The “Artificial Intelligence and Machine Learning Powered Signal Management Training Course” conference has been added to ResearchAndMarkets.com’s offering.
The quest to find precise evidence to answer complex questions from biomedicine and Healthcare for Drug Safety has brought us to a major crossroads.
With the exponential growth of biomedical knowledge, it is imperative that we teach machines and also learn from them. Data (Information), Connection (Knowledge) and the Tools (Software) designed to deliver these answers is the future of Drug Safety.
This course will prepare you to navigate the multiverse of AI, ML and Deep Learning from a drug safety perspective with an easily understandable and practical approach.
It will provide an excellent opportunity to discuss with an expert in this field the potential use of AI and ML in pharmacovigilance and risk management systems, and will include two demonstrations of AI-powered systems for PV management.
The course will not only provide you with a window but a door to the future landscape of pharmacovigilance and risk management.
Key topics:
- AI, ML and Deep Learning
- Natural Language Processing (NLP): Symbolic, Statistical, Neural
- Named Entity Recognition (NER) and Medical Language Modelling (MLM)
- Data Handling and Model Building
- Learning: Analogical, Inductive, Evolutionary
- Causality Scoring
- Intelligent Reporting
- Contemporaneous explainability
- Regulatory Compliance and Good Machine Learning Practices
- Resources and Tools available
Benefits of Attending
- Understand the technical processes utilised and the impact of those processes on your work as a safety professional and be able to modify those processes for maximum productivity
- Get hands-on experience with an AI-Powered Tool for Signal management
- Learn to create a personalised dashboard for signal assessment and evaluation using the AI and ML capabilities of the Tool
- Recognise and resolve the limitations of the machine and present the best man-machine model for safety assessment
Who Should Attend:
- This course would be of maximum benefit to Safety Professionals who have a basic understanding of Signal Management
- It would be highly advantageous for individuals with multifunctional responsibilities such as project managers, medical directors, medical reviewers and decision-makers involved in the digital transformation of health services
- Appropriate for all levels of Signal Evaluation and Risk Management PV Professionals who want to keep abreast of the AI surrounding them, and its influence and impact on their work
Agenda
Digital Days
- Can machines think?
- Intelligent games
- Geradus to Garmin to Google
Revolution Not Evolution
- Navigating the terminology
- AI and ML powered Signal Management
- NLP: Read and understand
Artificial Intelligence (AI)
- Introduction to AI
- AI models
- AI as a science
Machine Learning (ML)
- Introduction to Machine Learning
- Supervised learning
- Unsupervised learning
- Semi-supervised and reinforced learning
ML Models
- Linear models
- Nonlinear models
- Parametric and nonparametric models
ML Model Building Issues
- Bias and variance
- Under-fitting and over-fitting
- Loss functions
Data Handling
- Data pre-processing
- Descriptive statistics
- Missing data and representing data
Learning
- Analogical learning: Nearest neighbours
- Inductive learning: Decision trees
- Bayesian learning: Naive Bayes
- Evolutionary learning: Genetic algorithms
Deep Learning
- Feedforward neural networks
- Convolutional networks
- Sequential networks
- Generative adversarial networks
In Action
- Image Diagnosis
- Adverse event prediction
- Drug design
Cognitive Systems in Healthcare
- IBM Watson Health
- Merge Cardion, Merge Hemo
- MarketScan
AI, MI and NLP Powered End-to-End Signal Management Tool: PVs by RxLogix
- Demonstration
- Hands on signal automation
AI-Powered Complete Literature Monitoring Platform for Safety Surveillance: Biologist MLM AI by Biologist
- Demonstration
AI-Powered Smart Causality Assistant
- Causality scoring
- Intelligent reporting
Limitations and Discussions
- Customised dictionaries
- Ontologies
- Temporal resolution
- Information heterogenicity
Are We There Yet?
- Contemporaneous explainability – outcomes in AI-powered decision making
- Regulatory compliance ]
Source: https://www.businesswire.com/
