Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer
- 1 December 2019
- journal article
- research article
- Published by Elsevier BV in Journal of Biomedical Informatics
- Vol. 100, 103335
- https://doi.org/10.1016/j.jbi.2019.103335
Abstract
No abstract availableFunding Information
- Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
This publication has 19 references indexed in Scilit:
- Real-World Treatment Patterns, Overall Survival, and Occurrence and Costs of Adverse Events Associated With First-line Therapies for Medicare Patients 65 Years and Older With Advanced Non–small-cell Lung Cancer: A Retrospective StudyClinical Lung Cancer, 2018
- Real-World Evidence In Support Of Precision Medicine: Clinico-Genomic Cancer Data As A Case StudyHealth Affairs, 2018
- Phenotype-driven precision oncology as a guide for clinical decisions one patient at a timeNature Communications, 2017
- Real-world first-line treatment and overall survival in non-small cell lung cancer without known EGFR mutations or ALK rearrangements in US community oncology settingPLOS ONE, 2017
- Temporal electronic phenotyping by mining careflows of breast cancer patientsJournal of Biomedical Informatics, 2017
- Opportunities and challenges in leveraging electronic health record data in oncologyFuture Oncology, 2016
- Precision Oncology: An OverviewJournal of Clinical Oncology, 2013
- Deep phenotyping for precision medicineHuman Mutation, 2012
- Flexible guideline-based patient careflow systemsArtificial Intelligence in Medicine, 2001
- Guideline-based careflow systemsArtificial Intelligence in Medicine, 2000