436 citations
- Novartis (Switzerland)CH2 papers
- Pfizer (United States)US2 papers
- Roche (Switzerland)CH2 papers
- AbbVie (Germany)DE1 paper
- Bayer (United States)US1 paper
- Boehringer Ingelheim (Germany)DE1 paper
- Center for Astrophysics Harvard & SmithsonianUS1 paper
- Claremont McKenna CollegeUS1 paper
- Commonwealth Scientific and Industrial Research OrganisationAU1 paper
- Delft University of TechnologyNL1 paper
- Duke UniversityUS1 paper
- GlaxoSmithKline (United Kingdom)GB1 paper
7 papers
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL
Jeffery L. Painter, Venkateswara Rao Chalamalasetti, Raymond Kassekert +1
Objective: To enhance the efficiency and accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural langua…
Estimands in Hematologic Oncology Trials
Steven Sun, Hans-Jochen Weber, Emily Butler +2
The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpre…
Assessing the Impact of COVID-19 on the Objective and Analysis of Oncology Clinical Trials -- Application of the Estimand Framework
Evgeny Degtyarev, Kaspar Rufibach, Yue Shentu +10
COVID-19 outbreak has rapidly evolved into a global pandemic. The impact of COVID-19 on patient journeys in oncology represents a new risk to interpretation of trial results and it…
Learning about learning by many-body systems
Weishun Zhong, Jacob M. Gold, Sarah Marzen +2
Diverse many-body systems, from soap bubbles to suspensions to polymers, learn and remember patterns in the drives that push them far from equilibrium. This learning may be leverag…
Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data
Paweł Widera, Paco M. J. Welsing, Christoph Ladel +7
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majorit…
Classification of crystallization outcomes using deep convolutional neural networks
Andrew E. Bruno, Patrick Charbonneau, Janet Newman +6
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from vari…