Information Retrieval: A Health and Biomedical Perspective, Third Edition

William Hersh, M.D.

• Automated de-identification of records (Uzuner et al., 2007)
• Identification of smoking status from medical discharge summaries (Uzuner et al., 2008)
• Identification of obesity and its co-morbidities (Uzuner, 2009)
• Extracting medication information (Uzuner et al., 2010)
• Relationships between concepts (entities) in clinical text (Uzuner et al., 2011)
• Co-reference resolution and sentiment classification (Uzuner et al., 2012)
• Detection of temporal relations (Sun et al., 2013)
• De-identification and risk factor detection (Uzuner and Stubbs, 2015)
• Classify symptom severity in a domain for a patient, based on initial psychiatric evaluation (2016 task - no publications yet)
  

• Identifying QT prolongation in ECG reports (Denny, 2009)
  
• Replicating finding of known gene-disease associations from research data in EHR data for several diseases (Denny et al., 2010; Ritchie et al., 2010)
• Discovering new gene-disease associations (Denny et al., 2010)
• Identifying genomic variants associated with atrioventricular conduction abnormalities (Denny et al., 2010), red blood cell traits (Kullo et al., 2010), thyroid disorders (Denny et al., 2011), white blood cell count abnormalities (Crosslin et al, 2012)
• Identification of patients needing colorectal cancer testing (Denny et al., 2012)

• Multisite validation improves accuracy pf the phenotype algorithm
• Validation targets must be carefully considered and defined
• Specification of time frames for variables makes validation easier and improves accuracy
• Using repeated measures requires defining the relevant time period and specifying the most meaningful value to be studied
• Patient movement in and out of health systems can result in incomplete or fragmented data
• The review scope should be defined carefully
• Care is required in combining EMR and research data
• Medication data is best assessed using claims, medications dispensed, or medications prescribed
• Algorithm development and validation work should be an iterative process
• Validation by content experts or structured chart review is critical for accurate results

• Negation detection (Chapman et al., 2001) – developed the NegEx system to detect negation in clinical charts
• Syndromic surveillance of emergency department chief complaints (Chapman et al., 2005) – achieved low sensitivity but high specificity
• Description of the system of Denny et al. used in the eMERGE Project (Denny et al., 2005)
  
• Completeness of findings in spinal disability exams (Brown et al., 2006)
• Clinical research – finding patients with congestive heart failure (Pakhomov et al., 2007) and classifying foot examination results in patients with diabetes (Pakhomov et al., 2008)

• Extracting cancer disease characteristics from pathology reports (Coden et al., 2009)
• Identifying section headers in clinical documents (Denny et al., 2009)
• Determining aspirin use in patients with cardiovascular disease (Pakhomov et al., 2010)
• Improved information retrieval using concept associations in records (Hristidis et al., 2010)
• Processing emergency department notes for syndromic surveillance (Gerbier et al., 2011)
• Identification of post-operative complications in clinical notes (Murff et al., 2011)
• Identification of influenza for biosurveillance from encounter notes (Elkin et al., 2012)
• Detection of Charlton comorbidities (Singh et al., 2012)
• Discovery of postoperative complications (Fitzhenry et al., 2013; Tien et al., 2015)
• Identification of drug and food allergies entered using non-standard terminology (Epstein et al., 2013)
• Information extraction from narrative pathology reports and automatic population of structured templates (Ou and Partrick, 2014)
• Novel associations between ICD-9 codes in clinical data and those recognized by MetaMap in MEDLINE records (Hanauer et al., 2014)
  
• High-throughput phenotyping (Yu et al., 2015)
• Case detection of diabetes (Zheng et al., 2016)
• Identification of high-risk heart failure patients (Evans et al., 2016)
• Diseases associated with HLA variants (Karnes et al., 2017)
• Correlation of mammographic and pathologic findings in clinical decision support (Patel et al., 2017)
  

• Oncology (Spasić et al., 2014; Yim et al., 2016)
• Pathology (Burger et al., 2016)
• Radiology (Pons et al., 2016)

• Chemical and Drug Named Entity Recognition (CHEMDNER) - Detection of mentions of chemical compounds and drugs, in particular those chemical entity mentions that can subsequently be linked to a chemical structure (Krallinger et al., 2015; Krallinger et al., 2015)
  
• Gene Ontology (GO) curation - Development of automatic methods to aid GO curators in identifying articles with curatable GO information (triage) and extracting gene function terms and the associated evidence sentences in full-length articles (Mao et al., 2014)
  

• CHEMDNER patents - Identification of chemical compounds and of relevant biological context in patents (Krallinger et al., 2015)
  
• Chemical-disease relation (CDR) task - Automatic detection of chemical/drugs and diseases, and their relations in PubMed abstracts. In particular, the CDR task focuses on extracting the relationship of drug-induced diseases (Wei et al., 2016)
  

• Mining protein interactions and mutations for precision medicine - identifying and extracting protein-protein interactions affected by mutations described in the biomedical literature
• Text mining chemical-protein interactions
  

• 2009 - The main task was core event extraction, where participants were required to identify events concerning given proteins. Two additional optional events included event enrichment and negation and speculation recognition.
• 2011 - The main task continued to be event extraction.
  
• 2013 - The main task consisted of six types of event extraction: Genia Event Extraction for NFkB knowledge base construction, Cancer Genetics, Pathway Curation, Corpus Annotation with Gene Regulation Ontology, Gene Regulation Network in Bacteria, and Bacteria Biotopes.
  
• 2016 - The main task consisted of three types of event extraction:Genetic and molecular mechanisms involved in plant seed development; Bacteria Biotope, bacteria locations and normalization with an ontology; and NFkB Knowledge base construction.

• Content adaptation - acquiring and modeling new content
• Training adaptation - adding and learning from new question types
• Functional adaptation - adapting question analysis, hypothesis scoring, and new functionality specific to the domain