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. It's 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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