PatientGuard
The project investigates how to extract relevant information from clinical documents using LLM-based methods and evaluates the quality of these methods for the prediction and prevention of hospital-acquired infections.
Factsheet
- Schools involved School of Engineering and Computer Science
- Institute(s) Institute for Patient-centered Digital Health (PCDH)
- Research unit(s) PCDH / AI for Health
- Funding organisation Innosuisse
- Duration (planned) 04.11.2024 - 03.11.2025
- Head of project Prof. Dr. Kerstin Denecke
- Project staff Daniel Reichenpfader
- Partner MedNota GmbH
- Keywords Artificial intelligence, large language model, information extraction
Situation
Hospital-acquired infections are a major challenge for healthcare systems worldwide. They harm patients, increase healthcare costs and extend hospital length of stays. In Switzerland, an average of 5,9% of patients develop a healthcare-associated infection while in hospital. Up to 50% of these cases can be prevented by targeted measures. With this in mind, the Federal Office of Public Health FOPH wants to protect the population more effectively, i.e. reduce the number of infections and the associated long-term effects and mortality. Hospital-acquired infections can be detected or predicted by analysing data from electronic patient records. In hospitals, however, a considerable amount of infection-related data is stored in unstructured formats, e.g. in PDF reports (surgical reports, laboratory results, etc.) or clinical notes (handwritten or electronic). These documents contain valuable insights, but the manual extraction of relevant information is time-consuming and often leads to infection risks being overlooked. We want to use open-source large language models (LLMs) such as Llama 3.2 to process and analyse this unstructured data and to extract meaningful information about hospital-acquired infections for prediction and prevention purposes.
Course of action
We will use, compare and optimise various LLM-based methods to extract relevant clinical data from unstructured texts that refer to hospital-acquired infections.
Result
The project provides a feasibility analysis, information on which indicators can be extracted from which documents, and a comparison of the quality of various large language models.
