Learning to Identify Rare Disease Patients from Electronic Health Records

Autor: Colbaugh, Rich, Glass, Kristin, Rudolf, Christopher, Tremblay Volv Global, Lausanne, Switzerland, Mike

There is increasing interest in developing prediction models capable of identifying rare disease patients in population-scale databases such as electronic health records (EHRs). Deriving these models is challenging for many reasons, perhaps the most

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::14a0e1d5eaddd05547a968536bf37f0c
https://europepmc.org/articles/PMC6371307/

Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

Autor: Sharma R; Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York, NY, USA., Hogarth DK; Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL, USA., Colbaugh R; Volv Global SA, Epalinges, Switzerland., Glass K; Volv Global SA, Epalinges, Switzerland., Mezine A; Volv Global SA, Epalinges, Switzerland., Liakoni V; Volv Global SA, Epalinges, Switzerland., Rudolf C; Volv Global SA, Epalinges, Switzerland., Himmelhan I; Takeda Pharmaceuticals International AG, Zürich, Switzerland., Hinson J; Takeda Pharmaceuticals USA, Inc., Lexington, MA, USA., Sanchirico M; Takeda Pharmaceuticals USA, Inc., Lexington, MA, USA.

Publikováno v: COPD [COPD] 2024 Dec; Vol. 21 (1), pp. 2393348. Date of Electronic Publication: 2024 Sep 23.