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Supplemental material, Supplement_File_01_-_Survey_Instrument_3 for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes by Dinesh Pal Mudaranthakam, Colin Cernik, Leslie Curtis, Blake Griffith, Jinxiang Hu, Jo Wick, Jeffrey Thompson, Byron Gajewski, Devin Koestler, Roy A. Jensen and Matthew S. Mayo in Journal of Pharmacy Technology

Supplemental material, Supplement_File_02_-_Survey_Response_2 for Utilization of Technology to Improve Efficiency in Investigational Drug Management Processes by Dinesh Pal Mudaranthakam, Colin Cernik, Leslie Curtis, Blake Griffith, Jinxiang Hu, Jo Wick, Jeffrey Thompson, Byron Gajewski, Devin Koestler, Roy A. Jensen and Matthew S. Mayo in Journal of Pharmacy Technology

Objective: Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the relevant information is stored in an unstructured format that makes it difficult to use. Natural language processing approaches that attempt to automatically classify the data depend on vectorization algorithms that impose structure on the text, but these algorithms were not designed for the unique characteristics of EHR. Here, we propose a new algorithm for structuring so-called free-text that may help researchers make better use of EHR. We call this method Relevant Word Order Vectorization (RWOV). Materials and Methods: As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center during a recent year, from the unstructured text of pathology reports. Our approach attempts to account for the semi-structured way that healthcare providers often enter information. We compared this approach to the ngrams and word2vec methods. Results: Our approach resulted in the most consistently high accuracy, as measured by F1 score and area under the receiver operating characteristic curve (AUC). Discussion: Our results suggest that methods of structuring free text that take into account its context may show better performance, and that our approach is promising. Conclusion: By using a method that accounts for the fact that healthcare providers tend to use certain key words repetitively and that the order of these key words is important, we showed improved performance over methods that do not.

Supplemental material, suppl_file_xyz277344ddbed66 for Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD) by Dinesh Pal Mudaranthakam, Elena Shergina, Michele Park, Jeffrey Thompson, David Streeter, Jinxiang Hu, Jo Wick, Byron Gajewski, Devin C Koestler, Andrew K Godwin, Roy A Jensen and Matthew S Mayo in Cancer Informatics

Supplemental material, suppl_file_xyz277344ddbed66 for Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD) by Dinesh Pal Mudaranthakam, Elena Shergina, Michele Park, Jeffrey Thompson, David Streeter, Jinxiang Hu, Jo Wick, Byron Gajewski, Devin C Koestler, Andrew K Godwin, Roy A Jensen and Matthew S Mayo in Cancer Informatics