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Fani Deligianni
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
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What's inside
Syllabus
From machine learning models to clinical decision support systems
Adopting a machine learning model in a Clinical Decision Support System (CDSS) requires several steps that involve external validation, bias assessment and calibration, 'fairness' assessment, clinical usefulness, ability to explain the model's decision and privacy-aware machine learning models. In this module, we are going to discuss these concepts and provide several examples from state-of-the-art research in the area. External validation and bias assessment have become the norm in clinical prediction models. Further work is required to assess and adopt deep learning models under these conditions. On the other hand, research in 'fairness', human-centred CDSS and privacy concerns of machine learning models are areas of active research. The first week is going to cover the ground around the difference between reproducibility and generalisability. Furthermore, calibration assessment in clinical prediction models will be explored while how different deep learning architectures affect calibration will be discussed.
Read more
Syllabus
From machine learning models to clinical decision support systems
Adopting a machine learning model in a Clinical Decision Support System (CDSS) requires several steps that involve external validation, bias assessment and calibration, 'fairness' assessment, clinical usefulness, ability to explain the model's decision and privacy-aware machine learning models. In this module, we are going to discuss these concepts and provide several examples from state-of-the-art research in the area. External validation and bias assessment have become the norm in clinical prediction models. Further work is required to assess and adopt deep learning models under these conditions. On the other hand, research in 'fairness', human-centred CDSS and privacy concerns of machine learning models are areas of active research. The first week is going to cover the ground around the difference between reproducibility and generalisability. Furthermore, calibration assessment in clinical prediction models will be explored while how different deep learning architectures affect calibration will be discussed.
Read more
'Fairness' in Machine Learning Models
Naively, machine learning can be thought as a way to come to decisions that are free from prejudice and social biases. However, recent evidence show how machine learning models learn from biases in historic data and reproduce unfair decisions in similar ways. Detecting biases against subgroups in machine learning models is challenging also due to the fact that these models have not been designed or trained to discriminate deliberately. Defining 'fairness' metrics and investigating ways in ensuring that minority groups are not disadvantaged from machine learning models' decisions is an active research area.
Decision Curve Analysis and Human-Centered CDSS
Decision curve analysis is used to assess clinical usefulness of a prediction model by estimating the net benefit with is a trade-off of the precision and accuracy of the model. Based on this approach the strategy of ‘intervention for all’ and ‘intervention for none’ is compared to the model’s net benefit. Decision curve analysis is a human-centred approach of assessing clinical usefulness, since it requires experts’ opinion. Ethical Artificial Intelligence initiative indicate that a human-centred approach in clinical decision support systems is required to enable accountability, safety and oversight while the ensure ‘fairness’ and transparency.
Privacy Concerns in CDSS
Deep learning models have remarkable ability to memorise data even when they do not overfit. In other words, the models themselves can expose information about the patients that compromise their privacy. This can results in unintentional data leakage in inference and also provide opportunities for malicious attacks. We will overview common privacy attacks and defences against them. Finally, we will discuss adversarial attacks against deep learning explanations.
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Know what's good ,
what to watch for , and
possible dealbreakers
Examines a range of topics central to the use of machine learning in clinical decision support systems, addressing external validation, calibration analysis, assessment of bias and fairness, decision curve analysis, human-centered CDSS, privacy concerns, and adversarial attacks
Taught by Fani Deligianni, an expert in machine learning and its applications in healthcare
Provides a comprehensive overview of the challenges and opportunities in using machine learning for clinical decision support, making it suitable for both beginners and experienced learners in the field
Covers cutting-edge topics such as decision curve analysis, human-centered CDSS, and privacy concerns, which are highly relevant to the development and deployment of machine learning systems in healthcare
Requires learners to have a strong foundation in machine learning and statistics, making it more suitable for graduate students and professionals in the field
Does not provide hands-on experience with machine learning tools or software, which may limit its applicability for learners who are looking to develop practical skills in this area
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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 Clinical Decision Support Systems - CDSS 4 with these
activities:
Organize and review course materials on machine learning model evaluation
Show steps
Enhance your understanding of machine learning model evaluation by reviewing and organizing your course materials.
