AHFE Open AccessMuse Alpha: AI-based Preliminary Diagnosis for Cognitive Thought Patterns
Abstract
Recent issues of medication misuse and abuse have prompted research into utilizing big data and artificial intelligence to aid psychiatrists in determining medication dosages. Muse Alpha contributes through high-quality data collection, AI-based patient similarity analysis, and a patient-centric conversation approach. This includes the extraction of patterns of negative thoughts, achieving up to 87.5% accuracy in guiding conversations. The goal is to overcome the limitations of brief one to one interactions between doctors and patients, ensuring more accurate medication prescriptions. Discovering patterns of negative thoughts and conveying them visually to the doctor assists in providing an accurate diagnosis of the patient's condition and aids in the precise diagnosis of medication for the patient.
Keywords: Medical System, Artificial Intelligence, Machine Learning, Health Care, Medical Device
DOI: 10.54941/ahfe1004839
Cite this paper
More from this volume
- Exploring Intraoperative Cognitive Biases in Cardiac Surgery Teams
- A Structured Approach to Clinical Pathway Mapping (CPM): a Case Study of a Postoperative Patient Controlled Analgesia (PCA) pump
- Challenges and Uncertainties in Violence Risk Assessment: A Critical Examination of Practice and National Guidelines in Norway's Specialist Mental Health Services
- Artificial Intelligence in Medication Management for Alzheimer's Patients in China
- Exploring Self-Medication Practices Among Undergraduate University Students: A Qualitative Descriptive Study
- Exploring the Usability and User Experience of the Symptom Checker Interface
- Explainable Hybrid Machine Learning Technique for Healthcare Service Utilization
- Enhancing Accessibility in Mexican Healthcare: A Versatile Lift and Walking Aid System
- Patient Centered Care: Medical Error Disclosure Guidelines Across Canada
- Stepped Wedge Design Analyses under Pandemic`s Period Effect: Alternative Approach using Non-specific Neck Pain Intervention Data
- Evaluation of Patient Stress During Mammograms through Surface Electromyography Analysis
- Integrating Intelligent Control into Far UVC Wearable Garments for On-Demand Airborne Viral and Bacterial Protection