Publications by authors named "G Bouzille"
Background: Intensive care units (ICUs) handle the most critical patients with a high risk of mortality. Due to those conditions, close monitoring is necessary and therefore, a large volume of data is collected. Collaborative ventures have enabled the emergence of large open access databases, leading to numerous publications in the field.
View Article and Find Full Text PDFMeasurement of Cumulative Drug Exposure from Clinical Data Warehouse.
Stud Health Technol Inform
November 2024
Polypharmacy (PP) and hyperpolypharmacy (HPP), are prevalent among cancer patients and are associated with an increased risk of drug-drug interactions (DDI) and potentially inappropriate medications (PIM). This study aimed to characterize PP, HPP, DDI, and PIM in patients with hematological malignancies hospitalized for hematopoietic stem cell transplantation (HSCT) by introducing a novel metric: cumulative drug exposure. Clinical data warehouse (CDW) records were employed to develop algorithms that quantified patients' cumulative exposure to these prescribing determinants during hospitalization.
View Article and Find Full Text PDFArticle Synopsis
- Electronic health data for implantable medical devices (IMD) allows real-time monitoring of risks, especially as joint surgeries like hip and knee replacements increase due to an aging population.
- A machine learning tool utilizing natural language processing (NLP) was created to automatically extract and analyze operation details from orthopedic medical reports, achieving excellent precision (97.0%) and recall (96.0%).
- By automating data extraction and monitoring of orthopedic devices through clinical data warehouses, the tool aims to enhance patient safety, support surgeons and policymakers with actionable insights, and improve compliance in medical reporting.
Secure Extraction of Personal Information from EHR by Federated Machine Learning.
Stud Health Technol Inform
August 2024
Article Synopsis
- Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) is challenging due to privacy and security concerns, prompting a study on Federated Learning (FL) for French EHRs.
- The study used a multilingual BERT model and involved a simulation with 20 hospitals, comparing individual models (using only local data) and federated models (collaborative global model).
- Results showed that FL models maintain data confidentiality and achieved a competitive F1 score of 75.7%, highlighting FL's potential for improving health data analysis and privacy in EHRs.
Article Synopsis
- The paper presents a new method to improve access to clinical data warehouses (CDWs) for researchers and biomedical companies.
- It introduces a clinical data catalogue that answers key questions about data availability, quantity, and generation to aid project development.
- A prototype of the catalogue is demonstrated using visualization from the CDW of Rennes University Hospital.