Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years.

Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009-2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome.

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge.

Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach.

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity.

Hospital burden of coronary artery disease: Trends of myocardial infarction and/or percutaneous coronary interventions in France 2009-2014.

Background: Currently, cardiovascular disease (CVD) is widely acknowledged to be the first leading cause of fatality in the world with 31% of all deaths worldwide and is predicted to remain as such in 2030. Furthermore, CVD is also a major cause of morbidity in adults worldwide. Among these diseases, the coronary artery disease (CAD) is the most common cause, accounting for over 40% of CVD deaths.

PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns.