Nursing supports multiple care, organizational and communicative processes that are essential to provide patients with the best care. However, in the healthcare environment, digitalized resources such as Artificial Intelligence (AI) tools that facilitate the remote monitoring of patients and other Telemedicine tasks are increasingly advancing and having a greater impact. Therefore, professionals in the sector will find in this TECH program an unparalleled academic opportunity to update their knowledge. A university program that brings together the latest trends, applications and protocols, based on AI, which are used today to optimize patient care. All this through a 100% online academic itinerary, without strict schedules for study and the teaching guidance of the best experts in the sector.

A 100% online and comprehensive program with which you will delve into the key tools of Artificial Intelligence that can be applied in nursing practice”

Nursing is one of the pillars of healthcare, since multiple care, organizational and communication processes depend on it, and it facilitates interdisciplinary work. Likewise, the advance of digitalization in clinical environments has led to nurses being required to have more and better skills to cope with the traditional tasks of the sector, at the same time as taking on new challenges. One of these challenges has been the incorporation of Artificial Intelligence into areas such as Telemedicine, the management of patient databases or to maximize the control of care inputs.

In this context, nurses are invited to update their skills and, at the same time, to develop broad profiles that allow them to access new job opportunities. From these demands arises the Professional master’s degree in Artificial Intelligence in Nursing from TECH. This comprehensive program covers innovative concepts on the use of new digital technologies, based on AI, to improve efficiency and patient care.

The curriculum delves into general topics on Artificial Intelligence tools and then has specific modules for nurses, where the applications of these resources are analyzed to address the Nutrition of patients, or monitor their recovery after any procedure. Therefore, after delving into all these contents, nurses will be able to lead digital health projects and develop personalized care, increasing their value for more competitive job opportunities.

At the same time, this program has a 100% online methodology with which nurses will have the ease of studying and at the same time continue their work or personal obligations. At the same time, the syllabus is accessible 24 hours a day, 7 days a week, from any device with an Internet connection, although it can also be downloaded. On the other hand, the teaching-learning process is based on the implementation of the Relearning method, which facilitates the assimilation of key concepts through repetition. 

Get trained to adapt to technological changes in the healthcare field through this comprehensive program in Artificial Intelligence in Nursing”

This Professional master’s degree in Artificial Intelligence in Nursing contains the most complete and up-to-date program on the market. The most important features include: 

• The development of case studies presented by experts with a deep mastery of Artificial Intelligence tools that facilitate the work of nurses in clinics, hospitals and other care centers
• The graphic, schematic, and practical contents which provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

As a nurse trained in AI applications, you will contribute to the development of technological innovation projects to improve the efficiency and accuracy of patient care”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts in the field of educational coaching with extensive experience.

You will expand your skills and knowledge about AI and its impact on nursing practice through the analysis of simulated real cases"

You will have the Relearning method, in which TECH is a pioneer, to address the key concepts of Artificial Intelligence in Nursing and assimilate them through repetition"

The syllabus of this Professional master’s degree offers a complete tour from the basics of AI to APP tools specialized in health. In its modules, nurses will delve into areas such as the creation of personalized conversational assistants to optimize clinical care, the use of Virtual Reality in emotional support and rehabilitation, as well as other resources that allow them to personalize the care and monitoring of patients remotely. In addition, the entire curriculum has a disruptive teaching methodology, based on the reiteration of key concepts through a Relearning system and is taught 100% online.

You will have at your disposal advanced multimedia resources, such as explanatory videos and interactive summaries, for your comprehensive training with this TECH program”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Film
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation Semantic Web

1.6. Semantic Web

1.6.1. Specifications: RDF, RDFS and OWL
1.6.2. Inference/ Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, WhatsApp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative. Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to Its Shape

2.2.2.1. Numeric
2.2.2.2. Text:
2.2.2.3. Logical

2.2.3. According to Its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
2.7.3. Data Mining

2.8. Datawarehouse

2.8.1. Elements that Comprise It
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory Aspects

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model
3.10.3. Useful Tools

Module 4. Data Mining. Selection, Pre-Processing and Transformation

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data
4.3.3. Transforming Attributes

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data Vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Pre-Processing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Merge Sort
5.3.6. Quick Sort

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology

6.6. Ontology Languages and Ontology Creation Software

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Pre-Processing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. Algorithm C
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10. Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural Networks, the Basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Operations

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Hidden Layer
8.3.3. Output Layer

8.4. Layer Bonding and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the First Neural Network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Transfer Learning Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Transfer Learning Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Transfer Learning Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and Training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Grap Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Pre-Processing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tfdata API

