AI & Healthcare
Machine Learning Applications in Healthcare
Apply machine learning to clinical data, patient risk, diagnosis, prognosis, medical text, and imaging.
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Course overview
Apply machine learning to clinical data, patient risk, diagnosis, prognosis, medical text, and imaging.
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What you will learn
In this course, you will develop a deep and comprehensive understanding of the healthcare system, exploring its intricate processes, the various interacting entities, and how they function together within the wider health sector. This foundational knowledge will provide you with a broad perspective on how healthcare operates, setting the stage for more advanced learning. You will delve into essential medical and healthcare concepts, gaining insights into the different types of clinical data, including electronic health records, imaging data, and genetic information, and how these data types are collected, managed, and utilized within healthcare settings.
The course will equip you with the skills to perform patient risk stratification and survival analysis, critical techniques for predicting patient outcomes and tailoring treatments to individual needs. You will learn how to apply machine learning models for disease diagnosis and prognosis, enhancing your ability to contribute to clinical decision-making and patient care. These models will include both traditional approaches and cutting-edge techniques, ensuring a thorough understanding of how machine learning can be leveraged for diverse healthcare challenges.
As you advance through the course, you will explore the application of various machine learning model types, including standard regression and classification models, which are foundational to many predictive tasks in healthcare. Additionally, you will learn about natural language processing (NLP) models and large language models (LLMs), which are increasingly used to analyze unstructured data like clinical notes, patient records, and medical literature. The course will demonstrate how these models are utilized in clinical settings for tasks such as diagnosing diseases, predicting patient outcomes, and personalizing treatment plans, as well as their broader applications within the healthcare system, such as optimizing hospital operations and improving public health initiatives.
By the end of the course, you will be equipped with a robust set of skills and knowledge that will enable you to apply machine learning techniques to real-world healthcare problems, driving innovation and improving patient outcomes. Whether you are a healthcare professional looking to enhance your technical skills or a data scientist interested in entering the healthcare field, this course will provide you with the tools and understanding needed to make a meaningful impact in the world of healthcare.
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Lecture syllabus
Video titles and durations as published on Arabsera. The advertised lecture total may differ from the number of video entries, which can include introductions, tutorials, and split sessions.
Video titles and durations
- L0: Overview
- P1-A-The healthcare system
- P1-B-Cinical data standards and formats
- P2-A-The hospitalization timeline
- P2-B-Risk stratification in practice
- P2-C-ML for risk stratification
- p2-D-Survival analysis
- P3-A-Textual sources in healthcare
- P3-B-NLP evolution
- Part3-C-Information retrieval in healthcare
- Part3-D-LLMs on healthcare text
- P4-A-Medical imaging sources
- P4-B-Preprocessing medical images
- P4-C-Learning from medical images
- P5-A-AI regulations and compliance
- P5-B-Transparence and fairness
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Teaching support (check your plan)
- Discussion groups
- TA-GPT (coming soon)
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Textbook
No textbook specified.
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Certificate
Awarded upon completion of all course activities.