Tag

#healthcare

May 19, 20265 min read

Fail closed: designing an AI for people who can't afford a bad answer

In short: when the user can't afford a bad answer, every part of the system that can fail has to fail towards the safe answer, not towards the model. In a supervised care product I'm building, a voice…

February 24, 20264 min read

From medical records to legal documents: what changed

In short: the engineering barely changed. What changed is where the truth lives. In medicine it lives in a private record you have to protect. In law it lives in public text that keeps changing, and…

August 12, 20255 min read

Leakage: the bug that makes every metric lie

In short: leakage is when information from the test set, or from the answer itself, sneaks into training. The three kinds I check for every time are the same person on both sides of the split,…

July 29, 20255 min read

Classical ML or an LLM? The decision before touching code

In short: if the input is numbers or a signal and you have labelled examples, I start with a classical model. If the input is language and the job is to read or write it, I start with an LLM. Most of…

June 17, 20255 min read

Why I move between fields on purpose

In short: each new field breaks a different assumption I didn't know I was making, and that's the fastest way I've found to get better at the job. The tools carry over. The instincts don't, and…

June 3, 20255 min read

Class imbalance in clinical data: what actually worked

In short: oversampling and class weights mostly move you along the same precision-recall curve. What actually worked was picking the decision threshold on purpose, on a validation set, against a…

May 6, 20255 min read

Data that doesn't match across sites

In short: when several sites collect "the same" data, the columns share names and almost nothing else. Before any model, write one data dictionary, map every site to it in code, and check the result…

April 22, 20254 min read

Explaining a model to a doctor

In short: doctors don't want to see how the model works. They want to know why it said this about this patient, in units they already use, and whether they should trust it. A simple bar chart with…

April 8, 20254 min read

Monitoring a model that listens to patients

In short: for a voice model in healthcare, the true labels arrive late or never, so you can't watch accuracy. You watch the recordings coming in, the scores going out, and the plumbing in between, and…

February 25, 20255 min read

The clinician said no, and was right

In short: a clinical model can score beautifully because it has learned something the clinic already decided. The fastest way to catch that is not a better metric. It is ten minutes with the person…

February 11, 20254 min read

What "in production" means in a hospital

In short: in a hospital, a model is in production when a clinician can see its output during real care, a named group has approved that, a named person owns it when it breaks, and the ward runs fine…

December 17, 20244 min read

Respiratory sound classification, end to end

In short: I took a public set of lung sound recordings all the way from raw audio to a web demo anyone can upload a file to. The model was the smallest part. Most of the work was deciding what to…

December 3, 20244 min read

Running AI on your own machines

In short: when data is not allowed to leave the building, the smallest useful setup is two machines on the same network: one that asks, one with the GPU that answers. It takes about thirty lines of…

September 24, 20244 min read

The baseline before the fancy model: medical image segmentation

In short: before training a U-Net, write a threshold with a little cleanup and score it with the same metric. It's quick to write, it tells you how hard the problem really is, and on high-contrast…

August 13, 20245 min read

Doing ML in a field that runs on R and SPSS

In short: psychiatry researchers trust R and SPSS because they've checked those tools for years. Python output earns the same trust when it reproduces a result they already know, comes back in the…

July 30, 20245 min read

Federated learning: the model was the easy part

In short: when sites can't share their data, the model code is the smallest part of the work. Most of the effort goes into making the data mean the same thing everywhere, coping with sites that drop…