10.6.1. Using the tf.data API for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Pre-Processing Layers

10.8.1. Using the Keras Pre-Processing API
10.8.2. Pre-Processing Pipelined Construction with Keras
10.8.3. Using the Keras Pre-Processing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets for Data Loading
10.9.2. Preprocessing Data with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Applications
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Using the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-Trained Keras Models

11.6.1. Characteristics of Pre-Trained Models
11.6.2. Uses of Pre-Trained Models
11.6.3. Advantages of Pre-Trained Models

11.7. Pre-Trained Models for Transfer Learning

11.7.1. Learning by Transfer
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.1. Edge Detection
11.10.1. Rule-Based Segmentation Methods

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

12.1. Text Generation Using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Using Transformers Models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Pre-Processing
12.7.3. Training a Transformers Model for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using Hugging Face's Transformers Library
12.8.2. Hugging Face’s Transformers Library Application
12.8.3. Advantages of Hugging Face’s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Applications

12.10.1. Development of a Natural Language Processing Application with RNN and Attention
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Automatic Encoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Application
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing

4.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services. Opportunities and Challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/Uses of AI

15.2. Implications of Artificial Intelligence in Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Uses

15.3. Risks Related to the Use of AI in Healthcare Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/Uses of AI

15.4. Retail

15.4.1. Implications of AI in Retail. Opportunities and Challenges
15.4.2. Case Uses
15.4.3. Potential Risks Related to the Use of AI
15.4.4. Potential Future Developments/Uses of AI

15.5. Industry

15.5.1. Implications of AI in Industry. Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential Risks Related to the Use of AI in Industry

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/Uses of AI

15.7. Public Administration

15.7.1. AI Implications for Public Administration. Opportunities and Challenges
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Educational

15.8.1. AI Implications for Education. Opportunities and Challenges
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/Uses of AI

15.9. Forestry and Agriculture

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Uses
15.9.3. Potential Risks Related to the Use of AI
15.9.4. Potential Future Developments/Uses of AI

15.10. Human Resources

15.10.1. Implications of AI for Human Resources. Opportunities and Challenges
15.10.2. Case Uses
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/Uses of AI

Module 16. Application of Conversational Assistants in Artificial Intelligence for Nursing

16.1. Introduction to Conversational Assistants in AI for Nursing

16.1.1. Context of AI in Health and Its Application in Nursing
16.1.2. Benefits of Conversational Assistants in Nursing Care
16.1.3. Specific Applications in Nursing
16.1.4. Trends in Conversational Assistants in the Healthcare Sector

16.2. Types of Conversational Assistants in Healthcare

16.2.1. Types of Conversational Assistants in Healthcare (Synthesia, Heygen)
16.2.2. Differences between Assistants in Support, Diagnostic, and Follow-Up Functions
16.2.3. Examples of Conversational Assistants and Nursing Use Cases
16.2.4. Comparison between Automated Assistants and Hybrid Assistants (with Human Intervention)

16.3. Implementation of Conversational Assistants in Healthcare

16.3.1. Advantages of Assistants in the Healthcare Environment for Nurses
16.3.2. Challenges in the Implementation of Assistants in Clinical Processes
16.3.3. Technical Requirements for Implementation in Healthcare
16.3.4. Evaluation of Effectiveness and Benefits in the Educational Healthcare Setting

16.4. Creating Personalized Assistants in ChatGPT

16.4.1. Introduction to the Creation of a Chatbot in ChatGPT
16.4.2. Process of Customizing a Nursing Assistant (Part 1)
16.4.3. Process of Customizing a Nursing Assistant (Part 2)
16.4.4. Practical Examples of Personalized Healthcare Assistants

16.5. Impact of AI and Automation in the Health Sector

16.5.1. Changes in Job Roles Due to AI
16.5.2. Adaptation of Nursing Professionals to AI Technologies
16.5.3. Effects of Conversational Assistants on the Training of the Healthcare Workforce
16.5.4. Evaluation of the Impact of Automation on the Healthcare Sector

16.6. Integrating Conversational Assistants in Nursing Education

16.6.1. Role of Conversational Assistants in Clinical Learning
16.6.2. Using Conversational Assistants in Clinical Case Simulations
16.6.3. Application in Clinical Practice and Decision Making
16.6.4. Tools for Continuing Education with Assistants

