Machine learning approaches classify clinical malaria outcomes based on haematological parameters.
Collins M Morang'aLucas Amenga-EtegoSaikou Y BahVincent AppiahDominic S Y AmuzuNicholas AmoakoJames AbugriAbraham R OduroAubrey J CunningtonGordon A AwandareThomas Dan OttoPublished in: BMC medicine (2020)
The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.
Keyphrases
- clinical decision support
- machine learning
- liver failure
- electronic health record
- respiratory failure
- drug induced
- aortic dissection
- deep learning
- artificial intelligence
- hepatitis b virus
- big data
- adipose tissue
- combination therapy
- current status
- urinary tract infection
- metabolic syndrome
- weight loss
- replacement therapy
- extracorporeal membrane oxygenation
- acute respiratory distress syndrome