ML/AI Engineer & PI. Building a Nigerian chest X-ray benchmark to measure how well Western AI models work on African patients.

Joined March 2019
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Building Africa's first chest X-ray AI benchmark from Nigerian hospital. What happens when AI trained on American chest X-rays is deploy in a Nigerian hospital? No one has rigorously measured how well they work on our patients. I'm 9 weeks into a research project to find out.
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Week 10/48. This week I audited our entire NLP pipeline. The question: should you use rules or transformers for medical text? The answer surprised me. Thread.
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Also verified negation detection across 5,517 reports. 0 failures out of 134 negated mentions. And reviewed 617 reports with 3 labels — 89.9% of all label assignments supported by text evidence.
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The lesson: don't choose rules OR transformers. Use both. Rules give you interpretable precision. BERT gives you semantic reach. Our ensemble: rules primary on 11 labels, BERT primary on 2. 495 reports ready for radiologist validation. #MedicalAI #NLP #ChestXray #Africa #BuildInPublic
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A prompt you can use in Claude to help build & launch apps, v/@milesdeutscher.
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The pipeline: two extractors working in parallel. Rule-based: 447-phrase bilingual dictionary with negation detection BioClinicalBERT: fine-tuned transformer, Normal as 14th label Rules handle 11 labels. BERT handles 2 that rules can't detect. They vote. Flag disagreements.
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The dataset: 5,517 chest X-rays from FMC Ebute-Metta, Lagos. Free-text radiology reports. English mixed with local medical terms. 48% had no section headers. Step 1: turn those reports into structured labels 13 pathologies Normal. That took Weeks 1–8.
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Excitement is brewing as I gear up to witness #StudentSummit event. Can't wait to connect with like-minded individuals and dive deep into the world of innovation and creativity!!! Thanks to @MSFTReactor @MicrosoftNG @MicrosoftLearn @TheOyinbooke @japhletnwamu #SSLagosWatchParty
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Reminder
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30 Jun 2022
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