Phenotyping and visualizing infusion-related reactions for breast cancer patients

Deyu Sun, Gopal Sarda, Steven J. Skube, Anne H Blaes, Saif Khairat, Genevieve B Melton-Meaux, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Infusion-related reactions (IRRs) are typical adverse events for breast cancer patients. Detecting IRRs and visualizing their occurance associated with the drug treatment would potentially assist clinicians to improve patient safety and help researchers model IRRs and analyze their risk factors. We developed and evaluated a phenotyping algorithm to detect IRRs for breast cancer patients. We also designed a visualization prototype to render IRR patients' medications, lab tests and vital signs over time. By comparing with the 42 randomly selected doses that are manually labeled by a domain expert, the sensitivity, positive predictive value, specificity, and negative predictive value of the algorithms are 69%, 60%, 79%, and 85%, respectively. Using the algorithm, an incidence of 6.4% of patients and 1.8% of doses for docetaxel, 8.7% and 3.2% for doxorubicin, 10.4% and 1.2% for paclitaxel, 16.1% and 1.1% for trastuzumab were identified retrospectively. The incidences estimated are consistent with related studies.

Original languageEnglish (US)
Title of host publicationMEDINFO 2017
Subtitle of host publicationPrecision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics
EditorsZhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781614998297
StatePublished - 2017
Event16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China
Duration: Aug 21 2017Aug 25 2017

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Other16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017

Bibliographical note

Funding Information:
This research was supported by the Agency for Healthcare Research & Quality grant (#1R01HS022085) (Melton), the National Center for Complementary & Integrative Health Award (R01AT009457) (Zhang), and the University of Minnesota Clinical and Translational Science Award (#8UL1TR000114) (Blazer). The authors thank Fairview Health Services for support of this research.


  • Algorithms
  • Patient safety
  • Phenotype


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