Past events

Nima Hejazi, PhD, is an Assistant Professor of Biostatistics at the T.H. Chan School of Public Health of Harvard University

The widespread availability of high-dimensional data has catalyzed the process of biological pattern discovery. Today, the simultaneous screening of anywhere from thousands to millions of biological characteristics (in, e.g., genomics, metabolomics, proteomics, etc.) is commonplace in many experimental settings, making the simultaneous screening of such a large number of characteristics a central problem in computational biology and allied sciences. The information gleaned from such studies promises substantial progress, not only for the basic sciences but also for medicine and public health.

While the tools of modern chemical biology and biophysics allow for great precision in probing biological systems at the molecular-cellular level, population biomedical and public health sciences must operate without access to such a fine level of control. Instead, statistical innovations bridge the gap -- being used to dissect mechanistic processes and to mitigate the inferential obstacles imposed by the confounding of key relationships in observational (non-randomized) studies. Unfortunately, most off-the-shelf statistical techniques rely on restrictive assumptions - born of mathematical convenience - that invite opportunities for bias due to model misspecification (when the biological process under study fails to obey the assumed mathematical conveniences). Fortunately, model-agnostic inference, which draws on causal inference and semiparametric efficiency theory, provides an avenue for avoiding restrictive modeling assumptions while obtaining robust statistical inference about scientifically relevant parameters. 

We outline this this framework briefly and introduce a model-agnostic technique for biomarker discovery that incorporates causal inference-based parameters, that leverages state-of-the-art machine learning for flexible estimation (mitigating potential for model misspecification), and that incorporates variance moderation (curbing Type-I error) to deliver stable inference in high-dimensional settings, even when sample sizes are limited. When paired with domain expertise, this approach reliably identifies biomarkers linked to disease or exposure patterns, yielding insights for future investigations into therapeutics and policy interventions.

The approach is implemented in the open-source biotmle R/Bioconductor package (https://bioconductor.org/biotmle). This talk is based on joint work with Alan Hubbard, Mark van der Laan, and Philippe Boileau, described in the pre-print:

https://arxiv.org/abs/1710.05451

About the speaker:  Nima Hejazi, PhD, is an Assistant Professor of Biostatistics at the T.H. Chan School of Public Health of Harvard University. He completed an NSF Mathematical Sciences Postdoctoral Research Fellowship in 2022, and, prior to that, obtained his PhD in biostatistics from UC Berkeley. He was part of the founding core development team of the tlverse project (https://github.com/tlverse), an open source software ecosystem for targeted learning in the R programming language, and, since 2020, has collaborated closely with the Vaccine and Infectious Disease Division of the Fred Hutchinson Cancer Center, as a core member of the US Government Biostatistics Response Team and the COVID-19 Prevention Network as well as, more broadly, in studies of vaccine safety and efficacy of infectious diseases including HIV-1, malaria, and COVID-19.

Nima's research interests combine causal inference and machine learning, aiming to develop robust, efficient, and assumption-lean statistical procedures in a problem-first approach, tailored to problems arising in scientific collaborations. He is motivated by methodological topics related to distribution-free (nonparametric) inference, semiparametric-efficient inference, high-dimensional inference, targeted minimum loss estimation, and modern issues in the design of experiments (outcome-dependent sampling, sequentially adaptive treatments). His recent statistical research has been strongly informed by collaborative science in clinical trials and computational biology, especially as related to treatment and preventive vaccine efficacy trials and infectious disease epidemiology. Nima is also deeply interested in high-performance statistical computing and the development and design of open source software for reproducible applied statistical data science.

There is no video for this seminar.

Pekka Marttinen is an associate professor in machine learning in the department of computer science at Aalto university, Finland

Accurately predicting the need for healthcare services is important, to allocate the limited resources fairly and efficiently. In this presentation I will introduce the problem of predicting future diagnoses and hospital visits using individual level trajectories of diagnoses and medical procedure codes available in electronic medical records. I will then present our recent work on developing neural networks for nationwide healthcare registers for this problem, to predict the usage of healthcare services by the elderly population in Finland. We show that by leveraging individual patient trajectories and modern neural network architectures, the prediction accuracy can be significantly improved compared to multiple strong baselines. The presentation is based on the following articles:

https://proceedings.mlr.press/v116/kumar20a.html

https://arxiv.org/abs/2108.13672

About the speaker:   Pekka Marttinen is an associate professor in machine learning in the department of computer science at Aalto university, Finland. He received his PhD in Statistics at the University of Helsinki in 2008, and has been employed at Aalto since 2009, interleaved by periods as a visiting researcher at the Center for Communicable Disease Dynamics at Harvard and at the Sanger institute in Cambridge. He has received a Research Fellowship from the Academy of Finland. His research focuses on method development and modeling in machine learning, with emphasis on biomedical applications, and he has published 65 articles on these topics. He is best known for his work on scalable computational methods for detecting structure in massive genomic data sets and as well as for the development of Bayesian methodology for efficient and flexible modeling of complex and noisy data, including techniques such as likelihood-free inference and Bayesian neural networks.

Brian Arnold, Senior Data Scientist, Princeton University.

