SUMMARY
Moein Enayati, Ph.D., is a research scientist in the field of artificial intelligence (AI) and machine learning (ML). He is enthusiastic about using his expertise to advance the science of healthcare delivery and disease diagnosis. Dr. Enayati's research interests include advanced data analytics, ML, natural language processing and noninvasive sensors for early disease identification and monitoring of people's health conditions.
Dr. Enayati strives to develop models, algorithms and tools that leverage multimodal clinical data to provide insight into complex health conditions. This improves the safety, efficiency and effectiveness of people's care.
Working in the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Dr. Enayati makes significant efforts in developing novel technologies and algorithms that improve clinical practice and people's care.
He collaborates with clinical faculty and staff members from the cardiovascular medicine, emergency medicine and clinical genomics departments, the Center for Individualized Medicine, and the Mayo Clinic Comprehensive Cancer Center using exceptionally large sets of people's multimodal historical data. His research has resulted in numerous publications and multiple clinical decision systems integrated into the clinical practice inside and outside Mayo Clinic.
Focus areas
- ML for early disease identification. Dr. Enayati uses different data analytics and ML techniques to study big datasets of clinical records to identify important factors in disease diagnosis. Individual-specific low-dimensional feature representations are being adopted for longitudinal monitoring of people's health conditions.
- Text mining and natural language processing (NLP). One aspect of Dr. Enayati's research focuses on NLP, text processing and extraction of valuable clinical information from different semistructured and unstructured clinical notes. Research and development in the field of NLP provide automated alternatives for current time- and labor-intensive data extractions in clinical trials within different fields of practice.
- Diagnostic errors in medicine. Dr. Enayati develops ML algorithms to identify specific instances of missed or delayed diagnoses. He analyzes causal relationships among potential individual-, provider- and system-related factors. Such studies help healthcare professionals determine root cause parameters and provide guidance to reduce or eliminate chances of recurrence.
Significance to patient care
Dr. Enayati's specific aim is to develop and adapt advanced AI and ML technologies that can directly enhance both individuals' care and clinical practice. Such efficient, reliable, generalizable and explainable AI-based solutions will help identify the root cause of complex clinical situations, reduce the chance of adverse events, enhance the diagnostic process and improve people's experience of clinical care.