Lessons Learned in the Development of a Computable Phenotype for Response in Myeloproliferative Neoplasms

IEEE Int Conf Healthc Inform. 2018 Jun:2018:328-331. doi: 10.1109/ICHI.2018.00045. Epub 2018 Jul 26.

Abstract

Determining response status in patients with myeloproliferative neoplasms is a complex problem requiring the integration of both structured and unstructured data elements from disparate information systems. By applying multiple techniques, a collaborative team of informatics professionals and research personnel were able to determine which elements were amenable to automated extraction and which required expert adjudication. With this knowledge in mind, we were able to build a system that joins together programmatically-derived and manually-abstracted data elements to facilitate response assessment - an important end point in clinical and translational research in this disease area.

Keywords: cancer; clinical research informatics; computable phenotype; data mining; natural language processing; secondary use.