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Fani Deligianni
This course is a capstone assignment requiring you to apply the knowledge and skill you have learnt throughout the specialization. In this course you will choose one of the areas and complete the assignment to pass.
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What's inside
Syllabus
Permutation feature importance on the MIMIC critical care database
This is an advanced exercise/lesson that combines knowledge from the three earlier modules: 1) 'Data mining of Clinical Databases' to query the MIMIC database, 2) 'Deep learning in Electronic Health Records' to pre-process EHR and build deep learning models and 3) 'Explainable deep learning models for healthcare' to explain the models decision. In particular, permutation feature importance is implemented and applied on MIMIC-III extracted datasets. The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
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Syllabus
Permutation feature importance on the MIMIC critical care database
This is an advanced exercise/lesson that combines knowledge from the three earlier modules: 1) 'Data mining of Clinical Databases' to query the MIMIC database, 2) 'Deep learning in Electronic Health Records' to pre-process EHR and build deep learning models and 3) 'Explainable deep learning models for healthcare' to explain the models decision. In particular, permutation feature importance is implemented and applied on MIMIC-III extracted datasets. The technique is applied both on logistic regression and on an LSTM model. The explanations derived are global explanations of the model.
Read more
Traffic lights
Traffic lights
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what should give you pause and
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Applies knowledge and skills learned throughout the Explainable AI in Healthcare specialization
Covers specialized techniques like permutation feature importance, LIME, and Grad-CAM for explainable deep learning models in healthcare
Provides hands-on labs for applying these techniques on the MIMIC critical care database
Assumes prior knowledge in data mining of clinical databases, deep learning in electronic health records, and explainable deep learning models for healthcare
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Reviews summary
Advanced capstone in healthcare ai
According to learners, this course serves as a highly rewarding and challenging capstone for applying advanced concepts in healthcare AI. Students appreciate the opportunity to work with a real-world clinical database like MIMIC-III, which provides invaluable practical experience. The course excels in helping solidify understanding of explainable AI techniques (LIME, Grad-CAM, Permutation Importance) when applied to deep learning models. However, some students caution that it is highly demanding and requires a very strong foundation from prerequisite courses, making it unsuitable as a standalone introduction. The assignments are largely self-directed, reinforcing independent problem-solving skills.
Expect significant independent problem-solving and research.
"Don't expect hand-holding; this is a true capstone where you're expected to figure things out independently."
"The lack of step-by-step guidance was challenging at times, but it forced me to be resourceful and develop my own solutions."
"I enjoyed the freedom to approach the problems my own way, which made the learning more impactful and realistic."
The rigor of assignments fosters significant learning.
"It was tough, but the feeling of accomplishment after completing the complex assignments was immense."
"The challenging nature of the problems pushed me to think critically and debug effectively, leading to deeper understanding."
"While demanding, the course's rigor ensures you truly master the material and develop essential problem-solving skills."
Solidifies practical application of Explainable AI methods.
"The application of LIME and Grad-CAM in a real-world context significantly improved my understanding of XAI principles."
"I now feel confident explaining deep learning model decisions thanks to the practical, hands-on exercises in this course."
"This course was key in moving beyond theoretical knowledge to practical implementation of explainability methods for complex models."
Gain invaluable experience with authentic clinical datasets.
"Working with the MIMIC-III database was incredibly valuable; it's rare to get hands-on experience with real clinical data in a course."
"This capstone truly connected the dots from previous modules, allowing me to apply what I learned to a practical, impactful project."
"I found the exercises involving MIMIC-III data to be very relevant to my career goals in healthcare AI, offering practical insights."
Requires strong foundational knowledge from prior courses.
"This course is absolutely not for beginners. You must have a strong grasp of the previous modules, especially deep learning and data mining."
"I struggled initially because I hadn't fully mastered the concepts from CDSS 1-4. Reviewing those beforehand is crucial for success."
