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 language||English (US)|
|Title of host publication||MEDINFO 2017|
|Subtitle of host publication||Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics|
|Editors||Zhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine|
|Number of pages||5|
|State||Published - 2017|
|Event||16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China|
Duration: Aug 21 2017 → Aug 25 2017
|Name||Studies in Health Technology and Informatics|
|Other||16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017|
|Period||8/21/17 → 8/25/17|
Bibliographical noteFunding 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.
- Patient safety