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KAA health tech startup paid me a huge sum of amount to figure out why their AI kept failing. I found the problem in 2 hours.
They built a model to predict diabetic ketoacidosis from lab data.
Training accuracy: 87% ✓
Production accuracy: 53% ❌
Worse than flipping a coin.
Here’s what I found:
THE SETUP:
∙ 60K lab results from MIMIC dataset
∙ Filtered for diabetes patients
∙ Trained standard ML model
∙ Everything by the book
THE PROBLEM:
I asked one question: “What percentage of the ‘abnormal’ potassium values were ACTUALLY abnormal?”
42% of “critically high” potassium? The patient’s level had been normal 6 hours earlier.
That’s not biology. That’s hemolysis—when red blood cells rupture in the sample tube.
THE CONTAMINATION:
I found FIVE categories of garbage in their training data:
• 42% - Hemolyzed samples (false high potassium)
• 18% - Lipemic samples (false low glucose)• 12% - Biotin interference (false thyroid results)
• 15% - Wrong collection times (invalid trends)
• 13% - Reference ranges coded backwards
38% of their training labels were WRONG.
They weren’t training on disease patterns. They were training on lab artifacts.
THE FIX:
We added pre-analytical filters:
✓ Delta checks (flag impossible changes)
✓ Hemolysis exclusions
✓ Medication interference checks
✓ Analyzer-specific reference ranges
Dataset shrunk from 60K → 40K samples.
But production accuracy? 53% → 91%
THE OUTCOME:
The startup is now raising Series A on those numbers.
They didn’t need better algorithms.
They didn’t need more data.
They needed someone who understood that lab data isn’t just numbers—every value has a story.
Collection time. Sample quality. Instrument calibration. Interfering substances.
Ignore those, and you’re training on noise.
My advice: Before you train ANY model on lab data, hire someone who’s actually worked in a laboratory.
Have them audit for clinical reality, not statistical outliers.
That $15K audit saved millions in wasted development—and possibly prevented catastrophic patient harm.
💬 Data scientists: Have you audited YOUR training data for domain-specific artifacts? 👇
#HealthTech #MachineLearning #DataScience #AIinHealthcare #medicalai
@kagbeogbugo
