Scholars@Duke
Jessica Dale Tenenbaum

Jessica Dale Tenenbaum

Associate Professor of Biostatistics & Bioinformatics
Biostatistics & Bioinformatics, Division of Translational Biomedical
jessie.tenenbaum@duke.edu
Duke Box 2721, Durham, NC 27710
2424 Erwin Road Ste 902, 9024 Hock Plaza, Durham, NC 27705

Scholarly Works - Conferences

Machine Learning Algorithm Prospectively Predicts Survival for High-Risk Patients Undergoing Radiotherapy: A Survival Analysis of SHIELD-RT.

Conference International journal of radiation oncology, biology, physics · November 2021 Purpose/objective(s)Mortality prediction is critical to appropriate cancer care planning. This has become a topic of interest, with machine learning (ML) tools demonstrating accurate binary predictions for mortality at specific time points. There ... Full text Cite

Impact of machine learning-directed on-treatment evaluations on cost of acute care visits: Economic analysis of SHIELD-RT.

Conference Journal of Clinical Oncology · May 20, 2021 1509 Background: SHIELD-RT was a randomized controlled quality improvement study (NCT03775265) that implemented electronic health record-based machine learning (ML) to direct supplemental visits for high risk (HR) patients underg ... Full text Cite

Accuracy of a Natural Language Processing Pipeline to Identify Patient Symptoms during Radiation Therapy

Conference International Journal of Radiation Oncology*Biology*Physics · September 2019 Full text Cite

Democratizing Health Data for Translational Research.

Conference Pac Symp Biocomput · 2018 There is an expanding and intensive focus on the accessibility, reproducibility, and rigor of basic, clinical, and translational research. This focus complements the need to identify sustainable ways to generate actionable research results that improve hum ... Link to item Cite

Best practices and lessons learned from reuse of 4 patient-derived metabolomics datasets in Alzheimer's disease.

Conference Pac Symp Biocomput · 2018 The importance of open data has been increasingly recognized in recent years. Although the sharing and reuse of clinical data for translational research lags behind best practices in biological science, a number of patient-derived datasets exist and have b ... Link to item Cite

OPEN DATA FOR DISCOVERY SCIENCE.

Conference Pac Symp Biocomput · 2017 The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, ... Full text Link to item Cite

Assessing the quality of electronic health record data and patient self-report data

Conference Proceedings of the 22nd MIT International Conference on Information Quality, ICIQ 2017 · January 1, 2017 © 2017 MIT Information Quality Program. All rights reserved. Knowing the accuracy of self-reported medical data is critical to using the data in clinical decision-making and research. The same is true for data in Electronic Health Records (EHRs). For these ... Cite

Assessing the quality of electronic health record data and patient self-report data

Conference Proceedings of the 22nd MIT International Conference on Information Quality, ICIQ 2017 · January 1, 2017 © 2017 MIT Information Quality Program. All rights reserved. Knowing the accuracy of self-reported medical data is critical to using the data in clinical decision-making and research. The same is true for data in Electronic Health Records (EHRs). For these ... Cite

Assessing the quality of electronic health record data and patient self-report data

Conference Proceedings of the 22nd MIT International Conference on Information Quality Iciq 2017 · January 1, 2017 Knowing the accuracy of self-reported medical data is critical to using the data in clinical decision-making and research. The same is true for data in Electronic Health Records (EHRs). For these data, accuracy reported in the literature varies widely leav ... Cite

Translational bioinformatics 101

Conference Pacific Symposium on Biocomputing · January 1, 2016 Full text Cite