Data analytics and clinical feature ranking of medical records of patients with sepsis.
Davide ChiccoLuca OnetoPublished in: BioData mining (2021)
Our results showed that machine learning can be employed efficiently to predict septic shock, SOFA score, and survival of patients diagnoses with sepsis, from their electronic health records data. And regarding clinical feature ranking, our results showed that Random Forests feature selection identified several unexpected symptoms and clinical components as relevant for septic shock, SOFA score, and survival. These discoveries can help doctors and physicians in understanding and predicting septic shock. We made the analyzed dataset and our developed software code publicly available online.
Keyphrases
- septic shock
- electronic health record
- machine learning
- big data
- deep learning
- primary care
- healthcare
- end stage renal disease
- artificial intelligence
- newly diagnosed
- climate change
- clinical decision support
- social media
- prognostic factors
- data analysis
- depressive symptoms
- free survival
- neural network
- patient reported
- patient reported outcomes