Dr. Pradeepa H. Dakappa, MSc, PhD is an academic pharmacologist, researcher, and clinician with expertise at the interface of clinical pharmacology, biomedical signal processing, and artificial intelligence–driven diagnostic modeling. He currently serves as Associate Professor in the Department of Pharmacology at Siddaganga Medical College and Research Institute, Tumakur, Karnataka, India. With over a decade of academic and clinical experience, he has developed a multidisciplinary research profile integrating pharmacological science with computational analytics to enhance diagnostic precision and therapeutic outcomes. Dr. Dakappa earned his PhD in Pharmacology from Kasturba Medical College, Mangalore, under Manipal Academy of Higher Education. His doctoral research pioneered the application of continuous 24-hour tympanic temperature monitoring and advanced signal analysis in patients with undifferentiated fever, leading to the development of predictive models for disease classification. He also holds an MSc in Medical Pharmacology and a Postgraduate Diploma in Clinical Research, with formal training in pharmacovigilance, clinical data management, and biostatistics. His academic foundation is further strengthened by certifications in Biomedical Research, Epidemiology, Research Methodology, and Practical Biological Signal Analysis, including advanced training at NITK Surathkal. His research contributions are distinguished by innovative applications of entropy-based analytics and machine learning to physiological signals. He has co-authored pioneering studies utilizing Sample Entropy and Slope Entropy for differentiating infectious and non-infectious fever patterns. His work has demonstrated the feasibility of extracting clinically meaningful diagnostic insights from continuous temperature recordings using artificial neural networks and computational modeling techniques. Dr. Dakappa has published extensively in internationally indexed peer-reviewed journals, including Journal of Healthcare Engineering, Entropy, Mathematical Biosciences and Engineering, Critical Reviews in Biomedical Engineering, Tropical Doctor, F1000Research, and Journal of Clinical and Diagnostic Research. His research spans experimental pharmacology, clinical observational studies, biomedical engineering applications, and AI-based disease classification. His contributions include characterization of tri-phasic fever in dengue, temperature dynamics in tuberculosis, comparative evaluation of temperature recording methodologies, and predictive modeling for undifferentiated febrile illnesses. In addition to journal publications, he has contributed to international conference proceedings published by Springer Nature and IEEE, presenting work on intelligent disease classification systems and deep learning–based fever pattern recognition. His current research focuses on developing AI-driven software for automated causality assessment of adverse drug reactions, strengthening pharmacovigilance systems, and investigating epidemiological aspects of Type 3c diabetes mellitus. He is also actively engaged in knowledge, attitude, and practice studies related to ADR reporting and drug interaction awareness. An experienced educator and research mentor, Dr. Dakappa emphasizes translational pharmacology, critical thinking, and the integration of emerging technologies into medical education. His long-term research vision centers on intelligent healthcare systems, data-driven decision support tools, and innovation-driven pharmacological research aimed at improving patient safety and clinical outcomes.