Bayesian basics in Le Monde

learning and inference for medical discovery in Oxford [postdoc]

[Here is a call for a two-year postdoc in Oxford sent to me by Arnaud Doucet. For those worried about moving to Britain, I think that, given the current pace—or lack thereof—of the negotiations with the EU, it is very likely that Britain will not have Brexited two years from now.]

Numerous medical problems ranging from screening to diagnosis to treatment of chronic diseases to  management of care in hospitals requires the development of novel statistical models and methods. These models and methods need to address the unique characteristics of medical data such as sampling bias, heterogeneity, non-stationarity, informative censoring etc. Existing state-of-the-art machine learning and statistics techniques often fail to exploit those characteristics. Additionally, the focus needs to be on probabilistic models which are
interpretable by the clinicians so that the inference results can be integrated within the medical-decision making.

We have access to unique datasets for clinical deterioration of patients in the hospital, for cancer screening, and for treatment of chronic diseases. Preliminary work has been tested and implemented at UCLA Medical Center, resulting in significantly management care in this hospital.

The successful applicant will be expected to develop new probabilistic models and learning methods inspired by these applications. The focus will be primarily on methodological and theoretical developments, and involve collaborating with Oxford researchers in machine learning, computational statistics and medicine to bring these developments to practice.

The post-doctoral researcher will be jointly supervised by Prof. Mihaela van der Schaar and Prof. Arnaud Doucet. Both of them have a strong track-record in advising PhD students and post-doctoral researchers who subsequently became successful academics in statistics, engineering sciences, computer science and economics. The position is for 2 years.

Horizon Maths 2015: Santé & Données

Medical illuminations [book review]

Howard Wainer wrote another book, about to be published by Oxford University Press, called Medical Illuminations. (The book is announced for January 2 on amazon. A great New Year gift to be sure!) While I attended WSC 2013 in Hong Kong and then again at the RSS Annual Conference in Newcastle, I saw a preliminary copy of the book and asked the representative of OUP if I could get a copy for CHANCE (by any chance?!)… And they kindly sent me a copy the next day!

 “This is an odd book (…) gallop[ing] off in all directions at once.” (p.152)

As can be seen from the cover, which reproduces the great da Vinci’s notebook page above (and seen also from the title where illuminations flirts with illuminated [manuscript]), the book focus on visualisation of medical data to “improve healthcare”. Its other themes are using evidence and statistical thinking towards the same goal. Since I was most impressed by the graphical part, I first thought of entitling the post as “Howard does his Tufte“ (before wondering at the appropriateness of such a title)!

“As hard as this may be to believe, this display is not notably worse than many of the others containd in this remarkable volume.” (p.78)

In fact, this first section is very much related with CHANCE in that a large sequence of graphics were submitted by CHANCE readers when Howard Wainer launched a competition in the magazine for improving upon a Nightingale-like representation by Burtin of antibiotics efficiency. It starts from a administrative ruling that the New York State Health Department had to publish cancer maps overlayed with potentially hazardous sites without any (interpretation) buffer. From there, Wainer shows how the best as well as the worst can be made of graphical representations of statistical data. It reproduces (with due mention) Tufte‘s selection of Minard‘s rendering of the Napoleonic Russian campaign as the best graph ever… The corresponding chapters of the book keep their focus on medical data, with some commentaries on the graphical quality of the 2008 National Healthcare Quality Report (ans.: could do better!). While this is well-done and with a significant message, I would still favour Tufte for teaching data users to present their findings in the most effective way. An interesting final chapter for the section is about “controlling creativity” where Howard Wainer follows in the steps of John Tukey about the Atlas of United States Mortality, And then shows a perfectly incomprehensible chart taken from Understanding USA, a not very premonitory title… (Besides Howard’s conclusion quoted above, you should also read the one-star comments on amazon!)

“Of course, it is impossible to underestimate the graphical skills of the mass media.” (p.164)

Section II is about a better use of statistics and of communicating those statistics towards improving healthcare, from fighting diabetes, to picking the right treatment for hip fractures (from an X-ray),  to re-evaluate detection tests (for breast and prostate cancers) as possibly very inefficient, and to briefly wonder about accelerated testing. And Section III tries to explain why progress (by applying the previous recommendations) has not been more steady. It starts with a story about the use of check-lists in intensive care and the dramatic impact on their effectiveness against infections. (The story hit home as I lost my thumb due to an infection while in intensive care! Maybe a check-list would have helped. Maybe.)  The next chapter contrasts the lack of progress in using check-lists with the adoption of the Korean alphabet in Korea, a wee unrelated example given the focus of the book. (Overall, I find most of the final chapters on the weak side of the book.)

This is indeed an odd book, with a lot of clever remarks and useful insights, but not so much with a driving line that would have made Wainer’s Medical Illuminations more than the sum of its components. Each section and most chapters (!) contain sensible recommendations for improving the presentation and exploitation of medical data towards practitioners and patients. I however wonder how much the book can impact the current state of affairs, like producing better tools for monitoring one’s own diabetes. So, in the end, I recommend the reading of Medical Illuminations as a very pleasant moment, from which examples and anecdotes can be borrowed for courses and friendly discussions. For non-statisticians, it is certainly a worthy entry on the relevance of statistical processing of (raw) data.