An ecosystem for collaboration hospitals and pharmaceutical companies on AI projects
The core elements we provide:
- Health data analytics (data mining, data aggregation, data modelling, etc.)
- Artificial intelligence (AI) solutions for treatment and care (computer vision, investigation of surgical workflows, knowledge representation, learning from data).
- Annotation/labelling for video (e.g. surgical procedures) and static data (e.g. histopathology, x-rays, etc.), ensuring high quality of annotations and data preparation for machine learning (ML).
- Data anonymization, data reuse, and data security.
- Situation awareness, reasoning and decision support techniques.
- Complex analysis of AI and ML solutions for healthcare, including deep learning algorithms, recommendation systems and end-to-end decision-making (DM) systems.
Our research group proceeds a large amount of multi-modal data from electronic health records, cameras, and medical devices to develop AI-based systems to assist clinicians and staff in their medical routines.
To ensure privacy and security while training ML models, we utilize rigorous data preparation alongside with series of data protection techniques in the federated learning pipeline as data aggregation, perturbation, cleaning.
Early stage clinical trials: Current Stage
Individual Case Safety Reports (ICSR)
- ICSR involves understanding the complete text and entering into the case, the details of the adverse event, drugs, patient data, his/her history and so on
- data sharing
- store user data
- volume growth
- privacy and security of sensitive data
to develop a best-in-class platform that connects clinical and non-clinical data to accelerate drug discovery and treatment advancements for patients
- Customer engagement
- Privacy and Security
- Accuracy andEfficiency
Tools and technologies:
- Distributed ledger
- Federated learning
- Machine learning and AI
186 Startups Using AI in Drug Discovery
- Aggregate and synthesize information
- Understand mechanisms of disease
- Establish biomarkers
- Generate data and models
- Repurpose existing drugs
- Generate novel drug candidates
- Validate and optimize drug candidates
- Design drugs
- Design preclinical experiments
- Run preclinical experiments
- Design clinical trials
- Recruit for clinical trials
- Optimize clinical trials
- Publish data
- Analyze real world evidence
40 Pharma Companies Using AI in Drug Discovery
What we propose
Federated biomedical research and healthcare AI ecosystem
Artificial Intelligence-powered platform for drug discovery and collaboration pharmaceutical companies, hospitals andindividual users Source
Three core components
Orchestrated Customer Engagement
- Our model enables more effective customer engagement
- It can engage different stakeholders include payers, providers and
influencers, which range from national healthcare organizations to
diagnosticians, pathologists and health informatics experts.
- It also includes bodies like clinical commissioning groups, specialized
commissioning and specialist trusts; as well as Cancer Alliances and
primary care hospitals.
Support full-stack privacy and security
- We use federated learning and homomorphic encryption to keep sensitive
- Ecosystem based on a distributed edge computing infrastructure.
- Train an algorithm across multiple decentralized edge devices or servers
holding local data samples, without exchanging their data samples.
- These enable to reduce cloud infrastructure overheads and optimize all
available resources for efficient and economical scaling
Acceleration meaningful healthcare outcomes
- drug-discovery algorithms on each-other’s data
- algorithms might be trained on a pharma company’s data but data
itself wouldn’t be shared with collaborators instead, the models from
each dataset are shared and averaged together in the central model
- model development, training, and evaluation with no direct access to
or labelling of raw data, with communication cost as a limiting factor