INSIGHT-MP: Interpretable Natural Language Processing System for Identification of Transthyretin Amyloidosis with Mixed Phenotype Using Machine Learning (P12-11.010)
To develop a machine learning model (INSIGHT-MP) for identification of patients likely to have a mixed transthyretin amyloidosis (ATTR) phenotype among heart failure patients with neuropathy, which could guide and optimize referrals for diagnostic testing. We also aim to offer insights into the key factors driving the model.
Authors
- Robert Gottlieb (ORCID: https://orcid.org/0000-0001-8376-8709)
- Monica Bennett (ORCID: https://orcid.org/0000-0001-7506-7160)
- Cynthia Sunderman (ORCID: https://orcid.org/0000-0002-3484-4768)
- Jason Ettlinger
- John Venditto
- Mia A. Papas (ORCID: https://orcid.org/0000-0001-8636-411X)
- Amit Arora (ORCID: https://orcid.org/0000-0002-7347-4376)
- Briget da Graca (ORCID: https://orcid.org/0000-0002-8634-7122)
- Kendall Hammonds (ORCID: https://orcid.org/0000-0002-2993-7521)
- Elisa L. Priest (ORCID: https://orcid.org/0000-0003-0474-6275)
- Misbah U. Khan (ORCID: https://orcid.org/0009-0002-4989-5178)
Institutions
- Baylor Medical Center at Garland (US)
- AstraZeneca (France) (FR)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000210673
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00