"The level of self-sufficiency expected is high, assuming prior mastery of the specialized topics covered in the specialization."
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 Capstone Assignment - CDSS 5 with these
activities:
Brush up on programming skills in Python
Show steps
Ensure you have a strong foundation in Python, the primary programming language used in the course.
Browse courses on
Python
Show steps
-
Review Python syntax, data structures, and control flow.
-
Practice writing simple Python scripts and functions.
Review basic machine learning concepts
Show steps
Help you recall and strengthen your foundational knowledge of machine learning, making it easier to grasp the advanced concepts covered in the course.
Browse courses on
Machine Learning
Show steps
-
Revisit key concepts such as supervised and unsupervised learning, feature extraction, and model evaluation.
-
Review resources such as textbooks, online tutorials, or lecture notes to refresh your understanding.
Practice building and evaluating simple machine learning models
Show steps
Provide you with hands-on experience in applying machine learning techniques, reinforcing your understanding and improving your problem-solving skills.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset and define the problem you want to solve.
-
Preprocess the data, select features, and train a simple model.
-
Evaluate the model's performance using appropriate metrics.
-
Repeat the process with different models and parameters to compare their performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Engage in discussion forums and collaborate with peers
Show steps
Foster collaboration and knowledge sharing, allowing you to learn from others' perspectives and gain new insights.
Browse courses on
Collaboration
Show steps
-
Actively participate in discussion forums related to the course material.
-
Ask questions, share your thoughts, and engage with other students.
-
Collaborate on projects or assignments to leverage diverse skills and perspectives.
Explore online tutorials on advanced deep learning techniques
Show steps
Expose you to cutting-edge deep learning techniques, expanding your knowledge and keeping you up-to-date with the latest advancements.
Browse courses on
Deep Learning
Show steps
-
Identify reputable online platforms or courses that offer tutorials on advanced deep learning topics.
-
Choose a specific technique or model that interests you.
-
Follow the tutorials, complete exercises, and ask questions in discussion forums.
-
Implement the techniques in your own projects or assignments.
Create a short presentation on a machine learning algorithm
Show steps
Challenge you to synthesize your knowledge and communicate it effectively, enhancing your understanding and presentation abilities.
Browse courses on
Machine Learning
Show steps
-
Choose a specific machine learning algorithm and research its details.
-
Create a presentation outline and gather relevant information.
-
Design slides that clearly explain the algorithm's concepts and applications.
-
Practice delivering your presentation and gather feedback for improvement.
Contribute to an open-source project related to machine learning
Show steps
Provide you with real-world experience in machine learning, contribute to the community, and enhance your problem-solving skills.
Browse courses on
Open Source
Show steps
-
Identify an open-source project that aligns with your interests and skills.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Create a pull request with your changes and actively participate in the review process.
Brush up on programming skills in Python
Show steps
Ensure you have a strong foundation in Python, the primary programming language used in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax, data structures, and control flow.
- Practice writing simple Python scripts and functions.
Review basic machine learning concepts
Show steps
Help you recall and strengthen your foundational knowledge of machine learning, making it easier to grasp the advanced concepts covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Revisit key concepts such as supervised and unsupervised learning, feature extraction, and model evaluation.
- Review resources such as textbooks, online tutorials, or lecture notes to refresh your understanding.
Practice building and evaluating simple machine learning models
Show steps
Provide you with hands-on experience in applying machine learning techniques, reinforcing your understanding and improving your problem-solving skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset and define the problem you want to solve.
- Preprocess the data, select features, and train a simple model.
- Evaluate the model's performance using appropriate metrics.
- Repeat the process with different models and parameters to compare their performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Engage in discussion forums and collaborate with peers
Show steps
Foster collaboration and knowledge sharing, allowing you to learn from others' perspectives and gain new insights.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Actively participate in discussion forums related to the course material.
- Ask questions, share your thoughts, and engage with other students.
