Cao Xiao - Introduction to Deep Learning for Healthcare
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- Book:Introduction to Deep Learning for Healthcare
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This textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors increasing use. The authors present deep learning case studies on all data described.
Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. Its presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching.
This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.
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This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Life can only be understood backwards, but it must be lived forwards.
Sren Kierkegaard
Deep learning models are multi-layer neural networks that have shown great success in diverse applications. This is a book describing deep learning models in the context of healthcare applications.
When we took an artificial intelligence class many year ago, many topics were covered, including neural networks. The neural network model was presented as a supervised learning method. However, it was considered a practical failure compared to other more effective supervised learning methods such as decision trees and support vector machine. The common explanation about neural networks at the time involves two aspects: (1) Multi-layer neural networks can approximate any arbitrary functions and hence is a theoretically powerful model. (2) In practice, they dont work well due to the ineffective learning algorithm (i.e., backpropagation method). When we asked why backpropagation doesnt work well, a typical answer was about the accumulated errors across layers, which will eventually become too big to lead to an accurate model. Of course, the understanding of neural networks has evolved greatly in the past few years. When big labeled datasets and parallel computing infrastructure such as graphic processing units (GPU) finally become available, the power of deep neural networks will be unleashed. These days, deep learning models have become the most popular and standard machine learning models.
When we first got into machine learning for healthcare many years ago, we spoke with a senior medical doctor about the potential impact of machine learning and artificial intelligence (AI) in medicine in the future. Specifically, we asked him about the possibility of creating AI algorithms to mimic the practice of real-world doctors. He was very pessimistic about the possibility because he believes doctors largely depend on medical intuition to do their job, which is impossible to be learned by algorithms. Of course, now we know it is not only possible, but often AI algorithms can outperform human experts in various clinical pattern recognition tasks such as diagnosis. Even commercial medical devices have now become available (e.g., atrial fibrillation detection algorithm in Apple Watch). Many rely on deep learning models. Before we finished the book, we saw that doctors profile on LinkedIn listed as an innovator in AI for healthcare.
Humans are the only species on earth that can actively and systematically improve their health via technologies in the form of medicine. Throughout history, human knowledge is the driving force for the progress of medicine and healthcare. Humans created new technologies such as diagnostic tests, drugs, medical procedures, and devices. As the life expectancy increases, healthcare cost is growing dramatically over the years to be deemed unsustainable. For example, the US healthcare cost in 2019 alone is over 3.6 trillion dollars and accounts for 17.8% of gross domestic product (GDP). Within the gigantic spending in healthcare, there is enormous waste that should be avoided. The estimated total annual costs of waste were $760 billion to $935 billion [].
Meanwhile, mountains of new medical knowledge are being created, making human doctors knowledge quickly outdated. Moreover, human doctors are struggling to catch up with the increasing volume of patient visits. Physician burnout is a serious issue that affects all doctors in the age of electronic health records due to the overwhelming patient data for doctors to review and complex workflows, including tedious documentation tasks. Patients are also dissatisfied with limited interactions and attention from doctors during their short clinical visits. Quality of care is often sub-optimal, with over 400K preventable medical errors in hospitals each year [].
Data: Large amounts of rich data, especially in images and natural language texts, become available for training deep learning models.
Algorithms: Efficient neural network methods have been proposed and enhanced by many researchers in recent years.
Hardware: Advances in parallel computing, especially graphic process units (GPUs), have enabled a fast and affordable computing engine for deep learning workload.
Software: Scalable and easy-to-use programming frameworks have been developed and released via open source projects to the public. Most of them, including TensorFlow and Pytorch, have strong support from the technology industry.
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