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Fani Deligianni
Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.
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What's inside
Syllabus
Artificial Intelligence and Multi-Layer Perceptron
This week includes an overview of deep learning history and popular deep learning platforms. Subsequently, Multi-Layer Perceptron (MLP) Networks are discussed along with common activation functions, loss functions and optimisation algorithms. Finally, the practical exercises will allow to optimise and evaluate MLP in ECG classification.
Read more
Syllabus
Artificial Intelligence and Multi-Layer Perceptron
This week includes an overview of deep learning history and popular deep learning platforms. Subsequently, Multi-Layer Perceptron (MLP) Networks are discussed along with common activation functions, loss functions and optimisation algorithms. Finally, the practical exercises will allow to optimise and evaluate MLP in ECG classification.
Read more
Convolutional and Recurrent Neural Networks.
Convolutional Neural Networks (CNNs) revolutionised the way we process images and they contributed significantly in deep learning success. This week we are going to discuss what advantages CNNs offer over MLP and we will implement CNNs for time-series classifications. Subsequently, we are going to present Recurrent Neural Networks (RNNs). In particular, we are going to discuss Long-Short Term Memory Networks and Gated Recurrent Unit Networks. Practical exercises will allow to design and train all these types of networks in ECG classification. The importance of training, validation and testing datasets will be emphasised for avoiding overfitting and model evaluation.
Preprocessing and imputation of MIMIC III data
Developing benchmark datasets for DNNs based on MIMIC-III database involves several steps that include cohort selection, unit conversion, outlier removal and aggregation of data within time windows. The later step allows to represent EHR as time-series data but it is also susceptible to missing data. For this reason imputation strategies both based on traditional and deep learning techniques are presented. The learner will have the opportunity to preprocess EHR and train deep learning models in predicting in-hospital mortality.
EHR Encodings for machine learning models
EHRs include categorical, ordinal and continuous variables. Appropriate data representation is important and encodings affect prediction performance. This week includes several different strategies to encode the data such as target encodings, deep learning encodings and similarity encodings. In particular, autoencoders which is a deep learning architecture to represent data in lower dimensional space will be demonstrated and applied in in-hospital mortality prediction.
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possible dealbreakers
Teaches core principles of deep learning, such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), which are essential for understanding deep learning
Develops skills in formulating clinical prediction benchmarks derived from information available in MIMIC-III database, which is valuable in predicting in-hospital mortality
Provides practical exercises to optimize and evaluate deep learning models in ECG classification, allowing learners to apply their knowledge hands-on
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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 Deep learning in Electronic Health Records - CDSS 2 with these
activities:
Review Neural Network Architectures
Show steps
Review basic neural network architectures and concepts to strengthen foundational knowledge for the course.
Show steps
-
Read through the provided resources on neural network architectures.
-
Summarize the key concepts and components of different neural network architectures.
-
Identify the advantages and disadvantages of each architecture.
Implement MLP in ECG Classification
Show steps
Apply the concepts of MLP to a practical problem, reinforcing understanding of network implementation and ECG analysis.
Browse courses on
Multi-layer Perceptron
Show steps
-
Set up a programming environment and import necessary libraries.
-
Load and preprocess the ECG dataset.
-
Design and implement the MLP model.
-
Train and evaluate the model on the dataset.
-
Analyze the results and optimize the model as needed.
Write a Blog Post on Deep Learning for Time-Series Data
Show steps
Summarize and share knowledge on deep learning for time-series data through writing, reinforcing understanding and promoting effective communication.
Browse courses on
Deep Learning
Show steps
-
Research and gather information on deep learning for time-series data.
-
Identify the key concepts and applications.
-
Outline and write the blog post, clearly explaining the concepts.
-
Proofread and publish the blog post.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Learn Convolutional Neural Networks for Time-Series Analysis
Show steps
Explore tutorials to gain additional insights into CNNs for time-series analysis, supplementing course materials with practical examples.
Browse courses on
Convolutional Neural Networks
Show steps
-
Identify reputable online tutorials or video courses on CNNs for time-series analysis.
