A Deep Learning Framework for Automated ICD-10 Coding.

The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a patient's stay. The aim of this paper is to propose an automated coding system to assist physicians in the assignment of ICD codes to EHR. For this purpose, we created a pipeline of Natural Language Processing (NLP) and Deep Learning (DL) models able to extract the useful information from French medical texts and to perform classification.

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based "white-box" classification algorithm for medical data.

Background: Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay.

Methods: Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection.

Comparing Drools and ontology reasoning approaches for telecardiology decision support.

Implantable cardioverter defibrillators can generate numerous alerts. Automatically classifying these alerts according to their severity hinges on the CHA2DS2VASc score. It requires some reasoning capabilities for interpreting the patient's data.