Abstract
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. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.
Highlights
Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research [1]
We detailed the technique used, and we applied this approach to the morbid events chronology to predict in-hospital mortality after an acute coronary syndrome (ACS). en, we discussed the results and options for generalizing this methodology according to the data typology together with the retained data mining approach; we suggested applications for medical practice
Each discharge summary submitted to the French Hospital Discharge Database (FHDD) is linked to a national grouping algorithm leading to a French Diagnosis Related Group (DRG) [22]. is study was conducted according to the approval given by the Commission Nationale de l’Informatique et des Libertes (CNIL), agreement No 1375062
Summary
Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research [1]. International experience shows that the spectrum of application is wide: preventive medicine, improving care and quality of life, and reducing healthcare costs [2, 3]. Transition to electronic healthcare systems has led to the accumulation of vast amounts of medical data. Healthcare data is becoming just as important as administrative data, genomic, medical. Medical data mining has great potential for exploring hidden patterns in vast medical datasets [2]. Data mining prediction appears to be a promising tool [4]
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