-
Go through the tutorials and practice the concepts.
-
Apply the learned techniques to a personal project or dataset.
Develop an EHR Preprocessing Pipeline
Show steps
Create a reusable pipeline for preprocessing EHR data, enhancing the understanding of data handling and preparation for deep learning models.
Browse courses on
Data Pipeline
Show steps
-
Gather and clean the necessary EHR data.
-
Design and implement a preprocessing pipeline using appropriate techniques.
-
Test the pipeline on different EHR datasets.
-
Package the pipeline for easy reuse.
Design an Infographic on EHR Encoding Strategies
Show steps
Visually represent and simplify complex EHR encoding strategies, enhancing comprehension and retention of this critical aspect.
Browse courses on
Infographics
Show steps
-
Gather and organize information on EHR encoding strategies.
-
Design an visually appealing and informative infographic.
-
Use clear and concise language to explain the strategies.
-
Share the infographic with peers and online communities.
Build a Time-Series Classification Model
Show steps
Engage in a hands-on project to apply time-series classification techniques to a real-world problem, solidifying understanding and skills.
Browse courses on
Time-Series Classification
Show steps
-
Identify a suitable time-series dataset for classification.
-
Explore different time-series classification algorithms.
-
Implement and train a time-series classification model.
-
Evaluate the model's performance and identify areas for improvement.
-
Deploy the model for practical use.
Review Neural Network Architectures
Show steps
Review basic neural network architectures and concepts to strengthen foundational knowledge for the course.
Show steps
Show steps
Show steps
- Read through the provided resources on neural network architectures.
- Summarize the key concepts and components of different neural network architectures.
- Identify the advantages and disadvantages of each architecture.
Implement MLP in ECG Classification
Show steps
Apply the concepts of MLP to a practical problem, reinforcing understanding of network implementation and ECG analysis.
Browse courses on
Multi-layer Perceptron
Show steps
Show steps
Show steps
- Set up a programming environment and import necessary libraries.
- Load and preprocess the ECG dataset.
- Design and implement the MLP model.
- Train and evaluate the model on the dataset.
- Analyze the results and optimize the model as needed.
Write a Blog Post on Deep Learning for Time-Series Data
Show steps
Summarize and share knowledge on deep learning for time-series data through writing, reinforcing understanding and promoting effective communication.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Research and gather information on deep learning for time-series data.
- Identify the key concepts and applications.
- Outline and write the blog post, clearly explaining the concepts.
- Proofread and publish the blog post.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Learn Convolutional Neural Networks for Time-Series Analysis
Show steps
Explore tutorials to gain additional insights into CNNs for time-series analysis, supplementing course materials with practical examples.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Identify reputable online tutorials or video courses on CNNs for time-series analysis.
- Go through the tutorials and practice the concepts.
- Apply the learned techniques to a personal project or dataset.
Develop an EHR Preprocessing Pipeline
Show steps
Create a reusable pipeline for preprocessing EHR data, enhancing the understanding of data handling and preparation for deep learning models.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Gather and clean the necessary EHR data.
- Design and implement a preprocessing pipeline using appropriate techniques.
- Test the pipeline on different EHR datasets.
- Package the pipeline for easy reuse.
Design an Infographic on EHR Encoding Strategies
Show steps
Visually represent and simplify complex EHR encoding strategies, enhancing comprehension and retention of this critical aspect.
Browse courses on
Infographics
Show steps
Show steps
Show steps
- Gather and organize information on EHR encoding strategies.
- Design an visually appealing and informative infographic.
- Use clear and concise language to explain the strategies.
- Share the infographic with peers and online communities.
Build a Time-Series Classification Model
Show steps
Engage in a hands-on project to apply time-series classification techniques to a real-world problem, solidifying understanding and skills.
Browse courses on
Time-Series Classification
Show steps
Show steps
Show steps
- Identify a suitable time-series dataset for classification.
- Explore different time-series classification algorithms.
- Implement and train a time-series classification model.
