Publications by authors named "Marie Ansoborlo"
Background: Total hip, knee and shoulder arthroplasties (THKSA) are increasing due to expanding demands in ageing population. Material surveillance is important to prevent severe complications involving implantable medical devices (IMD) by taking appropriate preventive measures. Automating the analysis of patient and IMD features could benefit physicians and public health policies, allowing early issue detection and decision support.
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- 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.
At the core of the child's medical, social, and educational pathway, coordination and referral platforms (CRPs) for neurodevelopmental disorders (NDDs) have been gradually deployed in France since 2018 and support the early detection of NDDs in children. The 112 nationwide CRPs do not benefit from a common electronic health record system. Our aim was to propose an HER model for CRP to enable real-life data reuse, optimize care pathway management and conduct pre-screening for research.
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- The study focuses on creating an automated algorithm to quickly identify patients who might qualify for specific anti-cancer treatments, reducing the lengthy prescreening process for clinical trials.
- It analyzed 640 anonymized reports from multidisciplinary team meetings related to lung cancer, using regular expressions to extract relevant eligibility criteria, achieving impressive metrics: an average F1-score of 93%, 98% precision, and 92% recall.
- Despite these successes, there were significant inconsistencies in the completeness of patient and tumor information, with genetic mutations being particularly underreported and challenging to extract automatically.
Background: A noncompleter is defined as a participant who leaves a trial before the end of the planned follow-up. Research in nursing homes is highly exposed to this problem because of high death rates.
Objectives: The aim of this trial is to assess the statistical management of noncompleters in cluster randomized trials carried out in nursing homes.
Hip arthroplasty represents a large proportion of orthopaedic activity, constantly increasing. Automating monitoring from clinical data warehouses is an opportunity to dynamically monitor devices and patient outcomes allowing improve clinical practices. Our objective was to assess quantitative and qualitative concordance between claim data and device supply data in order to create an e-cohort of patients undergoing a hip replacement.
View Article and Find Full Text PDFPrescreening in Oncology Using Data Sciences: The PreScIOUS Study.
Stud Health Technol Inform
May 2021
The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to targeted therapies. This study aimed to develop an automated algorithm based on natural language processing to detect patients and tumor characteristics to reduce the time-consuming prescreening for trial inclusions.
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