10 citations · 27 across the 5 of their papers we have counts for
5 papers
"Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues
Tom Röhr, Soumyadeep Roy, Fares Al Mohamad +4
In a doctor-patient dialogue, the primary objective of physicians is to diagnose patients and propose a treatment plan. Medical doctors guide these conversations through targeted q…
Generalist embedding models are better at short-context clinical semantic search than specialized embedding models
Jean-Baptiste Excoffier, Tom Roehr, Alexei Figueroa +3
The increasing use of tools and solutions based on Large Language Models (LLMs) for various tasks in the medical domain has become a prominent trend. Their use in this highly criti…
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text
Betty van Aken, Jens-Michalis Papaioannou, Marcel G. Naik +4
The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but prov…
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer +3
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities. We simulate patients at admission time, when decisi…
Canonical Trends: Detecting Trend Setters in Web Data
Felix Biessmann, Jens-Michalis Papaioannou, Mikio Braun +1
Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem…