Ahondarás en la implementación del Big Data y en las técnicas de aprendizaje automático en la Investigación Clínica gracias a esta Postgraduate diploma”

Los fundamentos de Big Data son elementales para aprovechar el potencial de la información y los datos en el campo de la atención sanitaria. Entre sus principales aplicaciones a la Medicina, destaca el análisis de grandes cantidades de evidencias clínicas. Por ejemplo, los resultados de pruebas de laboratorio o los datos genómicos. De esta forma, los facultativos aprovechan estos recursos para diagnosticar enfermedades con mayor precisión y pronosticar el transcurso de las patologías. Así los facultativos garantizan a sus pacientes tratamientos más eficientes, al estar más adaptado a los individuos en función de sus necesidades personales. Asimismo, estos datos masivos contribuyen a identificar brotes en epidemias ante de que se propaguen, lo que implica una respuesta más rápida por parte de las autoridades sanitarias. 

En este contexto, TECH implementa un avanzado programa que ahondará en el procesamiento y análisis de textos en datos de salud. Bajo un enfoque eminentemente práctico, el plan de estudios abarca las ventajas de la IA en el campo de la salud. Así, el temario pone describe los métodos más avanzados para la recuperación de datos, para posteriormente realizar evaluaciones de calidad y seguridad en las informaciones almacenadas. También, la capacitación ahonda en los principales sistemas de apoyo para que los egresados tomen decisiones clínicas mediante la automatización inteligente. En relación con esto, los materiales didácticos aportan una visión holística sobre las innovaciones en el ámbito de la robótica quirúrgica entre los que se incluye el Sistema Da Vinci.

A su vez, la metodología implementada en este programa refuerza su carácter innovador. TECH ofrece un entorno educativo 100% online, adaptado a las necesidades de los profesionales en activo que buscan impulsar sus competencias. Igualmente, emplea el sistema de enseñanza Relearning, basado en la repetición de conceptos clave para fijar conocimientos y facilitar el aprendizaje. De esta manera, la combinación de flexibilidad y un enfoque pedagógico robusto, lo hace altamente accesible. Asimismo, los estudiantes accederán a una biblioteca atestada de recursos multimedia en diferentes formatos audiovisuales como resúmenes interactivos e infografías.  

Integrarás, tras esta titulación universitaria, herramientas de Inteligencia Artificial en las Historias Clínicas Electrónicas para detectar patologías de forma temprana y eficiente”

Esta Postgraduate diploma en Clinical Data Analysis and Treatment Personalization through Artificial Intelligence contiene el programa científico más completo y actualizado del mercado. Sus características más destacadas son: 

• El desarrollo de casos prácticos presentados por expertos en Inteligencia Artificial en Práctica Clínica
• Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información científica y práctica sobre aquellas disciplinas indispensables para el ejercicio profesional
• Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje
• Su especial hincapié en metodologías innovadoras 
• Las lecciones teóricas, preguntas al experto, foros de discusión de temas controvertidos y trabajos de reflexión individual
• La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet

Profundizarás, mediante este itinerario académico, en la importancia de la Ética durante el desarrollo de sistemas médicos de Inteligencia Artificial”  

El programa incluye en su cuadro docente a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio. 

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá al profesional un aprendizaje situado y contextual, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales. 

El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual el profesional deberá tratar de resolver las distintas situaciones de práctica profesional que se le planteen a lo largo del curso académico. Para ello, contará con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos.  

Analizarás de forma exhaustiva los modelos predictivos esenciales para la práctica clínica personalizada gracias a este exclusivo programa”

La metodología Relearning empleada en esta Postgraduate diploma conseguirá que adquieras habilidades de forma autónoma y progresiva. ¡A tu propia velocidad!”

