Hospitalization admission control of emergency patients using markovian decision processes and discrete event simulation

International audienceThis paper addresses the hospitalization admission control policies of patients from an emergency department that should be admitted shortly or transferred. When an emergency patient arrives, depending on his/her health condition, a physician may decide to hospitalize him/her in a specific department. Patient admission depends on the availability of beds, the length of stay (LOS) and the reward of hospitalization which are both patient-class specific. The problem consists in determining patient admission policies in order to maximize the overall gain. We first propose a Markov Decision Process (MDP) Model for determination of the optimal patient admission policy under some restrictive and necessary assumptions such as exponentially distributed LOS. A simulation model is then built to assess MDP admission policies under realistic conditions. We show that MDP policies significantly improve the overall gain for different types of facilities

Effects of moderate hyperbilirubinemia on nutritive swallowing and swallowing-breathing coordination in preterm lambs

Background: Hyperbilirubinemia (HB) occurs in 90% of preterm newborns. HB induces acute neurological disorders (somnolence, abnormal tone, feeding difficulties, auditory dysfunction) and alterations in respiratory control. These findings suggest brainstem neurotoxicity that could also affect swallowing centers. Objective: To test the hypothesis that HB impairs nutritive swallowing (NS) and swallowing-breathing coordination. Methods: Two groups of preterm lambs (born 14 days prior to term), namely control (n = 6) and HB (n = 5), were studied. On day 5 of life (D0), moderate HB (150-250 µmol/l) was induced during 17 h in the HB group. Swallowing was assessed via recording of pharyngeal pressure and respiration by respiratory inductance plethysmography and pulse oximetry. The effect of HB on NS was assessed during standardized bottle-feeding. A second recording was performed 48 h after recovery from HB (D3). Results: Swallows were less frequent (p = 0.003) and of smaller volume (p = 0.01) in HB lambs while swallowing frequency was decreased (p = 0.004). These differences disappeared after HB normalization. Swallowing-breathing coordination was impaired in HB lambs, with a decrease in percent time with NS burst-related apneas/hypopneas at D0 and D3. Simultaneously, HB lambs tended to experience more severe desaturations (<80%) during bottle-feeding. Finally, following bottle-feeding, the respiratory rate was significantly lower, along with an increased apnea duration in HB lambs. Conclusions: Swallowing and swallowing-breathing coordination are altered by acute moderate HB in preterm lambs. Decreased efficiency at bottle-feeding is accompanied by continuation of breathing during swallow bursts, which may promote lung aspiration

Costs and mortality associated with HIV: a machine learning analysis of the French national health insurance database

BACKGROUND: The objective is to characterise the economic burden to the healthcare system of people living with HIV (PLWHIV) in France and to help decision makers in identifying risk factors associated with high-cost and high mortality profiles. DESIGN AND METHOD: The study is a retrospective analysis of PLWHIV identified in the French National Health Insurance database (SNDS). All PLWHIV present in the database in 2013 were identified.  All healthcare resource consumption from 2008 to 2015 inclusive was documented and costed (for 2013 to 2015) from the perspective of public health insurance. High-cost and high mortality patient profiles were identified by a machine learning algorithm. RESULTS: In 2013, 96,423 PLWHIV were identified in the SNDS database, including 3,373 incident cases. Overall, 3,224 PLWHIV died during the three-year follow-up period (mean annual mortality rate: 1.1%). The mean annual per capita cost incurred by PLWHIV was € 14,223, corresponding to a total management cost of HIV of € 1,370 million in 2013. The largest contribution came from the cost of antiretroviral medication (M€ 870; 63%) followed by hospitalisation (M€ 154; 11%). The costs incurred in the year preceding death were considerably higher. Four specific patient profiles were identified for under/over-expressing these costs, suggesting ways to reduce them. CONCLUSION: Even though current therapeutic regimens provide excellent virological control in most patients, PLWHIV have excess mortality. Other factors such as comorbidities, lifestyle factors and screening for cancer and cardiovascular disease, need to be targeted in order to lower the mortality and cost associated with HIV infection

Modélisation automatique et simulation de parcours de soins à partir de bases de données de santé

During the last two decades, the amount of data collected in Information Systems has drastically increased. This large amount of data is highly valuable. This reality applies to health-care where the computerization is still an ongoing process. Existing methods from the fields of process mining, data mining and mathematical modeling cannot handle large-sized and variable event logs. Our goal is to develop an extensive methodology to turn health data from event logs into simulation models of clinical pathways. We first introduce a mathematical framework to discover optimal process models. Our approach shows the benefits of combining combinatorial optimization and process mining techniques. Then, we enrich the discovered model with additional data from the log. An innovative combination of a sequence alignment algorithm and of classical data mining techniques is used to analyse path choices within long-term clinical pathways. The approach is suitable for noisy and large logs. Finally, we propose an automatic procedure to convert static models of clinical pathways into dynamic simulation models. The resulting models perform sensitivity analyses to quantify the impact of determinant factors on several key performance indicators related to care processes. They are also used to evaluate what-if scenarios. The presented methodology was proven to be highly reusable on various medical fields and on any source of event logs. Using the national French database of all the hospital events from 2006 to 2015, an extensive case study on cardiovascular diseases is presented to show the efficiency of the proposed framework.Les deux dernières décennies ont été marquées par une augmentation significative des données collectées dans les systèmes d'informations. Cette masse de données contient des informations riches et peu exploitées. Cette réalité s’applique au secteur de la santé où l'informatisation est un enjeu pour l’amélioration de la qualité des soins. Les méthodes existantes dans les domaines de l'extraction de processus, de l'exploration de données et de la modélisation mathématique ne parviennent pas à gérer des données aussi hétérogènes et volumineuses que celles de la santé. Notre objectif est de développer une méthodologie complète pour transformer des données de santé brutes en modèles de simulation des parcours de soins cliniques. Nous introduisons d'abord un cadre mathématique dédié à la découverte de modèles décrivant les parcours de soin, en combinant optimisation combinatoire et Process Mining. Ensuite, nous enrichissons ce modèle par l’utilisation conjointe d’un algorithme d’alignement de séquences et de techniques classiques de Data Mining. Notre approche est capable de gérer des données bruitées et de grande taille. Enfin, nous proposons une procédure pour la conversion automatique d'un modèle descriptif des parcours de soins en un modèle de simulation dynamique. Après validation, le modèle obtenu est exécuté pour effectuer des analyses de sensibilité et évaluer de nouveaux scénarios. Un cas d’étude sur les maladies cardiovasculaires est présenté, avec l’utilisation de la base nationale des hospitalisations entre 2006 et 2015. La méthodologie présentée dans cette thèse est réutilisable dans d'autres aires thérapeutiques et sur d'autres sources de données de santé

