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Michael DAndrea
Learn with Udacity to harness AI for EHR data analysis. Learn how to build, assess, and interpret ML models for medical insights. Grow your career. Enroll Today
Prerequisite details
Read more
Learn with Udacity to harness AI for EHR data analysis. Learn how to build, assess, and interpret ML models for medical insights. Grow your career. Enroll Today
Prerequisite details
Read more
Learn with Udacity to harness AI for EHR data analysis. Learn how to build, assess, and interpret ML models for medical insights. Grow your career. Enroll Today
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Data cleaning
- Machine learning frameworks in Python
- Basic supervised machine learning
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/eliv55/applying
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What's inside
Syllabus
This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.
In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.
Read more
Syllabus
This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.
In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.
Read more
In this lesson you will learn how to work with different EHR codes and how to map them properly to records.
In this lesson, you'll gain skills in feature engineering and transformation of EHR.
In this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.
In this project students will use what they learn in the classroom to apply AI in healthcare for patient data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores coding in Python, a key industry standard, for healthcare data
Develops skills in supervised machine learning for healthcare data analysis
Builds a strong foundation for applying AI to medical insights, a growing field of practice
Taught by Michael DAndrea, who is recognized for their work in AI for medical insights
Examines AI for EHR data analysis, a highly relevant and in-demand topic
Prerequisites in data cleaning, Python, and Machine Learning frameworks may limit accessibility for some learners
Save this course
Save Applying AI to EHR Data to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applying AI to EHR Data with these
activities:
Review supervised machine learning concepts
Show steps
Refresh on supervised machine learning techniques used in the course
Browse courses on
Supervised Machine Learning
Show steps
-
Review different types of supervised machine learning algorithms
-
Discuss the evaluation metrics used for supervised machine learning models
Review Python and Machine Learning frameworks
Show steps
Refresher on background knowledge needed for this course
Browse courses on
Python
Show steps
-
Review basic Python syntax and data structures
-
Review the Scikit-learn, TensorFlow, and Keras libraries
Join study groups or online forums to discuss course material
Show steps
Engage with peers to clarify concepts, share insights, and improve understanding
Show steps
-
Join study groups or online forums related to the course
-
Participate in discussions and contribute to the community
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on building and evaluating machine learning models
Show steps
Supplement course material with hands-on practice building and evaluating machine learning models
Browse courses on
Machine Learning Models
Show steps
-
Find tutorials on building models for medical data analysis
-
Follow tutorials to implement models using Python
-
Evaluate model performance using metrics such as accuracy, precision, and recall
Solve data preprocessing and feature engineering coding challenges
Show steps
Practice applying data preprocessing and feature engineering techniques to real-world datasets
Browse courses on
Data Preprocessing
Show steps
-
Use Python libraries for data cleaning and manipulation
-
Engineer new features from existing data to improve model performance
Create a presentation on the challenges and opportunities of AI in healthcare
Show steps
Reinforce understanding of AI in healthcare by presenting on its challenges and opportunities
Browse courses on
AI in Healthcare
Show steps
-
Research and gather information on the topic
-
Develop a presentation outline and structure
-
Create visuals and supporting materials for the presentation
-
Practice delivering the presentation
Mentor other students who are struggling with course material
Show steps
Reinforce understanding by helping others and clarify concepts
Show steps
-
Identify opportunities to assist other students
-
Provide guidance and support to struggling students
Review supervised machine learning concepts
Show steps
Refresh on supervised machine learning techniques used in the course
Browse courses on
Supervised Machine Learning
Show steps
Show steps
Show steps
- Review different types of supervised machine learning algorithms
- Discuss the evaluation metrics used for supervised machine learning models
Review Python and Machine Learning frameworks
Show steps
Refresher on background knowledge needed for this course
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python syntax and data structures
- Review the Scikit-learn, TensorFlow, and Keras libraries
Join study groups or online forums to discuss course material
Show steps
Engage with peers to clarify concepts, share insights, and improve understanding
Show steps
Show steps
Show steps
- Join study groups or online forums related to the course
- Participate in discussions and contribute to the community
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on building and evaluating machine learning models
Show steps
Supplement course material with hands-on practice building and evaluating machine learning models
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Find tutorials on building models for medical data analysis
- Follow tutorials to implement models using Python
- Evaluate model performance using metrics such as accuracy, precision, and recall
Solve data preprocessing and feature engineering coding challenges
Show steps
Practice applying data preprocessing and feature engineering techniques to real-world datasets
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Use Python libraries for data cleaning and manipulation
- Engineer new features from existing data to improve model performance
Create a presentation on the challenges and opportunities of AI in healthcare
Show steps
Reinforce understanding of AI in healthcare by presenting on its challenges and opportunities
Browse courses on
AI in Healthcare
Show steps
Show steps
Show steps
- Research and gather information on the topic
- Develop a presentation outline and structure
- Create visuals and supporting materials for the presentation
- Practice delivering the presentation
Mentor other students who are struggling with course material
Show steps
Reinforce understanding by helping others and clarify concepts
Show steps
Show steps
Show steps
- Identify opportunities to assist other students
- Provide guidance and support to struggling students
Career center
Learners who complete Applying AI to EHR Data will develop knowledge and skills
that may be useful to these careers:
Healthcare Data Scientist
Healthcare Data Scientist
A Healthcare Data Scientist uses their knowledge of data science and healthcare to develop and apply analytical models to improve patient care and reduce costs. This course may be useful in developing the skills needed for this role, as it provides a foundation in machine learning, data analysis, and healthcare data standards.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course may be useful in developing the skills needed for this role, as it provides a foundation in machine learning, data analysis, and feature engineering.
