Bayesian networks in healthcare

A paper published today in the Journal Artificial Intelligence in Medicine  provides a comprehensive classification of the health conditions for which Bayesian networks have been used (the paper is part of a much bigger scoping review).

Four conditions - cardiac, cancer, psychological and lung disorders - make up over two-thirds of the research. The paper identifies differences in the approaches used by authors between each of these four primary health conditions and contributes to our understanding of how and what Bayesian networks are being considered for in healthcare.

Full details and download:

McLachlan, S., Dube, K., Hitman, G. A., Fenton, N. E., & Kyrimi, E. (2020). "Bayesian networks in healthcare: Distribution by medical condition". Artificial Intelligence in Medicine, 107, 101912. https://doi.org/10.1016/J.ARTMED.2020.101912

Full details and download of the full scoping review preprint:

Kyrimi, E, McLachlan, S, Dube, K, Neves M R, Fahmi,A, Fenton, N E, (2020) "A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future", arXiv:2002.08627

Book Review: David Spiegelhalter’s “The Art of Statistics: How to Learn from Data”

A superb, timely overview of the benefits and limitations of statistics in the era of big data and machine learning

David Spiegelhalter has gained a deserved reputation as a masterful communicator of statistics and risk through his media work and writings. I believe this timely book is the best introduction to the benefits and limitations of statistics that I have seen and is David’s most important work yet in public communication. Any of the minor concerns explained in the review that I have about the book (including the understated role of causal models and the role of the likelihood ratio in courts) are the inevitable result of having to be selective about which more detailed material has to be left out to satisfy both the page and audience constraints.

In summary, this book is a must have for a) anybody who wants to better understand statistics and risk; b) anybody involved in the communication of statistics and risk; and c) anybody undertaking a course in data science and machine learning.

Here is the link to the full review:

Book Review: David Spiegelhalter’s “The Art of Statistics: How to Learn from Data” Pelican Books, 2019

See also:

• Book Review: "The Book of Why: the new science of cause and effect" by Pearl and Mackenzie
• Hannah Fry’s “Hello World” and the Example of Algorithm Bias
• Book Review: "Doctoring Data" by Malcolm Kendrick

Postscript: One of the slight concerns I discuss in the review related to the example of the use of statistics in identifying unusually poor hospital treatment outcomes. This is an example that I thought was crying out for a causal model along the lines of this: