Healthcare Ml
4 long-form posts on Healthcare Ml: machine-learning research by Taha Bouhsine, each built around live, in-browser interactive visualizations.
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Running the Survival Trial, in JAX/Flax NNX
A runnable companion to the survival-model trial: the Yat DeepSurv trunk in Flax NNX, the Cox partial-likelihood loss, an LR-fair per-model training loop with best-epoch selection, the concordance / integrated-Brier / time-dependent-AUC evaluation, and the classical baselines wired through sksurv and lifelines. Every figure is a real number from a real run across five datasets, with the prototypes and the risk stratification captured as they form over training.
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The White-Box Survival Model on Trial
A classical kernel machine is beautiful and needs a solve that will not minibatch or compose. A deep net trains on anything and its risk score is a fog. What if one thing had the training of a net and the theory of a kernel? We put a deep Yat-kernel survival model on trial across five real datasets against Cox, penalized Cox, and Random Survival Forest: it trains with plain gradient descent, lands in the pack on the concordance index, and, because its units are genuine Mercer kernels, inherits exact attribution, calibration, editing, and bounded out-of-distribution response the others cannot give. Where it costs a point or two, we show exactly where.
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A White-Box DeepSurv, in JAX/Flax NNX
A runnable companion: the Cox partial-likelihood loss, a standard DeepSurv, and a Yat-kernel DeepSurv whose log-risk decomposes into prototype patients, all in Flax NNX. Concordance evaluation, exact convex attribution, cohort deletion, and OOD abstention as short array operations. Every number is from a real run on METABRIC breast-cancer survival.
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A Risk Model That Names Its Reasons
A survival model tells an oncologist a patient is high-risk, and she has to act on the number without being able to ask why. What would it take for the risk score to name its reasons? We build a Yat-kernel DeepSurv on breast-cancer survival, match a standard DeepSurv on concordance, and get a risk score that decomposes exactly into the prototype patients this one resembles, a model you can read, audit, and edit.