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Case studies
- Production optimisation through feedback analysis
- Satisfaction questionnaire analysis
- Health trajectory analysis
- Biocomputing transcriptome analysis
- Risk analysis and security policy
- Difficult intubations analysis
- Cyber-crimes detection
- Customer characterization
- Organization name identification
- Implementation of a Bayesian score
- Modeling and simulation of dynamic systems
- Analysis of a perfume market
- Consumer segmentation
- Identifying drivers of liking
- Salmonella isolation
- Political Analysis
- Studying consumer drivers
- Employees satisfaction analysis
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Difficult intubations analysis
Prediction of difficult intubation (DI) is crucial during pre anaesthesia assessment of a patient. Many criterions are used to predict DI with different performances. In this case study, we show how BayesiaLab, through its learning algorithms, allows to quickly discovering unknown probabilistic relationships between variables and enhance prediction.
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