What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Machine learning
Guergana Savova mostly deals with Natural language processing, Artificial intelligence, Data science, Information retrieval and Informatics.
His Natural language processing study incorporates themes from Domain, Cancer, Information management and SemEval.
He interconnects Machine learning, Breast cancer and Measure in the investigation of issues within Artificial intelligence.
His Data science research is multidisciplinary, relying on both Algorithm, Decision support system and Medical record.
His work deals with themes such as Biobank, Observational study, Pharmacogenomics and Clinical pharmacology, which intersect with Decision support system.
His Information retrieval study integrates concerns from other disciplines, such as Annotation, Text corpus and Parsing.
His most cited work include:
- Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications (1251 citations)
- Extracting information from textual documents in the electronic health record: a review of recent research. (621 citations)
- Overview of the ShARe/CLEF eHealth Evaluation Lab 2013 (202 citations)
What are the main themes of his work throughout his whole career to date?
Guergana Savova mainly investigates Artificial intelligence, Natural language processing, Information retrieval, Medical record and Information extraction.
His research in Artificial intelligence focuses on subjects like Relation, which are connected to SemEval.
He works in the field of Natural language processing, namely Unified Medical Language System.
His Information retrieval study combines topics in areas such as Semantics, Named-entity recognition and Set.
His Medical record research includes themes of Gold standard, Observational study, Disease and Biobank.
In most of his Information extraction studies, his work intersects topics such as Component.
He most often published in these fields:
- Artificial intelligence (57.43%)
- Natural language processing (50.00%)
- Information retrieval (14.85%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (57.43%)
- Aneurysm (7.43%)
- Natural language processing (50.00%)
In recent papers he was focusing on the following fields of study:
Guergana Savova mainly focuses on Artificial intelligence, Aneurysm, Natural language processing, Subarachnoid hemorrhage and Internal medicine.
His Artificial intelligence research incorporates elements of Machine learning and Medical record.
When carried out as part of a general Natural language processing research project, his work on Information extraction is frequently linked to work in Thriving, therefore connecting diverse disciplines of study.
The various areas that Guergana Savova examines in his Information extraction study include Component and Jargon.
In his study, Predictive value, Chronic condition and Emergency medicine is strongly linked to Logistic regression, which falls under the umbrella field of Subarachnoid hemorrhage.
His Internal medicine research incorporates themes from Gastroenterology and Cardiology.
Between 2017 and 2021, his most popular works were:
- Clinical Natural Language Processing in languages other than English: opportunities and challenges (66 citations)
- A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction (30 citations)
- Association between aspirin dose and subarachnoid hemorrhage from saccular aneurysms: A case-control study (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Machine learning
His primary areas of study are Artificial intelligence, Subarachnoid hemorrhage, Natural language processing, Aneurysm and Internal medicine.
His Artificial intelligence research focuses on Medical record and how it relates to Neurosurgery.
Guergana Savova has researched Subarachnoid hemorrhage in several fields, including Computed tomography angiography, Radiology and Middle cerebral artery.
He has included themes like Cancer, Encoder and Transformer in his Natural language processing study.
His Aneurysm research includes elements of Odds ratio, Receiver operating characteristic and Confidence interval.
His study looks at the relationship between Internal medicine and topics such as Cardiology, which overlap with Ezetimibe, Cholesterol and Lipoprotein.
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