Gold Standard Evaluation of an Automatic HAIs Surveillance System.
Beatriz Villamarín-BelloBerta Uriel-LatorreFlorentino Fdez-RiverolaMaría Sande-MeijideDaniel Glez-PeñaPublished in: BioMed research international (2019)
Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).
Keyphrases
- electronic health record
- public health
- deep learning
- machine learning
- healthcare
- emergency department
- mental health
- immune response
- artificial intelligence
- big data
- risk assessment
- escherichia coli
- single cell
- multidrug resistant
- toll like receptor
- health information
- social media
- gram negative
- structural basis
- neural network