ContentsI IntroductionI.1 Who should read this book?I.2 Book organizationII Health DataII.1 The growth of EHR AdoptionII.2 Health DataII.2.1 Life cycle of health dataII.2.2 Structured Health DataII.2.3 Unstructured clinical notesII.2.4 Continuous signalsII.2.5 Medical Imaging DataII.2.6 Biomedical data for in silico drug Discovery II.3 Health Data StandardsIII Machine Learning BasicsIII.1 Supervised LearningIII.1.1 Logistic RegressionIII.1.2 Softmax RegressionIII.1.3 Gradient DescentIII.1.4 Stochastic and Minibatch Gradient DescentIII.2 Unsupervised LearningIII.2.1 Principal component analysisIII.2.2 t-distributed stochastic neighbor embedding (t-SNE)III.2.3 ClusteringIII.3 Assessing Model PerformanceIII.3.1 Evaluation Metrics for Regression TasksIII.3.2 Evaluation Metrics for Classification TasksIII.3.3 Evaluation Metrics for Clustering TasksIII.3.4 Evaluation StrategyIII.4 Modeling ExerciseIII.5 Hands-On Practice34 CONTENTSIVDeep Neural Networks (DNN)IV.1 A Single neuronIV.1.1 Activation functionIV.1.2 Loss FunctionIV.1.3 Train a single neuronIV.2 Multilayer Neural NetworkIV.2.1 Network RepresentationIV.2.2 Train a Multilayer Neural NetworkIV.2.3 Summary of the Backpropagation AlgorithmIV.2.4 Parameters and Hyper-parametersIV.3 Readmission Prediction from EHR Data with DNNIV.4 DNN for Drug Property PredictionV EmbeddingV.1 OverviewV.2 Word2VecV.2.1 Idea and Formulation of Word2VecV.2.2 Healthcare application of Word2VecV.3 Med2Vec: two-level embedding for EHRV.3.1 Med2Vec MethodV.4 MiME: Embed Internal StructureV.4.1 Notations of MIMEV.4.2 Description of MIMEV.4.3 Experiment results of MIMEVI Convolutional Neural Networks (CNN)VI.1 CNN intuitionVI.2 Architecture of CNNVI.2.1 Convolution layer - 1DVI.2.2 Convolution layer - 2DVI.2.3 Pooling LayerVI.2.4 Fully Connected LayerVI.3 Backpropagation Algorithm in CNN*VI.3.1 Forward and Backward Computation for 1-D DataVI.3.2 Forward Computation and Backpropagation for 2-D ConvolutionLayer . VI.3.3 Special CNN ArchitectureVI.4 Healthcare Applications VI.5 Automated surveillance of cranial images for acute neurologic eventsVI.6 Detection of Lymph Node Metastases from Pathology ImagesVI.7 Cardiologist-level arrhythmia detection and classification in ambulatoryECGCONTENTS 5VIIRecurrent Neural Networks (RNN)VII.1Basic Concepts and NotationsVII.2Backpropagation Through Time (BPTT) algorithmVII.2.1Forward PassVII.2.2 Backward PassVII.3RNN VariantsVII.3.1 Long Short-Term Memory (LSTM)VII.3.2 Gated Recurrent Unit (GRU)VII.3.3 Bidirectional RNNVII.3.4 Encoder-Decoder Sequence-to-Sequence ModelsVII.4Case Study: Early detection of heart failureVII.5Case Study: Sequential clinical event predictionVII.6Case Study: De-identification of Clinical NotesVII.7Case Study: Automatic Detection of Heart Disease from electrocardiography(ECG) DataVIIAIutoencoders (AE)VIII.1OverviewVIII.2AutoencodersVIII.3Sparse AutoencodersVIII.4Stacked AutoencodersVIII.5Denoising AutoencodersVIII.6Case Study: "Deep Patient
About the Author: Dr. Cao ``Danica'' Xiao is the senior director, head of data science and machine learning at Amplitude. Before that, she was the director of machine learning in the analytics center of excellence of IQVIA. Before IQVIA, she was a research staff member in IBM Research. Her work focuses on developing machine learning and deep learning models to solve real-world healthcare challenges. She got her Ph.D. degree from University of Washington, Seattle.Dr. Jimeng Sun is a Health Innovation Professor at the Computer Science Department and Carle's Illinois College of Medicine in the University of Illinois Urbana-Champaign. His research focuses on artificial intelligence (AI) for healthcare, including deep learning for drug discovery, clinical trial optimization, computational phenotyping, clinical predictive modeling, treatment recommendation, and health monitoring. He completed his B.S. and M.Phil. in computer science at Hong Kong University of Science and Technology and his Ph.D. in computer science at Carnegie Mellon University.