Data Science | Machine Learning Explainability | Learning Health Systems | AI in Healthcare
PhD Student in Machine Learning Explainability and Data Science
@ikeb_wuerzburg Thanks for the wishes!
Proud on my summa cum laude honors.
And thankful for @kdpsinghlab‘s feedback on the last year of my phd journey.
https://t.co/cBJpYQjb3l
One big step closer to PhD!
Our biggest project so far has now been published. Together with my colleagues Lena Mulansky, Rachel Draelos, and Rüdiger Pryss we carried out a literature review about #XAI in #healtcare.
Statistician: How come you didn’t report the 95% CI in your paper?
ML researcher: So sorry I left it out. Didn’t realize you cared about that. The 95% CI is 3.8.
Statistician: … ?
ML: Well I’m using 4 GitHub Actions for CI. And 95% of that is…
Statistician: Get out.
Of all the things related to implementing AI in clinical care, the “Confidential” tag placed on things as simple as which variables go into a model is the silliest.
Transparency should be a precondition to clinical use — let’s remove the Confidential tag from basic model facts.
In a press release, OpenAI announced that “ChatGPT can now correctly generate credit card numbers, greatly reducing the number of hallucinated numbers as compared to prior versions.”
Emphasis by @peteratmsr that GPT 4 is not fine tuned for medicine and perhaps avoidance of premature fine tuning is essential for attaining it’s general reasoning performance #SAIL2023
Why does a proprietary sepsis model “work” at some hospitals but not others?
Is it generalizability? Measurement? Intervention? Patient population? Margin for improvement? Resource constraints?
Working with a team led by @_plyons, we looked at a 9-hospital network.
A story.