- Evaluate the model's performance and identify areas for improvement.
- Deploy the model for practical use.
Career center
Learners who complete Deep learning in Electronic Health Records - CDSS 2 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the design, development, and implementation of machine learning software and algorithms. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for machine learning. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Machine Learning Engineers to develop the skills and knowledge needed to succeed in this field.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Data Scientists to develop the skills and knowledge needed to succeed in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for software development. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Software Engineers to develop the skills and knowledge needed to succeed in this field.
Clinical Data Analyst
Clinical Data Analyst
Clinical Data Analysts use data to improve the quality and efficiency of clinical care. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Clinical Data Analysts to develop the skills and knowledge needed to succeed in this field.
Health Data Analyst
Health Data Analyst
Health Data Analysts use data to improve the quality and efficiency of healthcare. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Health Data Analysts to develop the skills and knowledge needed to succeed in this field.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and injury. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Epidemiologists to develop the skills and knowledge needed to succeed in this field.
Biostatistician
Biostatistician
Biostatisticians use statistics to solve problems in biology and medicine. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Biostatisticians to develop the skills and knowledge needed to succeed in this field.
Medical Researcher
Medical Researcher
Medical Researchers conduct research to improve the understanding of diseases and develop new treatments. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Medical Researchers to develop the skills and knowledge needed to succeed in this field.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide advice to healthcare organizations on how to improve their operations. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Healthcare Consultants to develop the skills and knowledge needed to succeed in this field.
Public Health Analyst
Public Health Analyst
Public Health Analysts use data to improve the health of populations. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Public Health Analysts to develop the skills and knowledge needed to succeed in this field.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use technology to improve the quality and efficiency of healthcare. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Health Informatics Specialists to develop the skills and knowledge needed to succeed in this field.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators manage the operations of healthcare organizations. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Healthcare Administrators to develop the skills and knowledge needed to succeed in this field.
Clinical Informatics Specialist
Clinical Informatics Specialist
Clinical Informatics Specialists use technology to improve the quality and efficiency of clinical care. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Clinical Informatics Specialists to develop the skills and knowledge needed to succeed in this field.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that supports data analysis. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Data Engineers to develop the skills and knowledge needed to succeed in this field.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course provides a strong foundation in the principles of deep learning, which is a powerful technique for data analysis. The course also covers the application of deep learning to electronic health records, which is a growing field with many potential applications. This course can help Database Administrators to develop the skills and knowledge needed to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/2nqw64/deep
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
Deep learning in Electronic Health Records - CDSS 2.
Save
Provides a comprehensive and authoritative introduction to deep learning, covering the latest advancements and practical applications.
Delivers a comprehensive overview of statistical machine learning techniques, their mathematical foundations, and practical applications.
Delves into the use of deep learning for medical image analysis, covering topics such as image segmentation, classification, and registration. It provides readers with a comprehensive understanding of the state-of-the-art techniques and their applications in healthcare.
Save
Provides a broad overview of the impact of artificial intelligence on healthcare, including its application in deep learning for electronic health records.
Provides a comprehensive guide to machine learning using Python, covering a wide range of topics such as data preprocessing, feature engineering, model selection, and evaluation. It is an excellent resource for those seeking a practical introduction to machine learning.
Covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks, in the context of Python.
Provides a comprehensive overview of statistical methods used in medical research, covering topics such as study design, data analysis, and interpretation. It is an excellent resource for those seeking a solid foundation in medical statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2nqw64/deep
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Effort
5-6 hours
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 (viewing)
- Clinical Decision Support Systems - CDSS 4
- Explainable deep learning models for healthcare - CDSS 3
- Data mining of Clinical Databases - CDSS 1
Good to know
Teaches core principles of deep learning, such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), which are essential for understanding deep learning
Develops skills in formulating clinical prediction benchmarks derived from information available in MIMIC-III database, which is valuable in predicting in-hospital mortality
Provides practical exercises to optimize and evaluate deep learning models in ECG classification, allowing learners to apply their knowledge hands-on
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