What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Natural language processing
- Internal medicine
Carol Friedman focuses on Artificial intelligence, Natural language processing, Information retrieval, Domain and Pharmacovigilance.
His study looks at the relationship between Artificial intelligence and fields such as Machine learning, as well as how they intersect with chemical problems.
His Natural language processing research is multidisciplinary, incorporating perspectives in XML, Clinical information and Ambiguity.
Carol Friedman has included themes like Text mining and Text processing in his Information retrieval study.
His work investigates the relationship between Domain and topics such as Controlled vocabulary that intersect with problems in Semantics.
His Pharmacovigilance research incorporates elements of Data mining and Health records.
His most cited work include:
- A General Natural-language Text Processor for Clinical Radiology (595 citations)
- GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. (482 citations)
- Automated encoding of clinical documents based on natural language processing. (414 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Natural language processing, Information retrieval, Unified Medical Language System and Data science.
His study in Artificial intelligence focuses on Information extraction in particular.
His research in Natural language processing intersects with topics in Controlled vocabulary and Semantics.
His Information retrieval research includes elements of Annotation, Cluster analysis, Recall and Encoding.
His Unified Medical Language System study combines topics from a wide range of disciplines, such as Abbreviations as Topic, Classifier, Ambiguity and Semantic network.
His Data science research is multidisciplinary, relying on both Data mining and Pharmacovigilance.
He most often published in these fields:
- Artificial intelligence (52.44%)
- Natural language processing (48.89%)
- Information retrieval (23.11%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (52.44%)
- Pharmacovigilance (9.78%)
- Medical emergency (5.33%)
In recent papers he was focusing on the following fields of study:
Carol Friedman mostly deals with Artificial intelligence, Pharmacovigilance, Medical emergency, Intensive care medicine and Drug reaction.
The various areas that Carol Friedman examines in his Artificial intelligence study include Regular expression, Biomedicine and Natural language processing.
The various areas that Carol Friedman examines in his Natural language processing study include Mental health, Health informatics and Substance abuse.
His Pharmacovigilance research is multidisciplinary, incorporating elements of Drug development and Data mining.
His study explores the link between Medical emergency and topics such as Point of care that cross with problems in Information retrieval and Evidence-based practice.
His study on Intensive care medicine also encompasses disciplines like
- Confounding, which have a strong connection to Research design, Comorbidity and Clinical information,
- Pharmacogenomics and related Scoring algorithm, Pharmacogenomic Testing, Efficacy and Patient education.
Between 2013 and 2020, his most popular works were:
- Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality (136 citations)
- Similarity-based modeling in large-scale prediction of drug-drug interactions (91 citations)
- Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Database
Carol Friedman mainly focuses on Pharmacovigilance, Data mining, Internal medicine, Artificial intelligence and Drug development.
His studies deal with areas such as Observational study, Electronic health record, Healthcare data, Data science and Receiver operating characteristic as well as Pharmacovigilance.
In his research, Logistic regression, Confounding Factors and Clinical information is intimately related to Drug reaction, which falls under the overarching field of Data mining.
The Case-control study research Carol Friedman does as part of his general Internal medicine study is frequently linked to other disciplines of science, such as Metformin, therefore creating a link between diverse domains of science.
Carol Friedman interconnects Biomedicine and Reading in the investigation of issues within Artificial intelligence.
His Drug development research incorporates themes from Social media and Adverse effect, Adverse Event Reporting System.
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