Pharmatics

Pharmatics uses cutting-edge predictive and generative AI, including large language models, to accelerate predictions and interventions by leveraging preclinical, clinical, and epidemiological evidence from worldwide publications and converting it into data- and evidence-based predictions and interventions.

Role in the project

We play an important role in the work on predictive modelling (WP6), by integrating the latest evidence into machine learning workflows for risk stratification and primary, secondary, and tertiary prevention of HCM and its complications. Additionally, we contribute to the development of the Application Programming Interface and the backend in the work on software development (WP7), as well as the integration of data with knowledge curation (WP2). Our involvement is crucial for ensuring that the machine learning models and software developed in the project are based on both data and evidence, facilitating safe and effective interventions.

Colleagues working on the project

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Felix Agakov

PhD in machine learning

Dr Agakov, an entrepreneur and AI/ML expert, brings 20 years of experience in AI/ML applications, including 15 years in digital and precision medicine. He excels in integrating data- and evidence-based approaches for managing long-term conditions and received “best paper” and “10-year-test-of-time” awards in applied machine learning. Dr Agakov led numerous UK-wide, Pan-European, and international R&D projects focusing on biomarker discovery, patient stratification, preventive care, and precision nutrition and collaborated with major biotechnology and digital health companies across the UK, EU, and North America.

pharmatics peter orchard web

Peter Orchard

PhD in machine learning

Dr Orchard is an AI/ML expert, focusing on deep multi-task learning and information extraction for intelligent digital health. With a PhD in machine learning and over 16 years of experience in software and medical applications, he led the development of information extraction algorithms and automated code generation in the MedAI platform. Dr Orchard made significant contributions to creating MedAI instances for diabetes and cardiovascular conditions and was involved in projects on risk stratification and biomarker discovery. His expertise also includes API/SaaS development and integrating diverse machine learning models into secure backend environments.

pharmatics panagiotis zacharis web

Panagiotis Zacharis

PhD in Electronic and Electrical Engineering

Dr Zacharis is an AI engineer. With over 10 years in scientific computing and medical applications, he contributed to various areas, including prediction of treatment response using clinical and biomarker measurements, data augmentation employing contrastively trained deep learning architectures, and drug discovery using iPSC assays, as well as predictions from medical images and videos using data-efficient neural network architectures. Dr Zacharis also worked on enhancing information extraction methods for tabular data in the MedAI pipeline, and on using language models for scientific discovery from biomedical literature.

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Anna Agakova

MSc in signal processing, MSc in software engineering

Mrs Agakova holds multiple post-graduate degrees in signal processing and software engineering, bringing 17 years of experience in scientific computing. She worked extensively on digital signal processing and served as the lead developer of a state-of-the-art tool for automatic signal integration from biomedical time series, currently powering novel biomarker acquisition platforms across Europe. Additionally, she was involved in developing numerous applications of AI and machine learning, contributing to biomarker discovery, patient stratification, patient monitoring, precision nutrition, and AI-powered self-management of long-term conditions.