Implement neural networks in the best digital university in the world according to Forbes"

Machine Learning in Biomedical Research is extremely beneficial for clinicians to improve the accuracy of their diagnoses. By analyzing considerable volumes of biomedical data, this system detects individual patterns in users' health. In this way, experts develop fully personalized treatment plans to suit the specific needs of patients. In addition, this technology is able to locate complex relationships between genes, proteins and diseases, facilitating the discovery of biomarkers.  This accelerates the research of both new therapies and drugs, which imply an improvement in the well-being of citizens.  

In this context, TECH implements a pioneering study that will equip specialists with a comprehensive understanding and the practical skills to become experts in Advanced Methodologies in Biomedical Research with AI. To enable this, the curriculum will delve into the simulation of biological processes and conditions. In this line, the syllabus will delve into the generation of synthetic datasets, as well as the scientific and clinical validation of the resulting models. It should be noted that the academic materials will highlight the importance of ethics and regulations associated with the use of synthetic data.  

The syllabus is made more dynamic by multimedia pills and a wide variety of didactic resources such as specialized readings and case studies.  In addition, the Relearningmethodology, used by this academic institution, will help professionals to achieve a much more effective update in less time. A unique opportunity to keep up to date through a flexible online pedagogical option, which favors the compatibility of the most demanding daily responsibilities with a university proposal that is at the forefront.  The only requirement for doctors is that they have a device with Internet access to access the Virtual Campus and expand their knowledge through the most innovative didactic content. 

As a specialist, you will be able to use Artificial Intelligence to collect data from medical devices and find more complex conditions”

The Postgraduate certificate in Advanced Methodologies in Biomedical Research with Artificial Intelligence 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 in Advanced Methodologies in Biomedical Research with AI
• The graphic, schematic, and practical contents with which they are created, 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

You will overcome contemporary challenges in Biomedical Research, from analyzing large datasets, to predicting clinical outcomes" 

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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 academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.   

You will delve into the use of medical imaging and genomic data, under a holistic approach to understand the complexity of diseases"

You will reinforce your key knowledge through the innovative Relearning methodology for an effective assimilation of the subject"

Through an eminently practical approach, this Postgraduate certificate will provide physicians with advanced AI techniques to apply in Biomedical Research. The curriculum will delve into essential aspects such as the execution of observational studies with Machine Learning, heterogeneous data integration methods, algorithms in biomedical data analysis and the use of Virtual Reality in clinical studies. On the other hand, the agenda will focus on applied Data Mining tools, in order for experts to overcome the challenges inherent to this resource. 

This program gives you the opportunity to update your knowledge in a real scenario, with the maximum scientific rigor of an institution at the forefront of technology" 

Module 1. Biomedical Research with AI

1.1. Design and Execution of Observational Studies with AI

1.1.1. Implementation of AI for the Selection and Segmentation of Populations in Studies
1.1.2. Use of Algorithms for Real-Time Monitoring of Observational Study Data
1.1.3. AI Tools for the Identification of Patterns and Correlations in Observational Studies
1.1.4. Automation of the Data Collection and Analysis Process in Observational Studies

1.2. Validation and Calibration of Models in Clinical Research

1.2.1. AI Techniques for Ensuring the Accuracy and Reliability of Clinical Models
1.2.2. Use of AI in the Calibration of Predictive Models in Clinical Research
1.2.3. Cross-validation Methods Applied to Clinical Models using AI
1.2.4. AI Tools for the Evaluation of the Generalization of Clinical Models

1.3. Methods for Integrating Heterogeneous Data in Clinical Research

1.3.1. AI Techniques for Combining Clinical, Genomic, and Environmental Data
1.3.2. Use of Algorithms for Handling and Analyzing Unstructured Clinical Data
1.3.3. AI Tools for Normalization and Standardization of Clinical Data
1.3.4. AI Systems for Correlating Different Types of Research Data

1.4. Integration of Multidisciplinary Biomedical Data

1.4.1. AI Systems to Combine Data from Different Biomedical Disciplines
1.4.2. Algorithms for the Integrated Analysis of Clinical and of Laboratory Data
1.4.3. AI Tools for the Visualization of Complex Biomedical Data
1.4.4. Use of AI in the Creation of Holistic Health Models from Multidisciplinary Data

1.5. Deep Learning Algorithms in Biomedical Data Analysis

1.5.1. Implementation of Neural Networks in Analysis of Genetic and Proteomic  Data
1.5.2. Using Deep Learning to Identify Patterns in Biomedical Data
1.5.3. Development of Predictive Models in Precision Medicine with Deep Learning
1.5.4. Application of AI in Advanced Biomedical Image Analysis

1.6. Optimization of Research Processes with Automation

1.6.1. Automation of Laboratory Routines with AI Systems
1.6.2. Use of AI for Efficient Management of Resources and Time in Research
1.6.3. AI Tools for Workflow Optimization in Clinical Research
1.6.4. Automated Systems for Tracking and Reporting of Research Progress

1.7. Simulation and Computational Modeling in Medicine with AI

1.7.1. Development of Computational Models to Simulate Clinical Scenarios
1.7.2. Use of AI for Simulation of Molecular and Cellular Interactions
1.7.3. AI Tools in the Creation of Predictive Disease Models
1.7.4. Application of AI in the Simulation of Drug and Treatment Effects

1.8. Use of Virtual and Augmented Reality in Clinical Trials

1.8.1. Implementation of Virtual Reality for Training and Simulation in Medicine
1.8.2. Use of Augmented Reality in Surgical and Diagnostic Procedures
1.8.3. Virtual Reality Tools for Behavioral and Psychological Studies
1.8.4. Application of Immersive Technologies in Rehabilitation and Therapy

1.9. Data Mining Tools Applied to Biomedical Research

1.9.1. Use of Data Mining Techniques to Extract Knowledge from Biomedical Databases
1.9.2. Implementation of AI Algorithms to Discover Patterns in Clinical Data
1.9.3. AI Tools for Trend Identification in Large Datasets
1.9.4. Application of Data Mining in the Generation of Research Hypotheses

1.10. Development and Validation of Biomarkers with Artificial Intelligence

1.10.1. Use of AI for the Identification and Characterization of New Biomarkers
1.10.2. Implementation of AI Models for the Validation of Biomarkers in Clinical Studies
1.10.3. AI Tools in Correlating Biomarkers with Clinical Results
1.10.4. AI Applications in the analysis of Biomarkers for Personalized Medicine

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences”