What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Internal 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.
His most cited work include:
- MIMIC-III, a freely accessible critical care database (2089 citations)
- What Is a Knowledge Representation (579 citations)
- Categorical and probabilistic reasoning in medical diagnosis (387 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (37.16%)
- Natural language processing (18.58%)
- Machine learning (13.18%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (37.16%)
- Natural language processing (18.58%)
- Machine learning (13.18%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- MIMIC-III, a freely accessible critical care database (2089 citations)
- Genetic Misdiagnoses and the Potential for Health Disparities (341 citations)
- De-identification of patient notes with recurrent neural networks. (170 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
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
- Psychological intervention, which have a strong connection to Intervention and Intensive care,
- State space which connect with Clinical events and Vital signs.
He interconnects Length of hospitalization, Database, Sepsis, Diagnosis code and Reinforcement learning in the investigation of issues within Intensive care.
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