Carlo Tacchetti, Vita-Salute San Raffaele University

Eleonora Harwich, Head of Collaborations, NHS AI Lab| NHSX

In this session, the speaker will explore the progress made in the UK in terms of the adoption of artificial intelligence in health and care. The NHS AI Lab has been set up to support the development and deployment of safe, ethical and effective AI. This session will explore the progress it has made to date in terms supporting innovation, increasing adoption, clarifying regulation and routes to market as well as increasing the workforce and public's confidence in the technology.

Alexandra Boussommier-Calleja, CEO & co-founder of ImVitro

This talk will first describe the path of Dr Alexandra Boussommier from being a researcher to an entrepreneur. We will then discuss ImVitro, the company she founded in 2019 that aims at using AI to tackle infertility. The overall research conducted by her team will be described along with the application of AI in embryology, the clinical needs it is addressing and the challenges faced in bringing AI into clinics.

ImVitro is an early stage Deep Tech start-up based in Paris, with pre-seed investment from Entrepreneur First. Its aim is to facilitate in vitro fertilization (IVF) using artificial intelligence to help the increasing number of people with fertility issues.

https://im-vitro.com/

Kathy Harrison, DataLoch programme manager, Usher Institute.

Atul Anand, DataLoch clinical lead, Centre for Cardiovascular Science.

The NHS in Scotland is facing the prospect of an aging population with more people with long-term conditions, with reducing resources and value for money challenges such as increasing costs of medicine and delayed discharge. DataLoch’s ambition is to create a health and social care data resource that enables a data driven approach to this challenge, supporting service managers, innovators and researchers to understand and deliver the change that’s needed. Routine data, processed to create a single longitudinal data set, will present a complete story of a patient’s health, diagnosis, treatments, medical procedures and outcomes. In this way, DataLoch will enable high quality research through slick and robust data access. The DataLoch service has been tested with an initial COVID-19 dataset and is now moving to a condition agnostic database structure to support wider research interests.

https://www.ed.ac.uk/usher/dataloch

Giovanni Stracquadanio, Senior Lecturer in Synthetic Biology, School of Biological Sciences

The availability of high-throughput technologies enables the characterization of cells with unprecedented resolution, ranging from the identification of single nucleotide mutations to the quantification of protein abundance. The availability of high-resolution molecular data has been particularly revolutionizing for cancer biology and has shed light on the underpinning mechanisms controlling tumour formation and response to treatment. These findings have been possible thanks to the development of analytical methods that can generate testable hypotheses from cancer omic data. Here I will present how we use Bayesian learning and deep learning to take advantage of population scale biobanks to identify the molecular mechanisms responsible for cancer heritability and tumorigenesis.

There is no video for this seminar.

Chris Williams, School of Informatics, University of Edinburgh

The practical work of deploying a machine learning system is dominated by issues outside of training a model: data preparation, data cleaning, understanding the data set, debugging models, and so on. The goal of the Artificial Intelligence for Data Analytics project at the Alan Turing Institute is to help to automate the whole data analytics process by drawing on advances in AI and machine learning. We will describe tools to address such tasks, including identifying syntactic and semantic data types, data integration, and identifying and repairing missing and anomalous data. Joint work with the AIDA team: Taha Ceritli, James Geddes, Ernesto Jimenez-Ruiz, Ian Horrocks, Alfredo Nazabal, Tomas Petricek, Charles Sutton, Gerrit Van Den Burg.

Jennifer Quint, National Heart and Lung Institute, Imperial College London & BREATHE Hub.

This talk will consider what clinicians think about data and what they should think about data. I’ll talk about how and where data are collected, used and how we can make it even better. Whilst clinicians use and report data in day-to-day clinical practice, entry tools and reporting are often inefficient, and whilst clinicians are skilled at observation and medical decision making, they often struggle with accurately recording and measuring activities. This in turn leads to data variation and issues in data quality.

The CDT held a virtual Industry Event on 19 March 2021. The aim of this event was to bring together the industry partners, students and academics to discuss interests, challenges and research developments in Biomedical AI. The event featured talks from the industry partners and CDT students:

Industrial Partner Talks

• Dr Kenj Takeda (Academic Health and AI Partnerships Director); “Microsoft Research AI in Healthcare”
• Dr Flaviu Cipcigan, (Research Scientist in Impact Science): “IBM’s Science & Technology Outlook”
• Dr Nichola Richmond (Director AI/ML and Machine Learning at GlaxoSmithKline); “AI@GSK & the AI Fellowship”

Student Talks

• Michael Stam - "Machine Learning for Reliable Protein Design"
• Rayna Andreeva - "Topological Data Analysis for Biomedical Images and 3D Surfaces"
• Matús Falis – ““Addressing Concept Sparsity in Medical Text with Medical Ontologies”

Speaker: Andy Smout, Vice President, Research at Canon Medical Research Europe

Abstract:

Canon Medical is a global supplier of radiology equipment and IT systems. Our Centre of Excellence for medical AI is based in Edinburgh, where we develop cutting-edge machine learning algorithms for detecting and characterising disease. Increasingly we find that access to data is the rate limiting step in machine learning – this is driving a change in how we do research, and has given birth to centres such as iCAIRD where Canon and other technology companies are able to work with clinical data in situ. We expect these centres to lower the barriers to entry for new medical AI companies, which in turn will create a diverse and potentially confusing marketplace with many overlapping and competing solutions. To make sense of this we see the need for an overarching infrastructure that brings AI into the clinical workflow, breaking down information silos and to create an integrated diagnostic solution focussed around the clinical pathway.