Elayavilli, Ravikumar Komandur

Background: Concerns for side-effects makes the identification of treatment targets of hypertension in the very elderly challenging. We sought to utilize natural language processing (NLP) to evaluate the prevalence of prespecified side-effects in a cohort of very elderly (≥80 years) patients. Methods and Results: We conducted a two-year, cross-sectional study involving 5169 very elderly patients with and without hypertension seen by primary or specialist providers. Hypertension patients had to have a prior hypertension ICD-9 code in the electronic medical records problem list and ≥2 office visits in 2008 and 2009. We used NLP to analyze extracted free text reports from last clinic visits between 2008 and 2009. Of the 10 prespecified side-effects, hypotension, fatigue, and dizziness were the commonest three conditions and hypotension was significantly higher in the HTN patients. Of the 2835 patients with no prior hypertension history and 2334 hypertension patients identified, the prevalence of each prespecified item in both groups were: acute renal failure (0.3% v 0.6%, p=0.13); syncope (2.8% v 2.2%, p = 0.17), dizziness (3.1% v 4.0%, p=0.09), history of fall (1.0% v 0.9%, p=0.65), fatigue (5.4% v 4.5%, p=0.19), femoral neck fracture (0.18% v 0.04%, p=0.16), palpitations (1.2% v 1.5%, p=0.35), hypotension (4.1% v 6.6%, p<0.001), hyperkalemia (1.1% v 1.7%, p=0.04), hypokalemia (0.4% v 0.9%, p=0.01). At least one presumed side-effect was present in 17.5% of all 5169 patients. Among hypertension patients, there was no significant difference in the prevalence of the side-effects among patients with BP above (578) or below 140/90 (1756) except for acute renal failure (1.21% vs 0.40%, p = 0.03). Conclusion: Analysis of free-text outpatient notes using NLP showed that hypotension and potassium abnormalities were significantly more prevalent in very elderly patients with hypertension compared to similarly aged patients with no hypertension history.

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.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.