LEADER 07464nam 2200445 450 001 9910488719203321 005 20220327090934.0 010 $a1-4842-7086-X 035 $a(CKB)5590000000516474 035 $a(MiAaPQ)EBC6676027 035 $a(Au-PeEL)EBL6676027 035 $a(OCoLC)1260347533 035 $a(CaSebORM)9781484270868 035 $a(EXLCZ)995590000000516474 100 $a20220327d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI for healthcare with Keras and Tensorflow 2.0 $edesign, develop, and deploy machine learning models using healthcare data /$fAnshik 210 1$a[Place of publication not identified] :$cApress,$d[2021] 210 4$d©2021 215 $a1 online resource (391 pages) 311 $a1-4842-7085-1 327 $aIntro -- Table of Contents -- About the Author -- About the Technical Reviewers -- Introduction -- Chapter 1: Healthcare Market: A Primer -- Different Stakeholders of the Healthcare Marketplace -- Regulators -- Food and Drug Administration (FDA) -- Center for Medicare and Medicaid Services (CMS) -- Center for Medicare and Medicaid Innovation (CMMI) -- Payers -- Providers -- Regulation of Healthcare Information -- AI Applications in Healthcare -- Screening -- Diagnosis -- Prognosis -- Response to Treatment -- What Is the Industry Landscape? -- Conclusion -- Chapter 2: Introduction and Setup -- Introduction to TensorFlow 2 -- TensorFlow Core -- TensorFlow JS -- TensorFlow Lite -- TensorFlow Extended -- TensorFlow 1.x vs 2.x -- What Is TF 1.x? -- Embracing TF 2.x -- Eager Execution -- AutoGraph -- TensorFlow Datasets -- tf.keras -- Estimators -- Recommendations for Best Use -- Installation and Setup -- Python Installation -- Using the Virtual Environment -- Library and Versions -- TensorFlow and GPU -- Others -- Conclusion -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- What Is EHR Data? -- MIMIC 3 Data: Setup and Introduction -- Access -- Introduction and Setup -- Data -- Social and Demographic -- Admissions Related -- Patient's Clinical Data -- Lab Events -- Comorbidity Score -- Modeling for Patient Representation -- A Brief Introduction to Autoencoders -- Feature Columns in TensorFlow -- Creating an Input Pipeline Using tf.data -- Creating Feature Columns -- Building a Stacked Autoencoder -- Cohort Discovery -- What Is an Ideal Cohort Set? -- Optimizing K-Means Performance -- Deciding the Number of Clusters by Inertia and Silhouette Score Analysis -- Checking Cluster Health -- Multitask Learning Model -- What Is Multitask Learning ? -- Different Ways to Train a MTL Model -- Training Your MTL Model -- Conclusion. 327 $aChapter 4: Predicting Medical Billing Codes from Clinical Notes -- Introduction -- Data -- NOTEEVENTS -- DIAGNOSES_ICD -- Understanding How Language Modeling Works -- Paying Attention -- Transforming the NLP Space: Transformer Architecture -- Positional Encoding -- Multi-Head Attention -- BERT: Bidirectional Encoder Representations from Transformers -- Input -- Token Embeddings -- Segment Embeddings -- Training -- Masked Language Modeling -- Next-Sentence Prediction -- Modeling -- BERT Deep-Dive -- What Does the Vocabulary Actually Contain? -- Training -- Conclusion -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Data -- Mapping Node Labels to OCR Output -- Node Features -- Hierarchical Layout -- Line Formation -- Graph Modeling Algorithm -- Input Data Pipeline -- What Are Graphs and Why Do We Need Them? -- Graph Convolutional Networks -- Convolutions over Graph -- Understanding GCNs -- Layer Stacking in GCNs -- Training -- Modeling -- Train-Test Split and Target Encoding -- Creating Flow for Training in StellarGraph -- Training and Model Performance Plots -- Conclusion -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Introduction -- Semi-Supervised Learning -- GANs -- Autoencoders -- Transfer Learning -- Weak Supervised Learning -- Exploring Snorkel -- Data Exploration -- Introduction -- Labeling Functions -- Regex -- Syntactic -- Distance Supervision -- Pipeline -- Writing Your LFs -- Working with Decorators -- Preprocessor in Snorkel -- Training -- Evaluation -- Generating the Final Labels -- Conclusion -- Chapter 7: Federated Learning and Healthcare -- Introduction -- How Does Federation Learning Work? -- Types of Federated Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Privacy Mechanism -- Secure Aggregation. 327 $aDifferential Privacy -- TensorFlow Federated -- Input Data -- Custom Data Load Pipeline -- Preprocessing Input Data -- Creating Federated Data -- Federated Communications -- Evaluation -- Conclusion -- Chapter 8: Medical Imaging -- What Is Medical Imaging? -- Image Modalities -- Data Storage -- Dealing with 2-D and 3-D Images -- Handling 2-D Images -- DICOM in Python -- EDA on DICOM Metadata -- View Position -- Age -- Sex -- Pixel Spacing -- Mean Intensity -- Handling 3-D Images -- NIFTI Format -- Introduction to MRI Image Processing -- Non-Even Pixel Distribution -- Correlation Test -- Cropping and Padding -- Image Classification on 2-D Images -- Image Preprocessing -- Histogram Equalization -- Isotropic Equalization of Pixels -- Model Creation -- Preparing Input Data -- Training -- Image Segmentation for 3-D Images -- Image Preprocessing -- Bias Field Correction -- Removing Unwanted Slices -- Model Creation -- Preparing Input Data -- Training -- Performance Evaluation -- Transfer Learning for Medical Images -- Conclusion -- References -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life -- Introduction -- Getting Data -- Designing Your Q& -- A -- Retriever Module -- Query Paraphrasing -- Retrieval Mechanics -- Term/Phrase-Based -- Semantic-Based -- Reranking -- Comprehension -- BERT for Q& -- A -- Fine-Tuning a Q& -- A Dataset -- Final Design and Code -- Step 0: Preparing the Document Data -- Step 1: BERT-QE Expansion -- Step 1.1: Extract the Top k Documents for a Query Using BM-25 -- Step 1.2: Relevance Score on the Top 200 Documents -- Step 2: Semantic Passage Retrieval -- Step 3: Passage Reranking Using a Fine-Tuned Covid BERT Model on the Med-Marco Dataset -- Step 4: Comprehension -- Conclusion -- Chapter 10: You Need an Audience Now -- Demystifying the Web -- How Does an Application Communicate?. 327 $aCloud Technology -- Docker and Kubernetes -- Why Docker? -- OS Virtualization -- Kubernetes -- Deploying the QnA System -- Building a Flask Structure -- Deep Dive into app.py -- Understanding index.html -- Dockerizing Your Application -- Creating a Docker Image -- Base Image and FROM Command -- COPY and EXPOSE -- WORKDIR, RUN, and CMD -- Dockerfile -- Building Docker Image -- Making It Live Using Heroku -- Conclusion -- Index. 606 $aArtificial intelligence$xMedical applications 615 0$aArtificial intelligence$xMedical applications. 676 $a610.285 700 $aAnshik$0898282 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910488719203321 996 $aAI for healthcare with Keras and Tensorflow 2.0$92820433 997 $aUNINA