16.7. Conversational Assistants in the Emotional Support of Patients

16.7.1. Applications of Assistants for Emotional Accompaniment
16.7.2. Examples of Conversational Assistants in Psychological Support
16.7.3. Limitations in the Emotional Support of Conversational Assistants
16.7.4. Considerations for the Use of AI in Emotional Support

16.8. Improving Efficiency and Patient Care with AI Assistants

16.8.1. Managing Queries and Frequently Asked Questions with Assistants
16.8.2. Optimizing Patient-Nurse Communication
16.8.3. Assistant Applications in Care Coordination
16.8.4. Evaluation of the Impact of Assistants on Clinical Efficiency

16.9. Development and Customization of Conversational Tools for Nurses

16.9.1. Process of Developing a Conversational Nurse Assistant from Scratch
16.9.2. Customization for Specific Nursing Needs
16.9.3. Updating and Continuous Improvement of Conversational Assistants
16.9.4. Implementing Assistants in Various Healthcare Settings

16.10. Virtual Learning and Continuing Education in AI for Nursing

16.10.1. Importance of AI Continuous Learning for Nursing
16.10.2. AI E-Learning Platforms and Assistants
16.10.3. AI Professional Development for Healthcare Workers
16.10.4. Future of AI Training for Nursing and Healthcare Workers

Module 17. Using Artificial Intelligence and Virtual Reality in Emotional Support in Nursing

17.1. Introduction to AI-Assisted Emotional Support (Woebot)

17.1.1. Concept and Relevance of Emotional Support in AI
17.1.2. Benefits and Limitations of AI Emotional Support
17.1.3. Main Applications in the Field of Mental Health
17.1.4. Differences with Traditional Emotional Support

17.2. Chatbots in Emotional Support

17.2.1. Types of Chatbots Available for Emotional Support (Replika, Wysa)
17.2.2. Examples of Mental Health Chatbots
17.2.3. Limitations of Chatbots in Emotional Support
17.2.4. Case Studies of the Use of Chatbots in the Healthcare Sector

17.3. AI Tools for Mental Health (Youper, Koko)

17.3.1. AI Success Stories in Mental Health
17.3.2. Current Emotional Support Tools
17.3.3. Integrating AI in Mental Health Therapies
17.3.4. Measuring the Effectiveness of AI Tools

17.4. Privacy and Security in AI-Assisted Emotional Support

17.4.1. Importance of Privacy in AI-Assisted Emotional Support
17.4.2. Privacy Regulations in the Use of AI in Healthcare
17.4.3. Data Security in Emotional Support Systems
17.4.4. Ethics and Protection of Sensitive Information

17.5. Comparison between Traditional Emotional Support and Emotional Support with AI

17.5.1. Current Challenges in Both Approaches
17.5.2. Benefits of Combining AI with Traditional Methods
17.5.3. Case Studies in Mixed Emotional Support
17.5.4. Implementation Challenges and Acceptance of AI Support

17.6. Virtual Reality in Patient Care (Psious, RelieVRx)

17.6.1. Introduction to Virtual Reality in Healthcare
17.6.2. Virtual Reality Devices and Their Medical Application
17.6.3. Virtual Reality in Patient Preparation
17.6.4. Evolution of Virtual Reality in Healthcare

17.7. Virtual Reality Applications in Rehabilitation (MindMotion, VRHealth)

17.7.1. Using Virtual Reality in Motor Rehabilitation
17.7.2. Pain Management Using Virtual Reality
17.7.3. Treatment of Phobias and Anxiety Disorders
17.7.4. Examples of Successful Rehabilitation with Virtual Reality

17.8. Ethical Considerations in the Use of Virtual Reality

17.8.1. Ethics in Virtual Reality Treatments
17.8.2. Patient Safety in Virtual Environments
17.8.3. Risks of Addiction and Overexposure to Virtual Reality
17.8.4. Regulations in the Use of Virtual Reality in Healthcare

17.9. Comparison of Traditional Treatments and Virtual Reality

17.9.1. Differences in the Effectiveness of Both Approaches
17.9.2. Use Cases for Mixed Treatments
17.9.3. Cost-Benefit Analysis
17.9.4. Expert Opinion on the Use of Virtual Reality

17.10. Future of Virtual Reality in Patient Care

17.10.1. Technological Advances in Virtual Reality Applied to Healthcare
17.10.2. Predictions on the Impact of Virtual Reality on Healthcare
17.10.3. Integrating Virtual Reality into Regular Medical Practices
17.10.4. Future Possibilities for Virtual Reality Training

Module 18. Clinical Management and Personalization of Care with Artificial Intelligence

