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HU Credits:
4
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Clinical Pharmacy
Semester:
2nd Semester
Teaching Languages:
Hebrew
Campus:
Ein Karem
Course/Module Coordinator:
Amichai Perlman
Coordinator Office Hours:
by appointment
Teaching Staff:
Dr. Amichai Perlman
Course/Module description:
The course aims to introduces foundational elements of clinical data science by combining theoretical lectures with hands-on experience using Python for the analysis and visualization of datasets related to medicine and pharmacotherapy.
Course/Module aims:
To provide an overview of different types of clinical data, their structure, and considerations for how they may be used for research and for development of medical applications. Provide participants with skills to independently process, analyze, and visualize clinical data, using a range of different types of datasets.
Learning outcomes - On successful completion of this module, students should be able to:
- Describe how different types of clinical data are generated (who, when, and why), and how they are gathered and recorded in information systems.
- Use Python and SQL to work with data, including selecting columns, filtering rows, joining tables, recoding variables
- Conduct exploratory data analysis using quantitative measures and visualization
- Analyze between-group differences and bivariate associations
- Develop and evaluate multivariate models
Attendance requirements(%):
80
Teaching arrangement and method of instruction:
Lectures, practice, and summary project, including include exercise and self-learning on DataCamp https://www.datacamp.com/
Course/Module Content:
1. Types of clinical data
2. Medical and Pharmacy related ontologies (ATC, ICD10, RxNorm, MedDRA).
3. Data formats, variable types, data processing and cleaning
4. Data manipulation and tidy data
5. Quantitative and visual characterization of variables.
6. Hypothesis testing and drug adverse event signal detection
7. Multivariate regression
8. Random forest.
Required Reading:
No required reading material
Additional Reading Material:
- Data Science from Scratch: First Principles with Python 2nd Edition
- Healthcare Analytics Made Simple
- Data Preparation and Exploration: Applied to Healthcare Data
- R for Data Science by Hadley Wickham & Garrett Grolemund
Grading Scheme :
Essay / Project / Final Assignment / Home Exam / Referat 60 %
Submission assignments during the semester: Exercises / Essays / Audits / Reports / Forum / Simulation / others 40 %
Additional information:
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