Biomedical Informatics Research

Biomedical informatics (BMI) is the interdisciplinary, scientific field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision-making, motivated by efforts to improve human health. The Biomedical Informatics (BMI) Core provides access to biomedical informatics expertise, technologies, and data management platforms to CCTS and their investigators. The CCTS BMI team integrates the CCTS BMI Core, Division of Biomedical Informatics (DBMI) and the Institute for Pharmaceutical Outcomes and Policy (IPOP) housed in the College of Pharmacy.

Research Areas of Focus

To schedule a consultation and possible collaboration with BMI faculty in the research areas listed below, please complete a Biomedical Informatics Service Request Form.

 

Data Analytics/Visualizations

Data Analytics/Visualizations, is the study of visual representations of abstract data using methods designed to focus the user’s ability to understand large amounts of data at once.

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Ontology-guided Multi-center Clinical Research Data Integration

Development of novel, flexible informatics methodologies, tools and infrastructure to  facilitate the collection, management, and analysis of clinical, physiological, and genomic data.  User-centered development approach and incorporation of visual, ontological, searchable  and explorative features in three interrelated components: Query Builder, Query Manager and Query Explorer. Integration of electrophysiological data from multiple sites. Construction of the National Sleep Research Resource.

Electronic Data Capture

We develop  complementary data extraction and presentation tools, such as OPIC and OnWARD.  These tools use an ontology-driven, secure, rapidly-deployed, web-based framework to support data capture for large-scale multi-center clinical research. Our overall approach is developed using the agile methodology  to provide a flexible, user-centered dynamic form generator, which can be quickly deployed and customized  for any clinical study without the need of deep technical expertise. Because of the flexible framework, these data management system can be extended to accommodate a large variety of data types, including genetic, genomic and proteomic data.

 

Knowledge Discovery

Knowledge discovery describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. Knowledge discovery developed out of the data mining domain, and is closely related to it both in terms of methodology and terminology.

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Knowledge Organization

Knowledge Organization (KO) is a field of information science that studies information representation and retrieval by analyzing content matter and classifying information-bearing entities for optimal retrieval purposes.

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Health Literacy

Health Literacy (HL) is the capability to read, understand and use health information to promote informed healthcare decisions. With the astounding volume of health information and advanced health IT, individuals are now facing the biggest challenge to collect, store, and organize personal health information for their own care.

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Data Mining

Data Mining: Biomedical Data Mining is an area in informatics that involves identifying patterns and discovering new knowledge in biomedical datasets (numerical/categorical sets, sequences, or graphs) through computational methods involving machine learning or statistical predictive modeling. The fundamental tasks in data mining involve clustering, statistically significant item set (sequence or graph)  or rule mining, and classification.

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Natural Language Processing

Natural Language Processing (NLP): NLP is the process of converting textual data into, ideally, 'actionable' information. But, often, it also includes converting unstructured text into structured data that is more straightforward to process using computers. NLP involves extracting information from free text that can help researchers in biological, medical, and clinical domains to answer new questions and expedite the discovery process; also assist hospitals in providing better healthcare to patients and their families. The extracted information can be used for solving other problems such as biomedical information retrieval, augmenting and auditing standard vocabularies, clinical cohort selection, quality control, and decision.

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Biomedical Ontology Quality Assurance

Development of methods and tools for exhaustive analysis and curation of large biomedical ontologies such as SNOMED CT and FMA. Methods and tools include: lattice-based structural auditing, cycle detection, relation reversal detection, self-similarity, cross-terminological system alignment, search and visualization interfaces for ontological systems.