Emotion recognition support system: Where physicians and psychiatrists meet linguists and data engineers

Area of Science:

• Medical Informatics
• Clinical Psychology
• Computational Linguistics

Background:

• Understanding patient emotional states is crucial for effective medical diagnosis and treatment.
• Patients often withhold emotional expression during consultations with non-psychiatrist physicians.
• Clinicians may lack the expertise or time to accurately interpret patients' verbal and non-verbal emotional cues, leading to an emotion recognition barrier.

Purpose of the Study:

• To address the emotion recognition barrier between clinicians and patients.
• To integrate approaches from psychology, linguistics, and data science for emotion detection.
• To propose an integrated solution for emotion recognition support during medical consultations.

Main Methods:

• Identifying and combining methodologies from psychology, linguistics, and data science.
• Analyzing verbal communication for emotional signals.
• Developing a platform to provide clinicians with emotional guides and indices.

Main Results:

• The proposed integrated approach facilitates emotion detection from verbal communication.
• The developed platform can offer real-time emotional insights to clinicians.
• This system aims to bridge the emotion recognition gap in clinical settings.

Conclusions:

• An integrated, interdisciplinary approach can enhance clinicians' ability to recognize patient emotions.
• A technological solution based on verbal communication analysis can support medical diagnosis and treatment.
• Improving emotion recognition is vital for personalized patient care and overcoming communication barriers.