2006 - Fellow of the Indian National Academy of Engineering (INAE)
2005 - Member of the National Academy of Medicine (NAM)
1992 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For pioneering the application of Artificial Intelligence in Medicine.
His main research concerns Artificial intelligence, Medical record, Health informatics, Confidentiality and Machine learning. He has researched Artificial intelligence in several fields, including Protected health information and Natural language processing. As a part of the same scientific study, Peter Szolovits usually deals with the Medical record, concentrating on Translational research and frequently concerns with Medical prescription, Medical physics and Identification.
His research integrates issues of Schedule and Expert system in his study of Health informatics. His study in the field of Health Insurance Portability and Accountability Act also crosses realms of Electronic medical record. His studies in Machine learning integrate themes in fields like Probabilistic logic, Reasoning system and Deductive reasoning.
Peter Szolovits spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Medical record and Task. His Artificial intelligence research includes elements of Domain and Context. His study connects Data mining and Machine learning.
His Medical record research incorporates elements of Confidentiality, Disease and Medical emergency. His Disease research is multidisciplinary, incorporating perspectives in Surgery and Cohort. His Cohort study combines topics in areas such as Odds ratio, Immunology and Confounding.
Peter Szolovits mainly investigates Artificial intelligence, Natural language processing, Machine learning, Artificial neural network and Task. His research on Artificial intelligence often connects related areas such as Context. His biological study spans a wide range of topics, including Test and Domain.
The Machine learning study combines topics in areas such as Field, Scalability and Medical record. In his study, User interface and Transfer of learning is inextricably linked to Named-entity recognition, which falls within the broad field of Artificial neural network. His study focuses on the intersection of Task and fields such as Convolutional neural network with connections in the field of SemEval and Ranking.
Peter Szolovits focuses on Artificial intelligence, Machine learning, Natural language processing, Artificial neural network and Intensive care medicine. His Artificial intelligence study frequently draws parallels with other fields, such as Context. His work carried out in the field of Machine learning brings together such families of science as Scalability, Throughput and Medical record.
Many of his research projects under Natural language processing are closely connected to X-ray report with X-ray report, tying the diverse disciplines of science together. His Intensive care medicine study also includes
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.
What Is a Knowledge Representation
Randall Davis;Howard E. Shrobe;Peter Szolovits.
Ai Magazine (1993)
MIMIC-III, a freely accessible critical care database
Alistair E.W. Johnson;Tom J. Pollard;Lu Shen;Li-wei H. Lehman.
Scientific Reports (2016)
Categorical and probabilistic reasoning in medical diagnosis
Peter Szolovits;Stephen G. Pauker.
Artificial Intelligence (1990)
Genetic Misdiagnoses and the Potential for Health Disparities
Arjun K. Manrai;Birgit H. Funke;Heidi L. Rehm;Morten S. Olesen.
The New England Journal of Medicine (2016)
Evaluating the State-of-the-Art in Automatic De-identification
Özlem Uzuner;Yuan Luo;Peter Szolovits.
Journal of the American Medical Informatics Association (2007)
Position paper: The coming of age of artificial intelligence in medicine
Vimla L. Patel;Edward H. Shortliffe;Mario Stefanelli;Peter Szolovits.
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge.
Catherine A. Brownstein;Alan H. Beggs;Nils Homer;Barry Merriman.
Public standards and patients' control: how to keep electronic medical records accessible but private.
Kenneth D Mandl;Peter Szolovits;Isaac S Kohane.
Automated de-identification of free-text medical records
Ishna Neamatullah;Margaret M Douglass;Li-wei H Lehman;Andrew Tomas Reisner.
BMC Medical Informatics and Decision Making (2008)
Implementing electronic medical record systems in developing countries.
Hamish S F Fraser;Paul Biondich;Deshen Moodley;Sharon Choi.
Journal of innovation in health informatics (2005)
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