This Postgraduate diploma will comprehensively address the impact of Artificial Intelligence on personalized medical care. To this end, the syllabus will cover the application of genomic-assisted analysis, delving into the interpretation of generic data to design specific therapeutic strategies. Likewise, the syllabus will offer students pioneering techniques to extract information from users that are currently implemented in the health sector. At the same time, they will master fundamental concepts of data mining and retrieval systems. Ethical aspects such as informed consent will also be included in the syllabus.

You will acquire a clinical approach based on data quality and integrity in the context of privacy regulations with this comprehensive study plan”

Module 1. Personalization of Healthcare through AI

1.1. AI Applications in Genomics for Personalized Medicine with DeepGenomics

1.1.1. Development of AI Algorithms for the Analysis of Genetic Sequences and their Relationship with Diseases
1.1.2. Use of AI in the Identification of Genetic Markers for Personalized Treatments
1.1.3. AI Implementation for Fast and Accurate Interpretation of Genomic Data
1.1.4. AI Tools in the Correlation of Genotypes with Drug Responses

1.2. AI in Pharmacogenomics and Drug Design using AtomWise

1.2.1. Development of AI Models for Predicting Drug Efficacy and Safety
1.2.2. Use of AI in the Identification of Therapeutic Targets and Drug Design
1.2.3. Application of AI in the Analysis of Gene-Drug Interactions for Treatment Personalization
1.2.4. Implementation of AI Algorithms to Accelerate the Discovery of New Drugs

1.3. Personalized Monitoring with Smart Devices and AI

1.3.1. Development of Wearables with AI for Continuous Monitoring of Health Indicators
1.3.2. Use of AI in the Interpretation of Data Collected by Smart Devices with FitBit
1.3.3. Implementation of AI-Based Early Warning Systems for Health Conditions
1.3.4. AI Tools for Personalization of Lifestyle and Health Recommendations

1.4. Clinical Decision Support Systems with AI

1.4.1. Implementation of AI to Assist Physicians in Clinical Decision Making with Oracle Cerner
1.4.2. Development of AI Systems that Provide Recommendations Based on Clinical Data
1.4.3. Use of AI in the Evaluation of Risks and Benefits of Different Therapeutic Options
1.4.4. AI Tools for Real-time Health Data Integration and Analysis

1.5. Trends in Health Personalization with AI

1.5.1. Analysis of the Latest Trends in AI for Healthcare Personalization
1.5.2. Use of AI in the Development of Preventive and Predictive Approaches in Health
1.5.3. Implementing AI in Adapting Health Plans to Individual Needs
1.5.4. Exploring New AI Technologies in the Field of Personalized Health

1.6. Advances in AI-assisted Surgical Robotics with Intuitive Surgical's da Vinci Surgical System

1.6.1. Development of Surgical Robots with AI for Precise and Minimally Invasive Procedures
1.6.2. Using AI to Create Predictive Disease Models Based on Individual Data
1.6.3. Implementation of AI Systems for Surgical Planning and Simulation of Operations
1.6.4. Advances in the Integration of Tactile and Visual Feedback in Surgical Robotics with AI

1.7. Development of Predictive Models for Personalized Clinical Practice

1.7.1. Using AI to Create Predictive Disease Models Based on Individual Data
1.7.2. Implementation of AI in Predicting Treatment Responses
1.7.3. Development of AI Tools for Anticipating Health Risks
1.7.4. Application of Predictive Models in the Planning of Preventive Interventions

1.8. AI in Personalized Pain Management and Treatment with Kaia Health

1.8.1. Development of AI Systems for Personalized Pain Assessment and Management
1.8.2. Use of AI in Identifying Pain Patterns and Responses to Treatments
1.8.3. Implementation of AI Tools in Customizing Pain Therapies
1.8.4. Application of AI in Monitoring and Adjusting Pain Treatment Plans

1.9. Patient Autonomy and Active Participation in Personalization

1.9.1. Promoting Patient Autonomy through AI Tools for Patient Health Management with Ada Health
1.9.2. Development of AI Systems that Empower Patients in Decision Making
1.9.3. Using AI to Provide Personalized Information and Education to Patients
1.9.4. AI Tools that Facilitate Active Patient Participation in Treatment

