The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.

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.

Discovering associations between problem list and practice setting.

Background: The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients' outcomes. One of the core criteria for "Meaningful Use" of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients.

Standardizing Heterogeneous Annotation Corpora Using HL7 FHIR for Facilitating their Reuse and Integration in Clinical NLP.

Manually annotated clinical corpora are commonly used as the gold standards for the training and evaluation of clinical natural language processing (NLP) tools. The creation of these manual annotation corpora, however, is both costly and time-consuming. There is an emerging need in the clinical NLP community for reusing existing annotation corpora across different clinical NLP tasks.

Rare disease knowledge enrichment through a data-driven approach.

Background: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR).

Extracting chemical-protein relations using attention-based neural networks.

Relation extraction is an important task in the field of natural language processing. In this paper, we describe our approach for the BioCreative VI Task 5: text mining chemical-protein interactions. We investigate multiple deep neural network (DNN) models, including convolutional neural networks, recurrent neural networks (RNNs) and attention-based (ATT-) RNNs (ATT-RNNs) to extract chemical-protein relations.

A comparison of word embeddings for the biomedical natural language processing.

Background: Word embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the ability of the vector representations being able to capture useful semantic properties and linguistic relationships between words. Different textual resources (e.g.

Detecting Pharmacovigilance Signals Combining Electronic Medical Records With Spontaneous Reports: A Case Study of Conventional Disease-Modifying Antirheumatic Drugs for Rheumatoid Arthritis.

Multiple data sources are preferred in adverse drug event (ADEs) surveillance owing to inadequacies of single source. However, analytic methods to monitor potential ADEs after prolonged drug exposure are still lacking. In this study we propose a method aiming to screen potential ADEs by combining FDA Adverse Event Reporting System (FAERS) and Electronic Medical Record (EMR).

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.

Background: Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost.

Clinical information extraction applications: A literature review.

Background: With the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.

Objectives: In this literature review, we present a review of recent published research on clinical information extraction (IE) applications.

Need of informatics in designing interoperable clinical registries.

Clinical registries are designed to collect information relating to a particular condition for research or quality improvement. Intuitively, informatics in the area of data management and extraction plays a central role in clinical registries. Due to various reasons such as lack of informatics awareness or expertise, there may be little informatics involvement in designing clinical registries.

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches.

Background: Self-management is crucial to diabetes care and providing expert-vetted content for answering patients' questions is crucial in facilitating patient self-management.

Objective: The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients.

Methods: We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum.

Populating Physician Biographical Pages Based on EMR Data.

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.

Systematic Analysis of Free-Text Family History in Electronic Health Record.

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.

Discovering adverse drug events combining spontaneous reports with electronic medical records: a case study of conventional DMARDs and biologics for rheumatoid arthritis.

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.

BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences.

Unlabelled: Extracting meaningful relationships with semantic significance from biomedical literature is often a challenging task. BioCreative V track4 challenge for the first time has organized a comprehensive shared task to test the robustness of the text-mining algorithms in extracting semantically meaningful assertions from the evidence statement in biomedical text. In this work, we tested the ability of a rule-based semantic parser to extract Biological Expression Language (BEL) statements from evidence sentences culled out of biomedical literature as part of BioCreative V Track4 challenge.

A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text.

Classification of drug-drug interaction (DDI) from medical literatures is significant in preventing medication-related errors. Most of the existing machine learning approaches are based on supervised learning methods. However, the dynamic nature of drug knowledge, combined with the enormity and rapidly growing of the biomedical literatures make supervised DDI classification methods easily overfit the corpora and may not meet the needs of real-world applications.

Leveraging word embeddings and medical entity extraction for biomedical dataset retrieval using unstructured texts.

The recent movement towards open data in the biomedical domain has generated a large number of datasets that are publicly accessible. The Big Data to Knowledge data indexing project, biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE), has gathered these datasets in a one-stop portal aiming at facilitating their reuse for accelerating scientific advances. However, as the number of biomedical datasets stored and indexed increases, it becomes more and more challenging to retrieve the relevant datasets according to researchers' queries.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states.

Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study.

Background: Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates.

'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities.

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce 'RE:fine Drugs', a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology.

BELTracker: evidence sentence retrieval for BEL statements.

Biological expression language (BEL) is one of the main formal representation models of biological networks. The primary source of information for curating biological networks in BEL representation has been literature. It remains a challenge to identify relevant articles and the corresponding evidence statements for curating and validating BEL statements.

MD-CTS: An integrated terminology reference of clinical and translational medicine.

New vocabularies are rapidly evolving in the literature relative to the practice of clinical medicine and translational research. To provide integrated access to new terms, we developed a mobile and desktop online reference-Marshfield Dictionary of Clinical and Translational Science (MD-CTS). It is the first public resource that comprehensively integrates Wiktionary (word definition), BioPortal (ontology), Wiki (image reference), and Medline abstract (word usage) information.

Operationalizing Semantic Medline for meeting the information needs at point of care.

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.

A Bibliometric Analysis on Cancer Population Science with Topic Modeling.

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.