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Record Nr. |
UNINA9910488719203321 |
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Autore |
Anshik |
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Titolo |
AI for healthcare with Keras and Tensorflow 2.0 : design, develop, and deploy machine learning models using healthcare data / / Anshik |
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Pubbl/distr/stampa |
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[Place of publication not identified] : , : Apress, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (391 pages) |
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Disciplina |
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Soggetti |
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Artificial intelligence - Medical applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- 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 |
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-- 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. |
Chapter 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. |
Differential 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 |
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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?. |
Cloud 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. |
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