The Niland Lab encompasses expertise in clinical research informatics and biostatistics. We are advancing informatics methodology to be able to extract data from contextual documents.
In one current informatics project, the Niland Lab is evaluating optical character recognition software to convert scanned paper documents into minable data that can be used to streamline the data abstraction process and expedite data capture for clinical research projects.
Two additional lab research projects focus on utilizing NLP to parse information from text-based clinical documents in the electronic medical record. One project developed NLP queries to extract cancer recurrence data, a notoriously complicated concept due to semantic interpretation buried deep in clinical notes. The work developed by this project is important for facilitating clinical data abstraction for information that is often not routinely collected in a standardized manner, even though it is often required for determinants of patient outcomes and survival.
A second NLP project examines the portability and reusability of NLP queries across institutions using a process which we developed, dubbed “Iterative Interactive Enrichment.” The results of this project support the idea that NLP queries can be developed generically so that they can be shared across institutions, resulting in portability, extensibility and improved accuracy.
Using their skillset in informatics and biostatistics, members of the Niland Lab team have contributed to a number of studies in oncology based research. Focusing on lymphoma, the Niland Lab is working closely with Dennis Weisenburger, M.D., and Joo Y. Song, M.D., of City of Hope’s Department of Pathology using data from tissue samples to identify connections between certain genetic mutations and patient outcomes.
The Niland Lab has collaborated on several breast and gynecological cancer studies alongside colleagues in the Department of Surgery. One paper published the Journal of Surgical Research identified factors that influence those with early-stage, hormone-positive disease to decline chemotherapy. Another publication in the Annals of Surgical Oncology examined the predictive power of a tumor response ratio related to outcomes of breast cancer patients receiving neoadjuvant chemotherapy.
A current study collaborating with John Yim, M.D., associate professor of surgery, compares recurrence and survival data between the nipple-sparing mastectomy and more radical surgeries. Additional lab activity includes research stemming from the clinical outcomes of ovarian cancer patients undergoing surgical cytoreduction, with the database developed with Thanh Dellinger, M.D., assistant professor of surgery.