1. Bridewell W, Das AK. Social network analysis of physician interactions: the effect of institutional boundaries on breast cancer careAmerican Medical Informatics Association Annual Symposium Proceedings, 2011:152-60.

  2. Lee WN, Bridewell W, Das AK. Alignment and clustering of breast cancer patients by longitudinal treatment historyAmerican Medical Informatics Association Annual Symposium Proceedings, 2011:760-7.

  3. Weber SC, Lowe H, Das A, Ferris T. A simple heuristic for blindfolded record linkageJournal of the American Medical Informatics Association, 2012:19:e157-e161.

  4. Weber SC, Seto T, Olson C, Kenkare P, Kurian AW, Das AK. Oncoshare: lessons learned from building an integrated multi-institutional database for comparative effectiveness researchAmerican Medical Informatics Association Annual Symposium Proceedings. 2012:970-8.

  5. Kurian AW, Mitani MS, Desai M, Yu PY, Seto T, Weber SC, Olson C, Kenkare P, Gomez SL, de Bruin MA, Horst K, Belkora J, May SG, Frosch DL, Blayney DW, Luft HS, Das AK. Breast cancer treatment across healthcare systems: linking electronic medical records and state registry data to enable outcomes researchCancer. 2014;120:103-11. 

  6. Halley MC, May SG, Rendle KA, Frosch DL, Kurian AW. Beyond barriers: fundamental disconnects underlying the treatment of breast cancer patients’ sexual healthCulture, Health and Sexuality. 2014;16:1169-80.

  7. Thompson CA, Kurian AW, Luft HS. Linking electronic health records to better understand breast cancer patient pathways within and between two health systemseGEMs (Generating Evidence & Methods to improve patient outcomes). 2015;3(1):Article 5.

  8. Mitani AA, Kurian AW, Das AK, Desai M. Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effectsStatistical Methodology. 2015;27:82-99.

  9. Afghahi A, Forgo E, Mitani A, Desai M, Varma S, Seto T, Jensen KC, Troxell M, Gomez SL, Das AK, Beck AH, Kurian AW*, West RB* (Contributed Equally). Chromosomal copy number alterations for risk assessment of ductal carcinoma in situBreast Cancer Research. 2015;17:108.

  10. Afghahi A, Mathur M, Mitani A, Desai M, Yu PP, de Bruin MA, Seto T, Olsen C, Kenkare P, Gomez SL, Das AK, Luft HS, Thompson C, Sledge G, Sing AP, Kurian AW. Use and impact of gene expression profiling in early-stage breast cancer: a study of linked electronic medical record, cancer registry and genomic data across two health care systemsJournal of Oncology Practice. 2016; 12:e697-709.

  11. Low YS, Daugherty AC, Schroeder EA, Chen W, Seto T, Weber S, Lim M, Hastie T, Mathur M, Desai M, Farrington C, Radin A, Sirota M, Kenkare P, Thompson CA, Yu PP, Gomez SL, Sledge GW, Kurian AW, Shah NH. Synergistic drug combinations from electronic health records and gene expression. Journal of the American Medical Informatics Association. 2017;24:565-76.

  12. Afghahi A, Purington N, Han S, Desai M, Rigdon J, Pierson E, Mathur MB, Thompson CA, Telli ML, Badve S, Curtis CN, West RB, Horst K, Gomez SL, Ford JM, Das AK, Sledge GW, Kurian AW. Higher absolute lymphocyte counts predict lower mortality from early-stage triple-negative breast cancer. Clinical Cancer Research. March 26, 2018. Epub ahead of print.

Funding Sources

Work described in these publications was supported by one or more of the following sponsors:

•• Susan and Richard Levy Gift Fund

•• Suzanne Pride Bryan Fund for Breast Cancer Research

•• Breast Cancer Research Foundation

•• California Breast Cancer Research Program Grants 16OB-0149 and 19IB-0124

•• Stanford University Developmental Research Fund

•• Contract No. HHSN261201000140C from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program to the Cancer Prevention Institute of California

• Clinical and Translational Science Award No. UL1 RR025744a to Stanford University from the National Institutes of Health