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Family history is an important component in modern clinical care especially in the era of precision medicine. Family history information in the Electronic Health Record (EHR) system is usually stored in structured format as well as in free-text format. In this study, we systematically analyzed a family history text corpus from 3 million clinical notes for the patients receiving their primary care at Mayo Clinic. Family members, medical problems, and their associations were analyzed and reported. Our findings showed a great agreement between positive/negated medical problems mentioned in the diagnosis report and the family history, as measured by observed agreement and random agreement. We also found that the family history of some medical problems existed up to 10~15 years prior to the diagnosis date of such problems. Finally two patient cases were studied to show the medical problems in the diagnosis and family history associated with the timeline.

The physicians' biographical pages are essential in providing information about physicians' specialties. However, physicians may not have biographical pages or the current pages are not comprehensive. We hypothesize that physicians' specialty information can be mined from Electronic Medical Records (EMRs) of their patients. We proposed an automated physician specialty populating (PSP) system that analyzes physician-ascertained diagnoses in EMRs, aggregates them to an appropriate granularity based on the current biographical pages, and populates the biographical pages accordingly. In this study, we applied the system using EMR data from Mayo Clinic and evaluated the system using the current biographical pages regarding various ranking strategies. Preliminary results demonstrated that using EMR data is a scalable and systematic way to populate physicians' biographical pages.

The use of multiple data sources has been preferred in the surveillance of adverse drug events due to shortcomings of using only a single source. In this study, we proposed a framework where the ADEs associated with interested drugs are systematically discovered from the FDA's Adverse Event Reporting System (AERS), and then validated through mining unstructured clinical notes from Electronic Medical Records (EMRs). This framework has two features. First, a higher priority was given to clinical practice during signal detection and validation. Second, the normalization by NLP facilitated the interoperation between AERS-DM and the EMR. To demonstrate this methodology, we investigated potential ADEs associated with drugs (class level) for rheumatoid arthritis (RA) patients. The results demonstrated the feasibility and sufficient accuracy of the framework. The framework can serve as the interface between the informatics domain and the medical domain to facilitate ADE discovery.

Bibliometric analysis is a research method used in library and information science to evaluate research performance. It applies quantitative and statistical analyses to describe patterns observed in a set of publications and can help identify previous, current, and future research trends or focus. To better guide our institutional strategic plan in cancer population science, we conducted bibliometric analysis on publications of investigators currently funded by either Division of Cancer Preventions (DCP) or Division of Cancer Control and Population Science (DCCPS) at National Cancer Institute. We applied two topic modeling techniques: author topic modeling (AT) and dynamic topic modeling (DTM). Our initial results show that AT can address reasonably the issues related to investigators' research interests, research topic distributions and popularities. In compensation, DTM can address the evolving trend of each topic by displaying the proportion changes of key words, which is consistent with the changes of MeSH headings.

Scientific literature is one of the popular resources for providing decision support at point of care. It is highly desirable to bring the most relevant literature to support the evidence-based clinical decision making process. Motivated by the recent advance in semantically enhanced information retrieval, we have developed a system, which aims to bring semantically enriched literature, Semantic Medline, to meet the information needs at point of care. This study reports our work towards operationalizing the system for real time use. We demonstrate that the migration of a relational database implementation to a NoSQL (Not only SQL) implementation significantly improves the performance and makes the use of Semantic Medline at point of care decision support possible.

The goal of this paper is to find relevant citations for clinicians' written content and make it more reliable by adding scientific articles as references and enabling the clinicians to easily update it using new information. The proposed approach uses information retrieval and ranking techniques to extract and rank relevant citations from MEDLINE for any given sentence. Additionally, this system extracts snippets of relevant content from ranked citations. We assessed our approach on 4,697 MEDLINE papers and their corresponding full-text on the subject of Heart Failure. We implemented multi-level and weight ranking algorithms to rank the citations. We demonstrate that using journal relevance and study design type improves results obtained from only using content similarity by approximately 40%. We also show that using full-text, rather than abstract text, leads to extracting higher quality snippets.