Show steps
-
Gather all course materials related to machine learning model evaluation
-
Organize the materials into a logical structure
-
Review the materials to identify key concepts
-
Create summary notes or mind maps to reinforce your understanding
Utilize online tutorials to practice machine learning model evaluation
Show steps
Gain practical implementation skills for evaluating machine learning models by using online tutorials.
Show steps
-
Identify online tutorials that cover machine learning model evaluation
-
Follow the tutorials and complete the exercises
-
Apply the techniques to your own dataset
Complete practice exercises in machine learning model evaluation
Show steps
Refine your understanding of machine learning model evaluation through practice exercises.
Show steps
-
Find practice exercises on machine learning model evaluation
-
Solve the exercises
-
Review your solutions and identify areas for improvement
Three other activities
Expand to see all activities and additional details
Show all six activities
Engage in peer discussions on machine learning model evaluation
Show steps
Enhance your understanding by engaging in discussions and sharing knowledge with peers.
Show steps
-
Join a study group or online forum focused on machine learning model evaluation
-
Participate in discussions and share your perspectives
-
Seek feedback and insights from others
Attend a workshop on machine learning model evaluation
Show steps
Gain practical insights and hands-on experience in machine learning model evaluation by attending a workshop.
Show steps
-
Research and identify a suitable workshop on machine learning model evaluation
-
Register and attend the workshop
-
Actively participate in the workshop activities
-
Follow up after the workshop by applying what you learned
Develop a presentation on machine learning model evaluation techniques
Show steps
Deepen your understanding of machine learning model evaluation techniques by creating a comprehensive presentation.
Show steps
-
Research and gather information on various machine learning model evaluation techniques
-
Organize the information into a logical structure
-
Create visual aids and examples to illustrate the concepts
-
Practice delivering the presentation
Organize and review course materials on machine learning model evaluation
Show steps
Enhance your understanding of machine learning model evaluation by reviewing and organizing your course materials.
Show steps
Show steps
Show steps
- Gather all course materials related to machine learning model evaluation
- Organize the materials into a logical structure
- Review the materials to identify key concepts
- Create summary notes or mind maps to reinforce your understanding
Utilize online tutorials to practice machine learning model evaluation
Show steps
Gain practical implementation skills for evaluating machine learning models by using online tutorials.
Show steps
Show steps
Show steps
- Identify online tutorials that cover machine learning model evaluation
- Follow the tutorials and complete the exercises
- Apply the techniques to your own dataset
Complete practice exercises in machine learning model evaluation
Show steps
Refine your understanding of machine learning model evaluation through practice exercises.
Show steps
Show steps
Show steps
- Find practice exercises on machine learning model evaluation
- Solve the exercises
- Review your solutions and identify areas for improvement
Three other activities
Expand to see all activities and additional details
Show all six activities
Engage in peer discussions on machine learning model evaluation
Show steps
Enhance your understanding by engaging in discussions and sharing knowledge with peers.
Show steps
Show steps
Show steps
- Join a study group or online forum focused on machine learning model evaluation
- Participate in discussions and share your perspectives
- Seek feedback and insights from others
Attend a workshop on machine learning model evaluation
Show steps
Gain practical insights and hands-on experience in machine learning model evaluation by attending a workshop.
Show steps
Show steps
Show steps
- Research and identify a suitable workshop on machine learning model evaluation
- Register and attend the workshop
- Actively participate in the workshop activities
- Follow up after the workshop by applying what you learned
Develop a presentation on machine learning model evaluation techniques
Show steps
Deepen your understanding of machine learning model evaluation techniques by creating a comprehensive presentation.
Show steps
Show steps
Show steps
- Research and gather information on various machine learning model evaluation techniques
- Organize the information into a logical structure
- Create visual aids and examples to illustrate the concepts
- Practice delivering the presentation
Career center
Learners who complete Clinical Decision Support Systems - CDSS 4 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning and other techniques to extract insights from data. They work in a variety of industries, including healthcare, finance, and retail. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for building and deploying effective models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. Their work includes data preparation, feature engineering, model training, and deployment. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for building and deploying effective models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including healthcare, finance, and retail. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for developing and deploying software applications.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They work in a variety of settings, including academia, industry, and government. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for designing and conducting epidemiological studies.
