Komandur Elayavilli, Ravikumar

Abstract Knowledge of the molecular interactions of biological and chemical entities and their involvement in biological processes or clinical phenotypes is important for data interpretation. Unfortunately, this knowledge is mostly embedded in the literature in such a way that it is unavailable for automated data analysis procedures. Biological expression language (BEL) is a syntax representation allowing for the structured representation of a broad range of biological relationships. It is used in various situations to extract such knowledge and transform it into BEL networks. To support the tedious and time-intensive extraction work of curators with automated methods, we developed the BEL track within the framework of BioCreative Challenges. Within the BEL track, we provide training data and an evaluation environment to encourage the text mining community to tackle the automatic extraction of complex BEL relationships. In 2017 BioCreative VI, the 2015 BEL track was repeated with new test data. Although only minor improvements in text snippet retrieval for given statements were achieved during this second BEL task iteration, a significant increase of BEL statement extraction performance from provided sentences could be seen. The best performing system reached a 32% F-score for the extraction of complete BEL statements and with the given named entities this increased to 49%. This time, besides rule-based systems, new methods involving hierarchical sequence labeling and neural networks were applied for BEL statement extraction.

Background: A clinical decision support system (CDSS) for cervical cancer screening identifies patients due for routine cervical cancer screening. Yet, high-risk patients who require more frequent screening or earlier follow-up to address past abnormal results are not identified. We aimed to assess the effect of a complex CDSS, incorporating national guidelines for high-risk patient screening and abnormal result management, its implementation to identify patients overdue for testing, and the outcome of sending a targeted recommendation for follow-up. Materials and Methods: At three primary care clinics affiliated with an academic medical center, a reminder recommending an appointment for Papanicolaou (Pap) testing or Pap and human papillomavirus cotesting was sent to high-risk women aged 18 through 65 years (intervention group) identified by CDSS as overdue for testing. Historical control patients, who did not receive a reminder, were identified by CDSS 1 year before the date when reminders were sent to the intervention group. Test completion rates were compared between the intervention and control groups through a generalized estimating equation extension. Results: Across the three sites, the average completion rate of recommended follow-up testing was significantly higher in the intervention group at 23.7% (61/257) than the completion rate at 3.3% (17/516) in the control group ( p < 0.001). Conclusions: A CDSS with enhanced capabilities to identify high-risk women due for cervical cancer testing beyond routine screening intervals, with subsequent patient notification, has the potential to decrease cervical precancer and cancer by improving adherence to guideline-compliant follow-up and needed treatment.