1.10. Integration of AI in Electronic Medical Records with Oracle Cerner

1.10.1. AI Implementation for Efficient Analysis and Management of Electronic Medical Records
1.10.2. Development of AI Tools for Extracting Clinical Insights from Electronic Medical Records
1.10.3. Using AI to Improve Accuracy and Accessibility of Data in Medical Records
1.10.4. Application of AI for the Correlation of Clinical History Data with Treatment Plans

Module 2. Big Data Analysis in the Health Sector with AI

2.1. Fundamentals of Big Data in healthcare

2.1.1. The Explosion of Data in the Health Field
2.1.2. Concept of Big Data and Main Tools
2.1.3. Applications of Big Data in Healthcare

2.2. Text Processing and Analysis in Health Data with KNIME and Python

2.2.1. Concepts of Natural Language Processing
2.2.2. Embedding Techniques
2.2.3. Application of Natural Language Processing in Healthcare

2.3. Advanced Methods of Data Retrieval in Health with KNIME and Python

2.3.1. Exploration of Innovative Techniques for Efficient Data Retrieval in Healthcare 
2.3.2. Development of Advanced Strategies for Information Extraction and Organization in Healthcare Settings 
2.3.3. Implementation of Adaptive and Customized Data Retrieval Methods for Various Clinical Contexts 

2.4. Quality Assessment in Health Data Analysis with KNIME and Python

2.4.1. Development of Indicators for Rigorous Assessment of Data Quality in Healthcare Settings
2.4.2. Implementation of Tools and Protocols to Ensure the Quality of Data Used in Clinical Analysis
2.4.3. Continuous Assessment of the Accuracy and Reliability of Results in Health Data Analysis Projects

2.5. Data Mining and Machine Learning in Healthcare with KNIME and Python

2.5.1. Main Methodologies for Data Mining
2.5.2. Health Data Integration
2.5.3. Detection of Patterns and Anomalies in Health Data

2.6. Innovative Areas of Big Data and AI in Healthcare

2.6.1. Exploring New Frontiers in the Application of Big Data and AI to Transform the Healthcare Sector
2.6.2. Identifying Innovative Opportunities for the Integration of Big Data and AI Technologies in Medical Practices 
2.6.3. Development of Cutting-Edge Approaches to Maximize the Potential of Big Data and AI in Healthcare 

2.7. Collection and Preprocessing of Medical Data with KNIME and Python

2.7.1. Development of Efficient Methodologies for Medical Data Collection in Clinical and Research Settings 
2.7.2. Implementation of Advanced Preprocessing Techniques to Optimize the Quality and Utility of Medical Data 
2.7.3. Design of Collection and Preprocessing Strategies that Ensure the Confidentiality and Privacy of Medical Information 

2.8. Data Visualization and Communication in Health with Tools such as PowerBI and Python

2.8.1. Design of Innovative Visualization Tools in Health
2.8.2. Creative Health Communication Strategies
2.8.3. Integration of Interactive Technologies in Health

2.9. Data Security and Governance in the Health Sector

2.9.1. Development of Comprehensive Data Security Strategies to Protect Confidentiality and Privacy in the Health Sector
2.9.2. Implementation of Effective Governance Frameworks to Ensure Ethical and Responsible Data Management in Medical Settings
2.9.3. Design of Policies and Procedures to Ensure the Integrity and Availability of Medical Data, Addressing Challenges Specific to the Healthcare Sector

2.10. Practical applications of Big Data in healthcare

2.10.1. Development of Specialized Solutions for Managing and Analyzing Large Data Sets in Healthcare Environments
2.10.2. Use of Practical Tools Based on Big Data to Support Clinical Decision Making
2.10.3. Application of Innovative Big Data Approaches to Address Specific Challenges within the Healthcare Sector

