Biomedical information is often captured in clinical notes or literatures in natural human language (instead of using structured and standardized formats). The field of information retrieval is concerned with identifying documents within a large collection that are most relevant to a user’s search. We provide consultations for research utilizing natural language processing (NLP) or text mining methods to analyze and understand the information within natural language documents.
Medical/Biological Literature-Based Extraction
- Extraction of published evidences from medical or biological literatures (e.g., protein interactions, drug-disease treatment or preventative relations).
Clinical Information Extraction and Retrieval
- Extraction of coded information from unstructured narratives (e.g., ejection fraction)
- Human-computer information retrieval using interactive text visualization
Daniel Harris, PhD
Assistant Professor, Department of Pharmacy Practice & Science
Specialities: Applied natural language processing for clinical data warehousing
Ramakanth Kavuluru, PhD
Associate Professor, Division of Biomedical Informatics, Department of Internal Medicine
Specialities: Extracting strong predictive signal by merging both structured and unstructured data sources from EMRs, scientific literature, and social media
Sujin Kim, PhD
Assistant Professor, Department of Pathology and Laboratory Medicine
Specialities: Analyzing large corpus of electronic medical notes using conventional NLP tools.