Deep learning in Electronic Health Records

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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.

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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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

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

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Review basic neural network architectures and concepts to strengthen foundational knowledge for the course.

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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

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Apply the concepts of MLP to a practical problem, reinforcing understanding of network implementation and ECG analysis.

Browse courses on Multi-layer Perceptron

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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

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Summarize and share knowledge on deep learning for time-series data through writing, reinforcing understanding and promoting effective communication.

Browse courses on Deep Learning

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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.

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Learn Convolutional Neural Networks for Time-Series Analysis

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Explore tutorials to gain additional insights into CNNs for time-series analysis, supplementing course materials with practical examples.

Browse courses on Convolutional Neural Networks

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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

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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

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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

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Visually represent and simplify complex EHR encoding strategies, enhancing comprehension and retention of this critical aspect.

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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

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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

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Clinical 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.

See salaries and explore the career path for Health Data Analyst

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Medical Researcher

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.

See salaries and explore the career path for Healthcare Consultant

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.

See salaries and explore the career path for Public Health Analyst

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.

See salaries and explore the career path for Health Informatics Specialist

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.

See salaries and explore the career path for Healthcare Administrator

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.

See salaries and explore the career path for Clinical Informatics Specialist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Database Administrator

Deep learning in Electronic Health Records - CDSS 2

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