Introduction to Clinical Data
Master healthcare data mining fundamentals, from data collection and analysis to ethical considerations in clinical research.
Master healthcare data mining fundamentals, from data collection and analysis to ethical considerations in clinical research.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI in Healthcare Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
(336 ratings)
23,015 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
Powered by
What you'll learn
Apply clinical data mining frameworks
Analyze healthcare system data
Handle unstructured medical data
Implement electronic phenotyping
Ensure ethical data use
Skills you'll gain
This course includes:
2.9 Hours PreRecorded video
20 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 8 modules in this course
This comprehensive course explores clinical data mining and analysis in healthcare settings. Students learn about various types of healthcare data, including electronic medical records, claims data, and unstructured data like medical images and text. The curriculum covers data collection, analysis methods, ethical considerations, and practical applications in healthcare decision-making.
Asking and answering questions via clinical data mining
Module 1 · 1 Hours to complete
Data available from Healthcare systems
Module 2 · 2 Hours to complete
Representing time, and timing of events, for clinical data mining
Module 3 · 1 Hours to complete
Creating analysis ready datasets from patient timelines
Module 4 · 1 Hours to complete
Handling unstructured healthcare data: text, images, signals
Module 5 · 1 Hours to complete
Putting the pieces together: Electronic phenotyping
Module 6 · 1 Hours to complete
Ethics
Module 7 · 51 Minutes to complete
Course Conclusion
Module 8 · 1 Hours to complete
Fee Structure
Instructors
Leading Authority in Biomedical Ethics
Dr. David Magnus serves as the Thomas A. Raffin Professor of Medicine and Biomedical Ethics, as well as a Professor of Pediatrics and Medicine, with a courtesy appointment in Bioengineering at Stanford University. He is the Director of the Stanford Center for Biomedical Ethics and has an influential role as a member of the Stanford Hospital and Clinics Ethics Committee. A past President of the Association of Bioethics Program Directors, he also serves as the Editor-in-Chief of the American Journal of Bioethics and currently holds the position of Vice-Chair of the Institutional Review Board (IRB) for the NIH Precision Medicine Initiative, known as “All of Us.” His extensive experience includes membership on Stanford’s IRB and Stem Cell Research Oversight Committee, along with a strong background as a research ethics consultant. Dr. Magnus's research encompasses a diverse array of topics in bioethics, such as research ethics, comparative effectiveness research ethics, transplant ethics, genetics and genomics, as well as patient-physician communication.
Pioneering AI in Healthcare and Biomedical Informatics
Dr. Nigam Shah is an esteemed Associate Professor of Medicine specializing in Biomedical Informatics at Stanford University, where he also serves as the Associate Chief Information Officer for Data Science at Stanford Health Care. His research focuses on integrating machine learning with medical ontologies to create a learning health system that enhances clinical decision-making and patient care. Dr. Shah has been recognized for his contributions to the field, being elected to the American College of Medical Informatics in 2015 and inducted into the American Society for Clinical Investigation in 2016. He holds an MBBS from Baroda Medical College in India, a PhD from Penn State University, and completed postdoctoral training at Stanford. With over 200 scientific publications and multiple patents to his name, Dr. Shah is at the forefront of advancing AI technologies in healthcare, striving to implement them ethically and effectively within clinical settings. His work not only addresses critical healthcare challenges but also aims to democratize medical knowledge through innovative data science applications.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.7 course rating
336 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.