Biostatistician
Biostatistician
A Biostatistician uses statistical methods to design and analyze medical research studies. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines that collect, clean, and transform data for analysis. This course may be useful in developing the skills needed for this role, as it provides a foundation in data cleaning, data pipelines, and feature engineering.
Public Health Analyst
Public Health Analyst
A Public Health Analyst collects, analyzes, and interprets data to identify public health problems and develop solutions. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Health Informatics Specialist
Health Informatics Specialist
A Health Informatics Specialist uses their knowledge of healthcare and information technology to design, implement, and manage health information systems. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Healthcare IT Manager
Healthcare IT Manager
A Healthcare IT Manager oversees the planning, implementation, and maintenance of healthcare information systems. This course may be useful in developing the skills needed for this role, as it provides a foundation in healthcare data analysis, machine learning, and healthcare data standards.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the distribution and determinants of health-related states or events in specified populations. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Healthcare Administrator
Healthcare Administrator
A Healthcare Administrator manages the day-to-day operations of a healthcare organization. This course may be useful in developing the skills needed for this role, as it provides a foundation in healthcare data analysis, machine learning, and healthcare data standards.
Health Policy Analyst
Health Policy Analyst
A Health Policy Analyst analyzes health policy and its impact on the healthcare system. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Healthcare Consultant
Healthcare Consultant
A Healthcare Consultant provides consulting services to healthcare organizations to help them improve their operations and patient care. This course may be useful in developing the skills needed for this role, as it provides a foundation in healthcare data analysis, machine learning, and healthcare data standards.
Clinical Research Coordinator
Clinical Research Coordinator
A Clinical Research Coordinator plans, organizes, and manages clinical research studies. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, healthcare data standards, and clinical research methods.
Medical Data Analyst
Medical Data Analyst
A Medical Data Analyst researches, compiles, and analyzes complex medical data to identify patterns, trends, and insights that help healthcare organizations improve patient care and reduce costs. This course may be useful in developing the skills needed for this role, as it provides a foundation in data cleaning, machine learning, and feature engineering, all of which are essential for success in this field.
Physician
Physician
A Physician provides medical care to patients. This course may be useful in developing the skills needed for this role, as it provides a foundation in data analysis, machine learning, and healthcare data standards.
Medical Writer
Medical Writer
A Medical Writer creates and edits medical documents, such as research papers, clinical guidelines, and patient education materials. This course may be useful in developing the skills needed for this role, as it provides a foundation in medical terminology, data analysis, and scientific writing.
For more career information including salaries, visit:
OpenCourser.com/course/eliv55/applying
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applying AI to EHR Data.
Provides a comprehensive introduction to machine learning with Python, including supervised learning, unsupervised learning, and deep learning. It would be a valuable reference for students who want to learn more about machine learning or improve their programming skills.
Provides a comprehensive introduction to deep learning with Python, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It would be a valuable reference for students who want to learn more about deep learning or improve their programming skills.
Save
Provides a comprehensive introduction to data mining, including data preprocessing, clustering, classification, and association analysis. It would be a valuable reference for students who want to learn more about data mining or improve their programming skills.
Provides a solid foundation in health informatics, which is essential for understanding the use of data in healthcare.
Provides a comprehensive introduction to statistics for data science, including probability, inference, and modeling. It would be a valuable reference for students who want to learn more about statistics or improve their programming skills.
Provides a comprehensive overview of artificial intelligence in healthcare, including its applications, benefits, and challenges. It would be helpful for students who want to learn more about the broader context of AI in healthcare.
Provides a comprehensive introduction to Python for data analysis, including data manipulation, data visualization, and machine learning. It would be a useful reference for students who want to learn more about Python or improve their programming skills.
Provides a comprehensive introduction to natural language processing with Python, including text preprocessing, text classification, and text generation. It would be a valuable reference for students who want to learn more about natural language processing or improve their programming skills.
Save
Provides a comprehensive overview of the current state of artificial intelligence (AI) in healthcare. It covers topics such as the use of AI for diagnosis, treatment, and drug discovery.
A good choice for additional reading, as it provides more advanced topics in machine learning that can supplement the course.
Offers a thought-provoking perspective on the future of healthcare and the role of technology.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/eliv55/applying
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Effort
3 weeks
Via
Udacity
Instructor
Michael DAndrea
Language
English
Good to know
Explores coding in Python, a key industry standard, for healthcare data
Develops skills in supervised machine learning for healthcare data analysis
Builds a strong foundation for applying AI to medical insights, a growing field of practice
Taught by Michael DAndrea, who is recognized for their work in AI for medical insights
Examines AI for EHR data analysis, a highly relevant and in-demand topic
Prerequisites in data cleaning, Python, and Machine Learning frameworks may limit accessibility for some learners
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