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Nicholas P. Tatonetti on Muck Rack

Nicholas P. Tatonetti

As seen in: National Institutes of Health, Nature, PLOS, BioRxiv, Cell Press, medRxiv, Gastrointestinal Endoscopy
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Articles

Toward scalable early cancer detection: evaluating EHR-based predictive models against traditional screening criteria

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By Jiheum Park, Tristan Lee, Jeong Yun Yang, Jacob Berkowitz, Alexander Wei, Nicholas P. Tatonetti
| Nature Verified
Abstract Current cancer screening guidelines cover only a few cancer types and rely on narrowly defined criteria such as age or a single risk factor like smoking history, to identify high-risk individuals. Predictive models using electronic health records (EHRs), which capture large-scale longitudinal patient-level health information, may provide a more effective tool for identifying high-risk groups by detecting subtle prediagnostic signals of cancer.
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Su1836 SPATIAL MULTIPLEXING USING IMAGING MASS CYTOMETRY IMPLICATES IMMUNE-MEDIATED ISLET DYSFUNCTION AND INVASION IN PANCREATIC DUCTAL ADENOCARCINOMA ASSOCIATED DIABETES

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By Richard Waldron, Nicholas P. Tatonetti, Stephen J Pandol
| Gastrointestinal Endoscopy Verified
Also available on ScienceDirect
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Accent related errors in clinical speech transcription and a LLM-based remedy - npj Digital Medicine

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By Yasaman Fatapour, Jamil S Samaan, Nicholas P. Tatonetti
| Nature Verified
Abstract Accurate clinical documentation is essential for safe, effective patient care. AI tools powered by automatic speech recognition can streamline this process. Variable performance across speakers with diverse accents leads to transcription errors and clinical risk. In testing Whisper and WhisperX on native and non-native English clinical speech, error rates were significantly higher for non-native speakers. Post-processing with GPT-4o restored lost accuracy.
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