Clinical Data Science
Unlocking the future of healthcare through clinical data science: Precision Medicine, Informed Decisions, Better Lives.
- Definition: Leverage state-of-the-art principles of data science to extract valuable insights from clinical data, improve disease diagnosis, and accelerate healthcare innovation.
- Data Sources: Electronic health records (EHRs), medical imaging, genomic data, and patient-reported outcomes.
- Benefits: Improved disease diagnosis, reduce cost from readmission and appointment no show, automate routine tasks to boost process efficiency, and minimize fraudulent transactions.
- Deliverables: Data exploration, processing, analysis, visualization, machine learning modeling, and prescriptive analytics.
Sample Projects
Prediction of disease states of
Otitis Media ear infection.
Prediction of freezing of gaits in Parkinson disease patients.
Prediction of appointment cancellations
Other Applications
Prediction of potentially fraudulent transaction in insurance claims.
Uncovering patterns in patient health records for risk score analysis.
Uncovering patterns in patient health records for risk score analysis.
