What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Database
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.
His most cited work include:
- Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports (191 citations)
- Hospital Workload and Adverse Events (189 citations)
- Forecasting daily patient volumes in the emergency department. (160 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (21.70%)
- Decision support system (14.62%)
- Data mining (12.74%)
What were the highlights of his more recent work (between 2012-2021)?
- Clinical decision support system (12.26%)
- Artificial intelligence (21.70%)
- Bayesian probability (7.55%)
In recent papers he was focusing on the following fields of study:
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.
Between 2012 and 2021, his most popular works were:
- Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium (73 citations)
- Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods (21 citations)
- A study of the transferability of influenza case detection systems between two large healthcare systems. (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Database
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.
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