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Training AI models to answer ‘what if?’ questions could improve medical treatments

sc604 — Fri, 19 Apr 2024 08:02:54 +0000

Artificial intelligence techniques can be helpful for multiple medical applications, such as radiology or oncology, where the ability to recognise patterns in large volumes of data is vital. For these types of applications, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now, an international team led by researchers from Ludwig-Maximilians-Universität München (LMU) and including researchers from the ̽��ֱ�� of Cambridge, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy.

̽��ֱ��researchers found that causal machine learning (ML) can estimate treatment outcomes – and do so better than the machine learning methods generally used to date. Causal machine learning makes it easier for clinicians to personalise treatment strategies, which individually improves the health of patients.

̽��ֱ��results, reported in the journal Nature Medicine, suggest how causal machine learning could improve the effectiveness and safety of a variety of medical treatments.

Classical machine learning recognises patterns and discovers correlations. However, the principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. When making therapy decisions for a patient, the ‘why’ is vital to achieve the best outcomes.

“Developing machine learning tools to address why and what if questions is empowering for clinicians, because it can strengthen their decision-making processes,” said senior author Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine. “But this sort of machine learning is far more complex than assessing personalised risk.”

For example, when attempting to determine therapy decisions for someone at risk of developing diabetes, classical ML would aim to predict how probable it is for a given patient with a range of risk factors to develop the disease. With causal ML, it would be possible to answer how the risk changes if the patient receives an anti-diabetes drug; that is, gauge the effect of a cause. It would also be possible to estimate whether metformin, the commonly-prescribed medication, would be the best treatment, or whether another treatment plan would be better.

To be able to estimate the effect of a hypothetical treatment, the AI models must learn to answer ‘what if?’ questions. “We give the machine rules for recognising the causal structure and correctly formalising the problem,” said Professor Stefan Feuerriegel from LMU, who led the research. “Then the machine has to learn to recognise the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.”

Even in situations for which reliable treatment standards do not yet exist or where randomised studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and form hypotheses for possible treatment plans, so the researchers hope.

With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, bringing individualised therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.

“ ̽��ֱ��software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. “Rather, complex modelling of the respective problem is required, involving close collaboration between AI experts and doctors.”

In other fields, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice,” he said. ̽��ֱ��paper describes the direction in which things could move over the coming years.”

“I have worked in this area for almost 10 years, working relentlessly in our lab with generations of students to crack this problem,” said van der Schaar, who is affiliated with the Departments of Applied Mathematics and Theoretical Physics, Engineering and Medicine. “It’s an extremely challenging area of machine learning, and seeing it come closer to clinical use, where it will empower clinicians and patients alike, is very satisfying.”

Van der Schaar is continuing to work closely with clinicians to validate these tools in diverse clinical settings, including transplantation, cancer and cardiovascular disease.

Reference:
Stefan Feuerriegel et al. ‘Causal machine learning for predicting treatments.’ Nature Medicine (2024). DOI: 10.1038/s41591-024-02902-1

Adapted from an LMU media release.

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

Yuichiro Chino via Getty Images

Computer-generated image of human brain

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Cambridge and LMU announce plans for strategic partnership

sjr81 — Tue, 29 May 2018 12:21:47 +0000

̽��ֱ�� ̽��ֱ�� of Cambridge and the Ludwig-Maximilians-Universität München (LMU) put pen to paper on a memorandum of understanding that will see the two institutions forge ever-closer links in education and research across a broad range of disciplines in the Sciences, Humanities and Medicine.

Senior leaders from Cambridge and LMU – which boast nearly 150 Nobel Laureates between them – came together over two days in Cambridge for meetings led by both the President of LMU, Professor Bernd Huber, and Cambridge Vice-Chancellor Professor Stephen Toope.

At the conclusion of the visit, officials from Cambridge and LMU signed the memorandum of understanding, which indicates the desire to develop a joint programme of strategic importance to both institutions. A full programme will be formulated by the end of the year, with a formal launch expected to take place in early 2019.

It is intended that the partnership will include joint research activities, the exchange of academic staff, postdoctoral and PhD candidates, as well as masters and undergraduate students, joint teaching initiatives, and training for the next generation of scholars. ̽��ֱ��partnership will be cross-disciplinary, covering broad areas in the Humanities and Cultural Studies, Law, Economics and Social Sciences, Natural Sciences, as well as Medicine, and will develop over the course of an initial five-year funding period.

Professor Chris Young, Head Elect of the School of Arts and Humanities, and Cambridge’s academic lead for the strategic partnership, said: “ ̽��ֱ��LMU is Germany's leading university in Germany's leading city.

“Its outstanding scholarship and rich network of associated institutes and industrial partnerships make it the perfect bridge to Bavaria, Germany and Europe. There are already myriad collaborations between colleagues at both universities, and this exciting new partnership will intensify and augment these for years to come.”

Professor Thomas Ackermann, Dean of the Faculty of Law and LMU’s Director for the strategic partnership, said: “ ̽��ֱ�� ̽��ֱ�� of Cambridge is one of the world’s leading institutions in education, learning, and research. ̽��ֱ��strategic partnership between our universities will pave the way towards a new level of cooperation. Together with my colleague, Chris Young, we will explore an interesting array of activities to ensure the program will be a great success for both universities.”

Cambridge Vice-Chancellor, Professor Stephen Toope, said: “No single institution can provide, on its own, the answers to the great challenges of these turbulent times. Collaboration and openness to the world are essential to achieving our academic and civic missions. Our partnership with LMU, one of Europe’s finest universities, creates exciting opportunities to work together to address tough issues and provide our students with a richer education.”

“ ̽��ֱ��strategic partnership with the ̽��ֱ�� of Cambridge, one of the leading universities in Europe and the world, will bring an exciting stimulus to research and learning at LMU,” said LMU President Professor Bernd Huber. “Our new partnership ensures that collaboration and exchange which are vital for academic innovation can continue to be pursued regardless of Brexit.”

Two of Europe’s leading research universities have announced the first step towards plans for a unique ‘strategic partnership’ – underlining the vital and ongoing relationship between British universities and their peer institutions across the EU in a post-Brexit landscape.

Collaboration and openness to the world are essential to achieving our academic and civic missions.

Stephen Toope

Nick Saffell/ ̽��ֱ�� of Cambridge

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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