– Help with diagnosis or personalized medicine through the use of patient data history (Electronic Health Record, Real World Evidence, etc.)
– Medical follow-up (processing of monitoring data, e-Health or connected health)
– Drug discovery (intelligent preselection of candidate molecules)
– Extraction of information from raw data, for example textual and creation of structured databases
– Analysis of results from clinical trials or biological databases
– Optimization of the performance of health establishments (processing of administrative data, etc.)
– Optimization of Marketing operations (time, costs etc.)
– Supply chain
– Market access

– Database architecture and management (design, operation and administration of the database)
– Detection and management of missing, duplicate or inconsistent data
– Tools: Oracle SQL, Hadoop, Scala, R, python

– Data visualization
– Biostatistics and consolidation,
– report
– Tools: Tableau, Spotfire, R Shiny, Python, Oracle SQL, Power BI, QlikView

– Textual document: medical records, test results, etc.
– Structured file: clinical study, results table, etc.
– Database: patients, molecular, etc.
– Imaging: x-rays, scanner, etc.

– NLP (Natural Language Processing): text mining, exploitation and structuring of textual data such as patient records or medical reports
– ANN (Artificial Neural Network), RNN (Recurrent Neural Network): processing of historical patient data for risk prediction, monitoring for follow-up or prevention
– CNN (Convolutional Neural Network): image analysis
– Unsupervised learning: labeling for fraud detection
– Random forest, k-means: classification or clustering from clinical trial data
– Data mining: health insurance fraud detection