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AI in Diagnostics
Abstract
In diagnostics we want to find cause-effect relationships. One common way to find such causal interactions is through experimental design. During experiments we can observe whether a controlled change in one factor leads to a change in a different one. Alternatively, if we have some level of understanding of the underlying mechanisms, we can use reasoning based on this knowledge to determine or at least predict possible cause and effect.
Experimentation can increase our level of understanding of underlying mechanisms, and as a result lead to more accurate diagnoses. However, such experiments can be too expensive, too impractical, or even impossible to perform. Besides, they rely on hypotheses that tend to be the result of the current mechanism-based knowledge.
Instead, it is possible to use state-of-the art machine learning methods to let the data do the talking. When we have access to sufficient amounts of data, we can learn cause-effect relationships that are difficult to predict solely using mechanism-based reasoning. Using statistical models based on directed graphs we can indeed extract causal relationships from purely observational data based on measures for independence between variables [1-3]. In addition, we can use unsupervised machine learning methods such as clustering and latent variable modelling (e.g [4]) to discover subtypes within the population or discover biomarkers even without any domain or mechanism-based knowledge or even detect underlying causes that cannot be observed directly.
In the context of the Repo4EU project, Machine2Learn will use the above techniques for use in AI-driven patient recruitment that could lead to the development of precision medicine [5] down the line.
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References
Spirtes Peter, Glymour Clark, Scheines Richard. Causation, Prediction, and Search. 2001. The MIT Press. [Cross Ref]
Cui Ruifei, Bucur Ioan Gabriel, Groot Perry, Heskes Tom. A novel Bayesian approach for latent variable modeling from mixed data with missing values. Statistics and Computing. Vol. 29(5):977–993. 2019. Springer Science and Business Media LLC. [Cross Ref]
Zhang Kun, Peters Jonas, Janzing Dominik, Schoelkopf Bernhard. Kernel-based Conditional Independence Test and Application in Causal Discovery. 2012. arXiv. [Cross Ref]
Peterson Thomas A., Doughty Emily, Kann Maricel G.. Towards Precision Medicine: Advances in Computational Approaches for the Analysis of Human Variants. Journal of Molecular Biology. Vol. 425(21):4047–4063. 2013. Elsevier BV. [Cross Ref]