What is she best known for?
The fields of study she is best known for:
- Statistics
- Artificial intelligence
- Internal medicine
The scientist’s investigation covers issues in Health informatics, Data science, Health care, Logistic regression and Artificial intelligence.
Her Health informatics research includes themes of Data access, Cloud computing, Biological data and Knowledge management.
Her Data science study incorporates themes from Field, Blockchain, Quality management, Translational bioinformatics and Big data.
Lucila Ohno-Machado combines subjects such as Control, Proportional hazards model and MEDLINE with her study of Health care.
The Logistic regression study combines topics in areas such as Risk assessment, Data mining, Data set and Intensive care medicine.
Her Artificial intelligence research incorporates themes from Set and Natural language processing.
Her most cited work include:
- Logistic regression and artificial neural network classification models: a methodology review (953 citations)
- Natural language processing: an introduction. (823 citations)
- The use of receiver operating characteristic curves in biomedical informatics (620 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Data science, Artificial intelligence, Health informatics, Data mining and Health care.
Her Data science research incorporates elements of Metadata, Data discovery, MEDLINE, Field and Big data.
As a part of the same scientific study, Lucila Ohno-Machado usually deals with the Artificial intelligence, concentrating on Logistic regression and frequently concerns with Receiver operating characteristic.
Lucila Ohno-Machado interconnects Informatics and Medical education in the investigation of issues within Health informatics.
Her research on Data mining frequently links to adjacent areas such as Set.
Her Health care study integrates concerns from other disciplines, such as Public relations and Internet privacy.
She most often published in these fields:
- Data science (19.11%)
- Artificial intelligence (16.99%)
- Health informatics (14.09%)
What were the highlights of her more recent work (between 2015-2021)?
- Data science (19.11%)
- Health care (13.32%)
- Data sharing (7.92%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Data science, Health care, Data sharing, Health informatics and Metadata.
Her Data science research includes themes of Precision medicine, MEDLINE, Blockchain, Interoperability and Big data.
As a part of the same scientific family, Lucila Ohno-Machado mostly works in the field of Blockchain, focusing on Decision support system and, on occasion, Quality.
Her Health care research integrates issues from Psychological intervention, Emergency department, Informatics and Public relations.
Her study in Data sharing is interdisciplinary in nature, drawing from both Family medicine, Information sensitivity and Information privacy, Internet privacy.
As part of her studies on Health informatics, Lucila Ohno-Machado often connects relevant areas like Artificial intelligence.
Between 2015 and 2021, her most popular works were:
- Pan-cancer analysis of whole genomes (538 citations)
- Blockchain distributed ledger technologies for biomedical and health care applications (352 citations)
- Blockchain distributed ledger technologies for biomedical and health care applications (352 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Internal medicine
Lucila Ohno-Machado focuses on Data science, Data sharing, Health care, Blockchain and Logistic regression.
Her biological study spans a wide range of topics, including Precision medicine, Health information exchange, Interoperability, Cloud computing and Health records.
Her study in the field of Health informatics also crosses realms of Race.
The various areas that Lucila Ohno-Machado examines in her Health informatics study include Systematic review and Artificial intelligence.
The concepts of her Artificial intelligence study are interwoven with issues in Predictive modelling and Natural language processing.
Her research in Logistic regression intersects with topics in Information leakage, Data mining and Receiver operating characteristic.
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