AI for healthcare with Keras and Tensorflow 2.0 design, develop, and deploy machine learning models using healthcare data
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Apress
[2021]
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631855206719 |
Tabla de Contenidos:
- 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
- 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 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.