Module 3. Ethics and Regulation in Medical AI  

3.1. Ethical Principles in the Use of AI in Medicine. 

3.1.1. Analysis and Adoption of Ethical Principles in the Development and Use of Medical AI Systems
3.1.2. Integrating Ethical Values into AI-Assisted Decision-Making in Medical Settings
3.1.3. Establishing Ethical Guidelines to Ensure the Responsible Use of Artificial Intelligence in Medicine

3.2. Data Privacy and Consent in Medical Contexts  

3.2.1. Developing Privacy Policies to Protect Sensitive Data in Medical AI Applications
3.2.2. Guarantee of Informed Consent in the Collection and Use of Personal Data in the Medical Field
3.2.3. Implementation of Security Measures to Safeguard Patient Privacy in Medical AI Environments

3.3. Ethics in Research and Development of Medical AI Systems

3.3.1. Ethical Evaluation of Research Protocols in the Development of AI Systems for Health
3.3.2. Ensure Transparency and Ethical Rigor in the Development and Validation of Medical AI Systems
3.3.3. Ethical Considerations in the Publication and Sharing of Medical AI Results

3.4. Social Impact and Accountability in Health AI  

3.4.1. Analysis of the Social Impact of AI on Health Service Delivery
3.4.2. Development of Strategies to Mitigate Risks and Ethical Responsibility in Medical AI Applications
3.4.3. Continuous Social Impact Assessment and Adaptation of AI Systems to Positively Contribute to Public Health

3.5. Sustainable Development of AI in the Health Sector  

3.5.1. Integration of Sustainable Practices in the Development and Maintenance of AI Systems in Health
3.5.2. Environmental and Economic Impact Assessment of AI Technologies in Health
3.5.3. Development of Sustainable Business Models to Ensure Continuity and Improvement of AI Solutions in the Health Sector

3.6. Data Governance and International Regulatory Frameworks in Medical AI  

3.6.1. Development of Governance Frameworks for Ethical and Efficient Data Management in Medical AI Applications
3.6.2. Adaptation to International Regulations to Ensure Ethical and Legal Compliance
3.6.3. Active Participation in International Initiatives to Establish Ethical Standards in the Development of Medical AI Systems

3.7. Economic Aspects of AI in the Health Sector  

3.7.1. Analysis of Economic Implications and Cost-Benefits in the Implementation of AI Systems in Health
3.7.2. Development of Business Models and Financing to Facilitate the Adoption of AI Technologies in the Healthcare Sector
3.7.3. Assessment of Economic Efficiency and Equity in Access to AI-Driven Health Services

3.8. Human-Centered Design of Medical AI Systems.  

3.8.1. Integration of Human-Centered Design Principles to Improve Usability and Acceptance of Medical AI Systems
3.8.2. Participation of Health Professionals and Patients in the Design Process to Ensure the Relevance and Effectiveness of the Solutions
3.8.3. Continuous User Experience Assessment and Feedback to Optimize Interaction with AI Systems in Medical Environments

3.9. Fairness and Transparency in Medical Machine Learning  

3.9.1. Development of Medical Machine Learning Models that Promote Equity and Transparency
3.9.2. Implementation of Practices to Mitigate Biases and Ensure Equity in the Application of AI Algorithms in the Field of Health
3.9.3. Continuous Assessment of Equity and Transparency in the Development and Deployment of Machine Learning Solutions in Medicine

3.10. Safety and Policy in the Implementation of AI in Medicine.

3.10.1. Development Security Policies to Protect Data Integrity and Confidentiality in Medical AI Applications
3.10.2. Implementation of Safety Measures in the Deployment of AI Systems to Prevent Risks and Ensure Patient Safety
3.10.3. Continuous Evaluation of Safety Policies to Adapt to Technological Advances and New Challenges in the Implementation of AI in Medicine

Una titulación universitaria con la que adquirirás conocimientos sin limitaciones geográficas o timing preestablecido. ¡Matricúlate ahora!”