His main research concerns Data science, Health informatics, Information retrieval, Process and Data mining. In his study, Medical record, Concordance, Applications of artificial intelligence and Speech recognition is inextricably linked to Medical emergency, which falls within the broad field of Health informatics. He has included themes like Disadvantage, Coding, Test case and Medical diagnosis in his Information retrieval study.
His Data mining study integrates concerns from other disciplines, such as Spec#, Bayesian network and Information model. Artificial intelligence covers Peter J. Haug research in Bayesian network. His Probabilistic logic study in the realm of Artificial intelligence connects with subjects such as Naive Bayes classifier.
The scientist’s investigation covers issues in Artificial intelligence, Decision support system, Data mining, Expert system and Clinical decision support system. His Artificial intelligence research incorporates themes from Machine learning, Information retrieval and Natural language processing. The various areas that Peter J. Haug examines in his Decision support system study include Context, Pneumonia and Medical emergency.
The Data mining study combines topics in areas such as Bayesian network, Bayesian probability and Medical diagnosis. Peter J. Haug has researched Medical diagnosis in several fields, including Emergency department and Medical physics. His Expert system research is multidisciplinary, relying on both Data science and Knowledge base.
Peter J. Haug spends much of his time researching Clinical decision support system, Artificial intelligence, Bayesian probability, Outbreak and Emergency department. He interconnects Intensive care medicine, Software engineering and Pneumonia in the investigation of issues within Clinical decision support system. His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Key and Natural language processing.
In general Machine learning study, his work on Model selection often relates to the realm of Benchmark, thereby connecting several areas of interest. His studies in Emergency department integrate themes in fields like Sepsis, Medical diagnosis, Medical emergency, Severe sepsis and Receiver operating characteristic. His research in Decision support system intersects with topics in Structured interview, Health informatics and World Wide Web, Information sharing.
Peter J. Haug focuses on Artificial intelligence, Natural language processing, Parsing, Medical diagnosis and Emergency department. His research in the fields of Arden syntax and Clinical decision support system overlaps with other disciplines such as Systems architecture and Process. The study incorporates disciplines such as Disease surveillance, Precision and recall, Case detection, Healthcare system and Software portability in addition to Natural language processing.
His studies deal with areas such as Epidemiology, Bayesian probability, Bayes' theorem, Test case and Feature selection as well as Parsing. His Medical diagnosis study which covers Context that intersects with Risk assessment, Medical emergency, Guideline and Knowledge management. His Emergency department research includes elements of Severe sepsis, Septic shock and False positive rate.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports
M Fiszman;W W Chapman;D Aronsky;R S Evans.
Journal of the American Medical Informatics Association (2000)
Forecasting daily patient volumes in the emergency department.
Spencer S. Jones;Alun Thomas;R. Scott Evans;R. Scott Evans;Shari J. Welch.
Academic Emergency Medicine (2008)
Hospital Workload and Adverse Events
Joel S. Weissman;Jeffrey M. Rothschild;Eran Bendavid;Peter Sprivulis.
Medical Care (2007)
Population-based family history-specific risks for colorectal cancer: a constellation approach.
David P. Taylor;Randall W. Burt;Randall W. Burt;Marc S. Williams;Peter J. Haug.
The Arden Syntax for Medical Logic Modules.
Hripcsak G;Clayton Pd;Pryor Ta;Haug P.
annual symposium on computer application in medical care (1990)
Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation
Stéphane Meystre;Peter J. Haug.
Journal of Biomedical Informatics (2006)
Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data
Susan Rea;Jyotishman Pathak;Guergana Savova;Thomas A. Oniki.
Journal of Biomedical Informatics (2012)
Classifying free-text triage chief complaints into syndromic categories with natural languages processing
Wendy W. Chapman;Lee M. Christensen;Michael M. Wagner;Peter J. Haug.
Artificial Intelligence in Medicine (2005)
Accuracy of administrative data for identifying patients with pneumonia.
Dominik Aronsky;Peter J. Haug;Charles Lagor;Nathan C. Dean.
American Journal of Medical Quality (2005)
An Event Model of Medical Information Representation
Stanley M. Huff;Roberto A. Rocha;Bruce E. Bray;Homer R. Warner.
Journal of the American Medical Informatics Association (1995)
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