Using machine learning predictions to design more efficient MS clinical trials
Автор: VJNeurology
Загружено: 2022-03-21
Просмотров: 441
Описание: Multiple sclerosis (MS) has a very heterogenous presentation, with some patients worsening steadily over time, whilst others experience sporadic autoimmune attacks, making measuring treatment efficacy very challenging. David Li-Bland, PhD, Unlearn.ai, Oakland, CA, discusses the need to increase the efficiency of MS clinical trials, to reduce the cost and increase the number of studies. Dr Li-Bland explains how 2400 patients with MS, including relapse-remitting, secondary-progressive, and primary-progressive MS, were used to train a machine learning model to predict MS progression. The model generates clinical predictions, such as Expanded Disability Status Score, MS Functional Composite (MSFC), and relapse events, based on a patient’s baseline characteristics. The model is used to create a digital twin for each patient, predicting the long-term effects of disease and relapse occurrence when the patient is given placebo. Digital twins can be used to simulate the outcomes of a control group, understand the effects of particular inclusion or exclusion criteria, or to select cohorts with different types of progression, allowing a reduction in the control group size and increased clinical trial efficiency. This interview took place at the ACTRIMS Forum 2022 in West Palm Beach, Florida.
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
