New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience.

Lawrenceville, NJ, USA--May 19, 2020--ISPOR--The Professional Society for Health Economics and Outcomes Research (HEOR) held its second Virtual ISPOR 2020 plenary session this afternoon, "HEOR and Clinical Decision Making--Advancing Meaningful Progress." Virtual ISPOR 2020 is the Society's first completely virtual conference that was redesigned as an online event when the COVID-19 pandemic required a necessary cancelation of the in-person conference. The rise of big data and artificial intelligence bring wide-ranging opportunities for HEOR to become a relevant part of clinical decision making. In this plenary, panelists explored data-driven, collaborative approaches to clinical decision making and ways that HEOR can help strengthen health service delivery and enhance the patient experience. Nigam Shah, MBBS, PhD was the first to provide introductory remarks. He outlined a patient journey that has the potential to generate a wide variety of disparate data sources--including claims, ICD codes, medications, procedures, lab tests, clinical notes, and more--that can be used to inform artificial intelligence (AI).