18.1. Introduction to Clinical Management with AI (IBM Watson Health)

18.1.1. Basic Concepts of AI-Assisted Clinical Management
18.1.2. Importance of AI in the Optimization of Clinical Resources
18.1.3. Successful Cases in the Implementation of AI in Hospitals
18.1.4. Analysis of Results and Improvements in Clinical Management

18.2. Optimizing Hospital Resources with AI (Qventus)

18.2.1. Bed and Resource Management with AI
18.2.2. AI in Medical Equipment Management
18.2.3. Integration of AI with Existing Hospital Systems
18.2.4. Benefits and Challenges of Automation in Clinical Resources

18.3. Comparison of Traditional and AI Tools

18.3.1. Differences in the Efficiency of Traditional and AI Tools
18.3.2. Advantages of AI Tools in Clinical Management
18.3.3. Cost Analysis of Traditional vs. AI Tools
18.3.4. Case Studies of the Application of AI Tools

18.4. AI in Schedule and Appointment Management (Zocdoc, Qure4u)

18.4.1. Optimizing Clinical Schedules Using AI
18.4.2. AI for Appointment Management and Consultation Scheduling
18.4.3. Reducing Waiting Times through AI
18.4.4. Efficiency in the Allocation of Time Resources with AI

18.5. Remote Patient Monitoring with AI (Current Health, Biofourmis)

18.5.1. Introduction to Remote Patient Monitoring
18.5.2. AI Tools for Remote Monitoring
18.5.3. Early Warning Systems in Assisted Monitoring
18.5.4. Telemedicine Platforms with AI

18.6. AI Applications in Chronic Diseases (Glytec, Kaia Health)

18.6.1. Using AI to Monitor Chronic Diseases
18.6.2. Using ORMON CONNECT
18.6.3. Comparison of Traditional and AI-Assisted Monitoring
18.6.4. Benefits of AI in the Management of Chronic Diseases

18.7. Ethical Considerations in AI Monitoring

18.7.1. Ethics in the Use of AI in Patient Monitoring
18.7.2. Data Protection in Remote Monitoring
18.7.3. Privacy Regulations in AI Systems
18.7.4. Examples of Successful and Ethical Practices in Monitoring

18.8. Personalized Care Management Using AI

18.8.1. Introduction to Personalized Care with AI
18.8.2. Clinical Decision Support Systems
18.8.3. Creating Personalized Advice with ChatGPT
18.8.4. AI Tools for Care Personalization

18.9. Care Planning with AI (Mediktor)

18.9.1. Creating Personalized Care Plans
18.9.2. Benefits and Applications of Assisted Care Plans
18.9.3. Comparison of Traditional and Personalized Care
18.9.4. Case Studies of AI Care Plans

18.10. Implementing Personalized Nursing Plans

18.10.1. Implementing AI in Personalized Nursing
18.10.2. Case Studies on Care Personalization with AI
18.10.3. Implementation Strategies in Care Plans
18.10.4. Future of AI in Nursing and Personalized Care

Module 19. Physical Activity Improvement with Artificial Intelligence and Virtual Reality for Nursing

19.1. Introduction to AI in Physical Activity (Google Fit)

19.1.1. Importance of AI in the Field of Physical Activity
19.1.2. Applications of AI in Fitness Tracking
19.1.3. Advantages of Using AI to Improve Physical Performance
19.1.4. Successful Cases of AI in Training Optimization

19.2. AI Tools for Physical Activity Tracking (Whoop, Google Fit)

19.2.1. Types of AI Tracking Devices
19.2.2. Smart Sensors and Wearables
19.2.3. Advantages of Using AI for Continuous Monitoring
19.2.4. Examples of Monitoring Platforms

19.3. Virtual and Augmented Reality in Physical Training

19.3.1. Introduction to Virtual Reality (VR) and Augmented Reality (AR)
19.3.2. Applying VR and AR in Fitness Programs
19.3.3. Benefits of Immersion in Extended Reality Environments
19.3.4. Case Studies of VR and AR Training

19.4. Platforms and Apps for Physical Activity Tracking (MyFitnessPal, Jefit)

19.4.1. Mobile Apps for Physical Activity Monitoring
19.4.2. Innovative AI-Based Platforms
19.4.3. Comparison of Traditional and AI Applications
19.4.4. Examples of Popular Platforms

19.5. Customizing AI Training Plans

19.5.1. Creating Customized Training Plans
19.5.2. Data Analysis for Real-Time Adjustments
19.5.3. AI in the Optimization of Routines and Targets
19.5.4. Examples of Customized Plans

