Serguei V S Pakhomov
Affiliation: Mayo Clinic
- Developing a corpus of clinical notes manually annotated for part-of-speechSerguei V Pakhomov
Division of Biomedical Informatics, Mayo College of Medicine, Rochester, MN 55905, USA
Int J Med Inform 75:418-29. 2006..We describe and discuss the process of training three domain experts to perform linguistic annotation...
- Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniquesSerguei V S Pakhomov
Division of Biomedical Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
J Am Med Inform Assoc 13:516-25. 2006..Human classification of diagnoses is a labor intensive process that consumes significant resources. Most medical practices use specially trained medical coders to categorize diagnoses for billing and research purposes...
- Electronic medical records for clinical research: application to the identification of heart failureSerguei Pakhomov
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
Am J Manag Care 13:281-8. 2007..To identify patients with heart failure (HF) by using language contained in the electronic medical record (EMR)...
- The role of the electronic medical record in the assessment of health related quality of lifeSerguei V S Pakhomov
Pharmaceutical Care and Health Systems, University of Minnesota, Minneapolis, MN, USA
AMIA Annu Symp Proc 2011:1080-8. 2011..Our method represents an efficient and scalable surrogate measure of HRQOL to predict healthcare spending in ambulatory diabetes patients...
- A corpus driven approach applying the "frame semantic" method for modeling functional status terminologyAlexander P Ruggieri
Division of Medical Informatics Research, Department of Health Sciences Research, Harwick 826, Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA
Stud Health Technol Inform 107:434-8. 2004....
- Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifierSerguei V Pakhomov
Division of Biomedical Informatics, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA
J Biomed Inform 38:145-53. 2005..86%) but worse accuracy than Perceptron (57 vs. 65%). Both algorithms perform better than the baseline with recall on positive samples of 71% and accuracy of 54%...
- UMLS-Interface and UMLS-Similarity : open source software for measuring paths and semantic similarityBridget T McInnes
University of Minnesota, Minneapolis, MN, USA
AMIA Annu Symp Proc 2009:431-5. 2009..Our frameworks constitute a significant contribution to the field of biomedical Natural Language Processing by providing a common development and testing platform for semantic similarity measures based on the UMLS...
- Automatic classification of foot examination findings using clinical notes and machine learningSerguei V S Pakhomov
Department of Pharmaceutical Care and Health Systems, University of Minnesota, Twin Cities, MN, USA
J Am Med Inform Assoc 15:198-202. 2008..This application may improve quality and safety by providing inexpensive and scalable methodology for quality and risk factor assessments at the point of care...
- Using compound codes for automatic classification of clinical diagnosesSerguei V Pakhomov
Division of Medical Informatics Research, Department of Health Sciences Research, Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA Pakhomov serguei mayo edu
Stud Health Technol Inform 107:411-5. 2004..A small improvement (3%) with using compound categories over simple categories indicates that using multiple code categories is a promising solution, although clearly in need of further research and refinement...
- Measures of semantic similarity and relatedness in the biomedical domainTed Pedersen
Department of Computer Science, 1114 Kirby Drive, University of Minnesota, Duluth, MN 55812, USA
J Biomed Inform 40:288-99. 2007..We conclude that there is a role both for more flexible measures of relatedness based on information derived from corpora, as well as for measures that rely on existing ontological structures...