Technical Writer
Technical Writer
Technical Writers create written materials about technical topics. They work in a variety of settings, including software companies, engineering firms, and manufacturing companies. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for understanding the technical literature and communicating complex technical information to a variety of audiences.
Biostatistician
Biostatistician
Biostatisticians use statistics to design and analyze studies in the health sciences. They work in a variety of settings, including academia, industry, and government. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for designing and analyzing statistical studies.
Science Writer
Science Writer
Science Writers create written materials about scientific and technical topics. They work in a variety of settings, including newspapers, magazines, and websites. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for understanding the scientific literature and communicating complex scientific information to a variety of audiences.
Patent Attorney
Patent Attorney
Patent Attorneys help inventors obtain patents for their inventions. They work in a variety of settings, including law firms, corporations, and government agencies. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for understanding the patent process and drafting patent applications.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use data to improve the quality and efficiency of healthcare delivery. They work in a variety of settings, including hospitals, clinics, and insurance companies. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for analyzing healthcare data.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide advice and guidance to healthcare organizations on a variety of topics, including strategy, operations, and technology. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for understanding the challenges and opportunities facing healthcare organizations.
Medical Writer
Medical Writer
Medical Writers create written materials about medical and health-related topics. They work in a variety of settings, including healthcare organizations, pharmaceutical companies, and medical journals. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for understanding the scientific literature and communicating complex medical information to a variety of audiences.
Medical Physicist
Medical Physicist
Medical Physicists use physics to improve the quality and safety of medical diagnosis and treatment. They work in a variety of settings, including hospitals, clinics, and research institutions. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for developing and implementing medical physics technologies.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use technology to improve the quality and efficiency of healthcare delivery. They work in a variety of settings, including hospitals, clinics, and insurance companies. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for developing and implementing health information systems.
Clinical Research Scientist
Clinical Research Scientist
Clinical Research Scientists design and conduct clinical trials to evaluate the safety and efficacy of new drugs and treatments. They work in a variety of settings, including academia, industry, and government. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for designing and conducting clinical trials.
Public Health Scientist
Public Health Scientist
Public Health Scientists work to improve the health of populations. They work in a variety of settings, including academia, industry, and government. This course may be useful to those who wish to enter this field, as it provides a foundation in machine learning evaluation techniques that are essential for designing and implementing public health programs.
For more career information including salaries, visit:
OpenCourser.com/course/b7do30/clinical
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
Clinical Decision Support Systems - CDSS 4.
Provides a thorough grounding in the principles and practices of clinical decision support systems, including a chapter on machine learning.
This textbook provides a thorough overview of decision support systems in healthcare, including their design, implementation, and evaluation.
Save
This comprehensive textbook on data mining can provide the foundation for understanding the algorithms and approaches used in the course.
Save
Provides a detailed overview of predictive modeling techniques, including both traditional and modern approaches, serving as a helpful reference for understanding the methods used in the course.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
This textbook provides an easy-to-follow guide to the basics of health data analytics, which can aid understanding of the course topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b7do30/clinical
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Effort
5-6 hours/week
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:
- Capstone Assignment - CDSS 5
- Deep learning in Electronic Health Records - CDSS 2
- Clinical Decision Support Systems - CDSS 4 (viewing)
- Explainable deep learning models for healthcare - CDSS 3
- Data mining of Clinical Databases - CDSS 1
Good to know
Examines a range of topics central to the use of machine learning in clinical decision support systems, addressing external validation, calibration analysis, assessment of bias and fairness, decision curve analysis, human-centered CDSS, privacy concerns, and adversarial attacks
Taught by Fani Deligianni, an expert in machine learning and its applications in healthcare
Provides a comprehensive overview of the challenges and opportunities in using machine learning for clinical decision support, making it suitable for both beginners and experienced learners in the field
Covers cutting-edge topics such as decision curve analysis, human-centered CDSS, and privacy concerns, which are highly relevant to the development and deployment of machine learning systems in healthcare
Requires learners to have a strong foundation in machine learning and statistics, making it more suitable for graduate students and professionals in the field
Does not provide hands-on experience with machine learning tools or software, which may limit its applicability for learners who are looking to develop practical skills in this area
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