Title: Espousal of Machine Learning to Address Neurological Ailments : Appraisal of the Contemporary Endeavors
Machine learning is a specialization of Artificial intelligence that deals with developments of predictive algorithmic models. With Artificial intelligence in general and machine learning in particular exhibiting superior predictive and prognositicative capabilities, when compared with conventional statistical techniques, an extraordinary trend is emerging calling our attention towards the approach to develop machine learning models to optimize the outcomes of an ailment by addressing it at each and every step from prevention through management to prognostication. Researchers throughout the globe are coming together in a bid to counter a surfeit of neurological disorders by adopting machine learning with appreciably motivating results. That being indicated, considerable exploration requires to be undertaken vis a vis the neoplastic conditions especially glioblastoma multiforme and anaplastic oligodendroglioma that have considerably high associated morbidity and mortality. In our talk, we shall be discussing novel algorithmic models developed by researchers to take up debilitating neurological disorders with exploration of a fruitful future research guidance.
Ali Haider Bangash is pursuing Bachelors of Medicine, Bachelors of Surgery at STMU Shifa College of Medicine, Islamabad, Pakistan with an interest in Neurosurgical Oncology and Vascular Surgery. His research focuses on the applications of machine learning and their incorporation into the management protocols to decrease the morbidity and mortality associated with medical conditions especially Neoplastic conditions. He is a medical student member of the American Society of Clinical Oncology and the North American Spine Society as well as the COST Action Evidence-Based Research (EVBRES) based at the Western Norway University of Applied Sciences, Bergen, Norway. He has contributed to multi-center collaborative studies published in Anaesthesia and The British journal of surgery.