Explainable deep learning models for healthcare

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Interpretable vs Explainable Machine Learning Models in Healthcare

Deep learning models are complex and it is difficult to understand their decisions. Explainability methods aim to shed light to the deep learning decisions and enhance trust, avoid mistakes and ensure ethical use of AI. Explanations can be categorised as global, local, model-agnostic and model-specific. Permutation feature importance is a global, model agnostic explainabillity method that provide information with relation to which input variables are more related to the output.

Local Explainability Methods for Deep Learning Models

Local explainability methods provide explanations on how the model reach a specific decision. LIME approximates the model locally with a simpler, interpretable model. SHAP expands on this and it is also designed to address multi-collinearity of the input features. Both LIME and SHAP are local, model-agnostic explanations. On the other hand, CAM is a class-discriminative visualisation techniques, specifically designed to provide local explanations in deep neural networks.

Gradient-weighted Class Activation Mapping and Integrated Gradients

GRAD-CAM is an extension of CAM, which aims to a broader application of the architecture in deep neural networks. Although, it is one of the most popular methods in explaining deep neural network decisions, it violates key axiomatic properties, such as sensitivity and completeness. Integrated gradients is an axiomatic attribution method that aims to cover this gap.

Attention mechanisms in Deep Learning

Attention in deep neural networks mimics human attention that allocates computational resources to a small range of sensory input in order to process specific information with limited processing power. In this week, we discuss how to incorporate attention in Recurrent Neural Networks and autoencoders. Furthermore, we visualise attention weights in order to provide a form of inherent explanation for the decision making process.

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Emphasises interpretable and explainable machine learning models in healthcare, aligning with the industry's focus on understanding and trustworthiness

Provides hands-on applications of explainablity methods, such as PFI, LIME, SHAP, and CAM, giving learners practical experience

Covers attention mechanisms in deep learning, offering insights into how neural networks allocate resources for decision-making

Requires foundational knowledge in machine learning and deep learning, which may limit accessibility for beginners

Focuses primarily on explainability methods for time-series classification, limiting its applicability to other domains

Taught by Fani Deligianni, an experienced machine learning researcher, enhancing the course's credibility

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Career center

Learners who complete Explainable deep learning models for healthcare - CDSS 3 will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models that automate complex tasks and improve decision-making. Understanding explainable deep learning models is crucial for Machine Learning Engineers in healthcare, as it enables them to build models that are transparent, interpretable, and trustworthy. This course provides a comprehensive understanding of explainability techniques, empowering Machine Learning Engineers to create reliable and ethically sound AI solutions for healthcare.

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Data Scientist

A Data Scientist employs data science methods and techniques to extract insight from data to make informed decisions. Explainable deep learning models have emerged as a valuable tool for Data Scientists as they provide a deeper understanding of how complex machine learning models make predictions, which can enhance the accuracy and reliability of data-driven decision-making. This course can help aspiring Data Scientists develop the skills to build and interpret explainable deep learning models for healthcare applications, a highly sought-after skill in the industry.

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Healthcare Data Analyst

Healthcare Data Analysts collect, analyze, and interpret healthcare data to identify trends, patterns, and insights that inform decision-making and improve patient outcomes. Explainable deep learning models are increasingly used in healthcare data analysis to uncover hidden insights and relationships in complex medical data. This course equips Healthcare Data Analysts with the knowledge and skills to leverage explainable deep learning models to extract meaningful insights from healthcare data, supporting evidence-based decision-making and improving patient care.

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Physician-Scientist

Physician Scientists are medical doctors who also conduct research. Explainable deep learning models are revolutionizing medical research by providing a deeper understanding of disease mechanisms and patient response. This course equips Physician Scientists with the skills to apply explainable deep learning models in their research, enabling them to contribute to the development of more effective and personalized treatments.

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Medical Imaging Scientist

Medical Imaging Scientists develop and use advanced imaging technologies to diagnose and treat medical conditions. Explainable deep learning models have transformed medical imaging, enabling the creation of AI-powered imaging systems that automate disease detection and improve diagnostic accuracy. This course provides Medical Imaging Scientists with the knowledge and skills to harness explainable deep learning models in their work, empowering them to develop and implement novel imaging solutions that enhance patient care.