- Collaborate on projects or assignments to leverage diverse skills and perspectives.
Explore online tutorials on advanced deep learning techniques
Show steps
Expose you to cutting-edge deep learning techniques, expanding your knowledge and keeping you up-to-date with the latest advancements.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify reputable online platforms or courses that offer tutorials on advanced deep learning topics.
- Choose a specific technique or model that interests you.
- Follow the tutorials, complete exercises, and ask questions in discussion forums.
- Implement the techniques in your own projects or assignments.
Create a short presentation on a machine learning algorithm
Show steps
Challenge you to synthesize your knowledge and communicate it effectively, enhancing your understanding and presentation abilities.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific machine learning algorithm and research its details.
- Create a presentation outline and gather relevant information.
- Design slides that clearly explain the algorithm's concepts and applications.
- Practice delivering your presentation and gather feedback for improvement.
Contribute to an open-source project related to machine learning
Show steps
Provide you with real-world experience in machine learning, contribute to the community, and enhance your problem-solving skills.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source project that aligns with your interests and skills.
- Familiarize yourself with the project's codebase and documentation.
- Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Create a pull request with your changes and actively participate in the review process.
Career center
Learners who complete Capstone Assignment - CDSS 5 will develop knowledge and skills
that may be useful to these careers:
Health Data Analyst
Health Data Analyst
The Health Data Analyst plays a crucial role in analyzing and interpreting large datasets relevant to healthcare. This course provides a strong foundation in data mining, deep learning, and explainable AI, which are essential skills for extracting meaningful insights. By understanding the techniques covered in this course, you can enhance your ability to process and analyze healthcare data to inform decision-making and improve patient outcomes.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers leverage technical expertise to design, implement, and maintain machine learning models. This course equips you with the necessary knowledge in deep learning and explainable AI, which are crucial for developing and deploying effective AI solutions in healthcare. By completing this course, you gain a competitive edge in the field and can contribute to advancements in patient care.
Data Scientist
Data Scientist
Data Scientists specialize in extracting knowledge from data and applying it to solve real-world problems. This course provides a comprehensive understanding of data mining, deep learning, and explainable AI, which are essential tools for data scientists working in healthcare. By mastering these techniques, you can effectively analyze healthcare data, identify patterns, and develop data-driven solutions to improve patient outcomes.
Clinical Research Associate
Clinical Research Associate
Clinical Research Associates play a vital role in the development and execution of clinical trials. This course provides a solid foundation in data mining, deep learning, and explainable AI, which can enhance your ability to analyze clinical data, assess patient outcomes, and ensure the integrity of research findings. By taking this course, you can gain a competitive advantage in the field and contribute to the advancement of medical knowledge.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide expert advice and guidance to healthcare organizations. This course enhances your understanding of data mining, deep learning, and explainable AI, which are increasingly used in healthcare settings. By completing this course, you can develop a deeper understanding of the data-driven aspects of healthcare, enabling you to provide more informed and effective consulting services.
Medical Informatics Specialist
Medical Informatics Specialist
Medical Informatics Specialists bridge the gap between healthcare and technology. This course provides a comprehensive understanding of data mining, deep learning, and explainable AI, which are essential skills for developing and implementing innovative healthcare solutions. By mastering these techniques, you can contribute to the advancement of medical informatics and improve patient care.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to analyze and interpret data in healthcare and biomedical research. This course provides a strong foundation in data mining, deep learning, and explainable AI, which are increasingly used in biostatistics. By completing this course, you can enhance your ability to analyze complex healthcare data and contribute to the development of evidence-based healthcare practices.
Clinical Data Manager
Clinical Data Manager
Clinical Data Managers oversee the collection, management, and analysis of clinical data. This course provides a comprehensive understanding of data mining, deep learning, and explainable AI, which are becoming increasingly important in clinical data management. By completing this course, you can enhance your ability to manage and analyze clinical data effectively, ensuring its accuracy and integrity.
