare system, with potential implications for enhancing empathetic communication in clinical settings. This experiment's findings suggest that the LSM model has been developed for the purpose of classifying empathy levels in clinical conversations. However, there are limitations to this study, including the simplification of empathy aspects, which focus solely on affective and cognitive empathy
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