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Pharmaceutical Scientist

Pharmaceutical Scientists develop and evaluate new drugs and therapies. Explainable deep learning models are revolutionizing drug discovery and development by providing a deeper understanding of drug mechanisms and patient response. This course provides Pharmaceutical Scientists with the skills to apply explainable deep learning models in their research, enabling them to contribute to the development of more effective and personalized treatments.

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Health Informatics Specialist

Health Informatics Specialists use information technology to improve healthcare delivery and outcomes. Explainable deep learning models are emerging as powerful tools for Health Informatics Specialists, as they enable the development of AI-powered healthcare applications that are transparent, interpretable, and trustworthy. This course equips Health Informatics Specialists with the knowledge and skills to integrate explainable deep learning models into their work, empowering them to create innovative solutions that enhance healthcare efficiency and quality.

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Clinical Research Associate

Clinical Research Associates manage and coordinate clinical trials to evaluate the safety and efficacy of new medical treatments. Explainable deep learning models are revolutionizing clinical research by providing a deeper understanding of patient data and improving the accuracy of clinical trial outcomes. This course provides Clinical Research Associates with the skills to apply explainable deep learning models in their work, enabling them to contribute to the development of more effective and personalized treatments.

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Medical Physicist

Medical Physicists apply physics principles to develop and use advanced technologies in healthcare. Explainable deep learning models are becoming integral to medical physics, enabling the development of more precise and personalized treatments. This course provides Medical Physicists with the foundation to understand and utilize explainable deep learning models in their work, empowering them to contribute to the design and implementation of cutting-edge medical technologies.

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Biomedical Engineer

Biomedical Engineers design and develop medical devices and technologies. Explainable deep learning models are transforming biomedical engineering by providing new insights and tools for improving the design and performance of medical devices. This course provides Biomedical Engineers with the skills to integrate explainable deep learning models into their work, empowering them to develop innovative medical technologies that enhance patient care.

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Healthcare Administrator

Healthcare Administrators manage the operations of healthcare organizations, including hospitals, clinics, and long-term care facilities. Explainable deep learning models are transforming healthcare administration by providing new insights and tools for improving efficiency, quality, and patient satisfaction. This course equips Healthcare Administrators with the knowledge and skills to leverage explainable deep learning models in their work, empowering them to make data-driven decisions that improve healthcare delivery.

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Biostatistician

Biostatisticians use statistical methods to design studies, analyze data, and interpret results in healthcare research. Explainable deep learning models are revolutionizing biostatistics by providing a deeper understanding of complex biological and medical processes. This course equips Biostatisticians with the skills to integrate explainable deep learning models into their research, enabling them to derive more accurate and interpretable insights from biomedical data.

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Healthcare Consultant

Healthcare Consultants provide expert advice to healthcare organizations on improving operations, efficiency, and patient care. Explainable deep learning models are transforming healthcare consulting by providing new insights and tools for optimizing healthcare delivery. This course equips Healthcare Consultants with the knowledge and skills to leverage explainable deep learning models in their consulting work, enabling them to develop innovative solutions that drive improvements in healthcare systems.

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Public Health Analyst

Public Health Analysts use data to identify and address public health issues. Explainable deep learning models are emerging as powerful tools for Public Health Analysts, as they enable the analysis of complex public health data and the identification of hidden patterns and trends. This course provides Public Health Analysts with the skills to apply explainable deep learning models in their work, empowering them to develop more effective and targeted public health interventions.

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Medical Writer

Medical Writers specialize in creating clear and accurate medical content for a variety of audiences. Explainable deep learning models are increasingly used in medical writing to simplify complex medical concepts and improve patient education. This course provides Medical Writers with the skills to understand and interpret explainable deep learning models, enabling them to effectively communicate complex medical information in a clear and engaging manner.

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Explainable deep learning models for healthcare - CDSS 3

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