Powered by
Completion
CERTIFICATE
Deep learning in Electronic Health Records - CDSS 2
Master deep learning techniques for EHR analysis, including neural networks, data preprocessing, and advanced imputation strategies for healthcare analytics.
Course Cost
Free course
Intermediate
Skill Level
29 Hours
Self-paced lessons
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 Informed Clinical Decision Making using Deep Learning 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.
English
What you'll learn
Train and optimize various deep learning architectures for EHR data
Implement preprocessing techniques for clinical time-series data
Master different imputation strategies for handling missing values
Develop clinical prediction models using MIMIC-III database
Apply data encoding techniques for categorical and continuous variables
Skills you'll gain
This course includes:
4.03 Hours PreRecorded video
5 quizzes
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
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 4 modules in this course
This comprehensive course covers deep learning applications in Electronic Health Records (EHR). Students learn to implement various neural network architectures including Multi-layer Perceptron, Convolutional Neural Networks, and Recurrent Neural Networks for healthcare data analysis. The curriculum addresses challenges specific to EHR data, such as missing values and heterogeneous data types, through advanced imputation techniques and encoding strategies. Practical applications focus on clinical prediction using the MIMIC-III database.
Artificial Intelligence and Multi-Layer Perceptron
Module 1 · 6 Hours to complete
Convolutional and Recurrent Neural Networks
Module 2 · 7 Hours to complete
Preprocessing and imputation of MIMIC III data
Module 3 · 9 Hours to complete
EHR Encodings for machine learning models
Module 4 · 7 Hours to complete
Fee Structure
Reviews
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.
Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
4,984
Students
5
Courses
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.