19.6. Motivation and Progress Tracking with AI Tools

19.6.1. AI for Progress and Performance Analysis
19.6.2. AI-Assisted Motivation Techniques
19.6.3. Real-Time Feedback and Personalized Motivation
19.6.4. Success Stories in Improving Exercise Adherence

19.7. Comparative Analysis of Traditional and AI Methods

19.7.1. Efficiency of Traditional Methods vs. AI
19.7.2. Costs and Benefits of Using AI in Training
19.7.3. Challenges and Limitations of Technology in Physical Training
19.7.4. Expert Opinion on the Impact of AI

19.8. Ethics and Privacy in Monitoring Physical Activity with AI

19.8.1. Protection of Personal Data in AI Tools
19.8.2. Privacy Regulations in AI Devices
19.8.3. Liability in the Use of Physical Activity Data
19.8.4. Ethics in Monitoring and Analysis of Personal Data

19.9. Future of AI in Training and Physical Activity

19.9.1. Technological Advances in AI and Fitness
19.9.2. Predictions on the Impact of AI on Physical Activity
19.9.3. Possibilities for Development in Extended Reality
19.9.4. Long-Term Vision of AI in the Sports Environment

19.10. Case Studies in Physical Activity Improvement with AI

19.10.1. Case Studies on Training Optimization
19.10.2. Experiences of Users in Improving Their Performance
19.10.3. Analysis of Data from AI and Fitness Studies
19.10.4. Results and Conclusions on the Impact of AI

Module 20. Optimizing Nutrition and Health Education with Artificial Intelligence in Nursing

20.1. Principles of Personalized Nutrition with AI in Nursing

20.1.1. Fundamentals of Personalized Nutrition
20.1.2. Role of AI in Individualized Nutrition
20.1.3. Benefits of Personalization in Nutritional Plans
20.1.4. Examples of Success in Personalized Nutrition

20.2. AI Applications for Nutrition

20.2.1. AI Mobile Nutrition Applications (MyFitnessPal, Foodvisor, Yazio)
20.2.2. Dietary Tracking Tools
20.2.3. Comparison of AI Apps for Nutrition
20.2.4. Review of Popular Applications

20.3. Personalized Nutrition Assistants

20.3.1. AI for Nutritional Recommendations (Nutrino, Viome, Noom)
20.3.2. Virtual Assistants in Nutrition
20.3.3. Examples of Personalization in Nutrition
20.3.4. Challenges in the Development of Nutritional Assistants

20.4. Comparison of Traditional and AI Tools in Nutrition

20.4.1. Effectiveness of Traditional vs. AI Methods
20.4.2. Benefits of AI over Conventional Tools
20.4.3. Costs and Accessibility of AI Tools
20.4.4. Comparative Case Studies

20.5. Future of AI-Assisted Nutrition

20.5.1. Technological Innovations in Nutrition
20.5.2. Predictions on the Impact of AI in Nutrition
20.5.3. Future Challenges in the Personalization of Nutrition
20.5.4. Long-Term Vision of AI in Nutrition

20.6. AI Tools for Outreach and Health Education

20.6.1. Introduction to AI Tools in Health Education
20.6.2. Guide for Creating Effective Educational Prompts
20.6.3. Introduction to Gemini
20.6.4. Introduction to ChatGPT

20.7. Optimizing Educational Searches with AI

20.7.1. AI-Assisted Search Engines
20.7.2. Examples of Search Engines in Health Education
20.7.3. Advanced AI Search Functions
20.7.4. Using Special Operators to Improve Searches

20.8. AI-Enhanced Academic Presentations

20.8.1. AI Tools for Academic Presentations
20.8.2. ChatGPT for Scientific Presentations
20.8.3. Gemini for Event Presentations
20.8.4. Additional Platforms such as Gamma.app, Beautiful AI and Tome

20.9. Creating Scientific Posters with AI

20.9.1. Introduction to AI Tools for Posters
20.9.2. Visme as a Tool for Scientific Posters
20.9.3. Biorender for Visualizing Scientific Information
20.9.4. Jasper and Canva in the Creation of Posters

20.10. Creating Educational Avatars and Assistants

20.10.1. AI Applied to the Creation of Educational Avatars
20.10.2. Conversation Engines for Educational Assistants
20.10.3. Tools such as Heygen and Synthesia
20.10.4. Studio D-ID in the Creation of Interactive Avatars

This program composed of 20 academic modules is the most comprehensive in the university landscape on the applications of Artificial Intelligence in Nursing”