Research Scientist
Research Scientist
Research Scientists conduct scientific research and develop new technologies. This course provides a solid foundation in data mining, deep learning, and explainable AI, which are essential for conducting cutting-edge research in healthcare and biomedical sciences. By completing this course, you can enhance your ability to design and execute research projects, contribute to scientific knowledge, and advance patient care.
Health Economist
Health Economist
Health Economists analyze the cost-effectiveness and value of healthcare interventions. This course provides a comprehensive understanding of data mining, deep learning, and explainable AI, which are increasingly used in health economics. By completing this course, you can enhance your ability to evaluate the economic impact of healthcare decisions and contribute to more efficient and equitable healthcare systems.
Healthcare Policy Analyst
Healthcare Policy Analyst
Healthcare Policy Analysts develop and evaluate policies that affect healthcare systems and patient care. This course provides a comprehensive understanding of data mining, deep learning, and explainable AI, which are increasingly used in healthcare policy analysis. By completing this course, you can enhance your ability to analyze healthcare data, assess the impact of policies, and contribute to the development of more effective healthcare policies.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health-related events. This course provides a strong foundation in data mining, deep learning, and explainable AI, which are becoming increasingly important in epidemiology. By completing this course, you can enhance your ability to analyze health data, identify risk factors, and develop evidence-based public health interventions.
Pharmacist
Pharmacist
Pharmacists provide medication-related advice and services to patients. This course may be useful for Pharmacists seeking to enhance their understanding of data mining, deep learning, and explainable AI, which are increasingly used in drug development and personalized medicine. By completing this course, you can gain a competitive edge in the field and contribute to improved patient outcomes.
Nurse
Nurse
Nurses provide direct patient care and play a vital role in healthcare delivery. This course may be useful for Nurses seeking to enhance their understanding of data mining, deep learning, and explainable AI, which are increasingly used in healthcare settings. By completing this course, you can gain a better understanding of data-driven approaches to patient care and contribute to improved patient outcomes.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators oversee the management and operation of healthcare organizations. This course may be useful for Healthcare Administrators seeking to enhance their understanding of data mining, deep learning, and explainable AI, which are becoming increasingly important in healthcare administration. By completing this course, you can gain a competitive edge in the field and contribute to more efficient and effective healthcare organizations.
For more career information including salaries, visit:
OpenCourser.com/course/n3lwbj/capstone
Reading list
We've selected seven 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
Capstone Assignment - CDSS 5.
A comprehensive resource on interpretable machine learning, covering techniques for understanding and explaining model predictions. Useful for gaining a deeper understanding of the interpretability of machine learning models.
A practical guide to understanding and interpreting machine learning models, including techniques for visualizing and explaining model predictions. Helpful for gaining hands-on experience with XAI methods.
A comprehensive guide to deep learning using Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. Useful for gaining practical experience with deep learning.
Provides a practical guide to data science methods for healthcare professionals, covering topics such as data collection, analysis, and interpretation.
Introduces the Python programming language and its popular data analysis libraries, including Pandas, NumPy, and IPython. Helpful for gaining proficiency in data manipulation and analysis.
Save
Provides a comprehensive overview of electronic health records (EHRs), including their benefits, challenges, and implementation strategies. Useful for understanding the role of EHRs in the context of machine learning and AI in healthcare.
Save
Provides a comprehensive overview of cloud computing for healthcare. It covers topics such as cloud computing architectures, cloud computing services, and cloud computing security. It valuable resource for both researchers and practitioners who are interested in using cloud computing to improve healthcare outcomes.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/n3lwbj/capstone
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Effort
2 hours to complete
Level
Intermediate
Via
Coursera
Institution
University of Glasgow
Instructor
Fani Deligianni
Language
English
This course belongs to a
collection
called
Informed Clinical Decision Making using Deep Learning. It's comprised of five courses:
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