Using Deep Learning to Suggest Treatment for Proximal Humerus Fractures.

Proximal humeral fractures are among the most common fractures seen in emergency departments. Accurately diagnosing and selecting the most appropriate treatment for these fractures can be challenging, and consultation with a senior orthopedic surgeon can be time-consuming for both the patient and the emergency unit. We developed a machine learning model for predicting the type of treatment based on injury radiographic images.

Semantic Querying of Hospital Data Using an Ontology-Based Model of Discharge Summaries and ICD 10.

This paper focusses on the question of knowledge-based modeling of discharge summaries for semantic requesting in the context of hospital reporting process. An ontology was designed and implemented to capture patient data and required expert knowledge. The modeling process was carried out manually using Protégé after an initial reverse engineering of the official format of discharge summaries.

Association of Occupational Pesticide Exposure With Immunochemotherapy Response and Survival Among Patients With Diffuse Large B-Cell Lymphoma.

Importance: Professional use of pesticides is a risk factor for non-Hodgkin lymphoma. The main biological mechanisms of pesticides and chemotherapy are genotoxicity and reactive oxygen species generation. Cellular adaptation among patients exposed to low doses of genotoxic and oxidative compounds might hinder chemotherapy efficiency in patients with lymphoma.

The issue of building generative terminologies for automatic medical data aggregation.

This work addresses the problem of medical terminologies building. Starting from a set of elementary knowledge, a multi-hierarchical terminology is generated according to formal principles that are based on explicit classificatory points of view. The experimentation has been carried out on kidney diseases.