Showing 6-10 of 128 resources:
Using Real-World Data to Unlock Better Outcomes in IBD & NASH Patients

Gastrointestinal and metabolic disorders are highly prevalent and result in reduced quality of life for many patients. Treatments are aimed at reducing symptoms and preventing complications and progression. More research is needed to better understand natural history and progression, treatment effectiveness, and outcomes. Register here Linked real-world data (RWD) and machine learning (ML) applications provide[…]

Finding hard-to-find patients: Integrating real-world data and AI

BioPharma Dive Published Online: July 18, 2022 Read the Full Article>> Identifying patients ‘outside the clinic’ can provide significant benefits for researchers and population health managers. Better understanding of patient cohorts can shed light on patient journeys, help optimize decisions around treatment choice and timing and inform the development of new therapies and intervention programs.[…]

Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis

Sage Journals Published June 22, 2022 Read the Full Manuscript>>  Abstract Background Disability assessment using the Expanded Disability Status Scale (EDSS) is important to inform treatment decisions and monitor the progression of multiple sclerosis. Yet, EDSS scores are documented infrequently in electronic medical records. Objective To validate a machine learning model to estimate EDSS scores[…]