Process discovery, analysis and simulation of clinical pathways using health-care data

Les deux dernières décennies ont été marquées par une augmentation significative des données collectées dans les systèmes d'informations. Cette masse de données contient des informations riches et peu exploitées. Cette réalité s’applique au secteur de la santé où l'informatisation est un enjeu pour l’amélioration de la qualité des soins. Les méthodes existantes dans les domaines de l'extraction de processus, de l'exploration de données et de la modélisation mathématique ne parviennent pas à gérer des données aussi hétérogènes et volumineuses que celles de la santé. Notre objectif est de développer une méthodologie complète pour transformer des données de santé brutes en modèles de simulation des parcours de soins cliniques. Nous introduisons d'abord un cadre mathématique dédié à la découverte de modèles décrivant les parcours de soin, en combinant optimisation combinatoire et Process Mining. Ensuite, nous enrichissons ce modèle par l’utilisation conjointe d’un algorithme d’alignement de séquences et de techniques classiques de Data Mining. Notre approche est capable de gérer des données bruitées et de grande taille. Enfin, nous proposons une procédure pour la conversion automatique d'un modèle descriptif des parcours de soins en un modèle de simulation dynamique. Après validation, le modèle obtenu est exécuté pour effectuer des analyses de sensibilité et évaluer de nouveaux scénarios. Un cas d’étude sur les maladies cardiovasculaires est présenté, avec l’utilisation de la base nationale des hospitalisations entre 2006 et 2015. La méthodologie présentée dans cette thèse est réutilisable dans d'autres aires thérapeutiques et sur d'autres sources de données de santé.During the last two decades, the amount of data collected in Information Systems has drastically increased. This large amount of data is highly valuable. This reality applies to health-care where the computerization is still an ongoing process. Existing methods from the fields of process mining, data mining and mathematical modeling cannot handle large-sized and variable event logs. Our goal is to develop an extensive methodology to turn health data from event logs into simulation models of clinical pathways. We first introduce a mathematical framework to discover optimal process models. Our approach shows the benefits of combining combinatorial optimization and process mining techniques. Then, we enrich the discovered model with additional data from the log. An innovative combination of a sequence alignment algorithm and of classical data mining techniques is used to analyse path choices within long-term clinical pathways. The approach is suitable for noisy and large logs. Finally, we propose an automatic procedure to convert static models of clinical pathways into dynamic simulation models. The resulting models perform sensitivity analyses to quantify the impact of determinant factors on several key performance indicators related to care processes. They are also used to evaluate what-if scenarios. The presented methodology was proven to be highly reusable on various medical fields and on any source of event logs. Using the national French database of all the hospital events from 2006 to 2015, an extensive case study on cardiovascular diseases is presented to show the efficiency of the proposed framework

Stochastic simulation of clinical pathways from raw health databases

International audienceThis paper presents a method to automatically create stochastic simulation models of clinical pathways from raw databases. We introduce an automatic procedure to convert a process model, discovered with process mining, into an actionable simulation model. The concept of state charts is used and enriched to incorporate the distinctive features of healthcare processes into the model. The clinical pathway model is used to simulate new patients' sequence of events. The resulting model is validated by comparing key performances indicators with historical data. Finally, we use the model to perform an automatically setup sensitivity analysis. The whole process is automated and can be used with any input data

Discovery of patient pathways from a national hospital database using process mining and integer linear programming

International audienceThe analysis of patient pathways from event log is gaining importance in the field of medical information. It provides deep insights about the care process and the ways to improve it. This paper combines optimization and process mining. A new Integer Linear Programming model is proposed to discover the care process at a macroscopic scale from a large-size database. When dealing with health-care data, the main challenge to overcome is the considerable variability of patients' behaviors. An original size constraint and an aggregation method are used to create simple but significant process models. The results of a case study on heart failures confirm the ability of the approach to reveal the process information behind the data

An optimization-based process mining approach for explainable classification of timed event logs

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Diagnosing Hemophagocytic Lymphohistiocytosis with Machine Learning: A Proof of Concept

Hemophagocytic lymphohistiocytosis is a hyperinflammatory syndrome characterized by uncontrolled activation of immune cells and mediators. Two diagnostic tools are widely used in clinical practice: the HLH-2004 criteria and the Hscore. Despite their good diagnostic performance, these scores were constructed after a selection of variables based on expert consensus. We propose here a machine learning approach to build a classification model for HLH in a cohort of patients selected by glycosylated ferritin dosage in our tertiary center in Lyon, France. On a dataset of 207 adult patients with 26 variables, our model showed good overall diagnostic performances with a sensitivity of 71.4% and high specificity, and positive and negative predictive values which were 100%, 100%, and 96.9%, respectively. Although generalization is difficult on a selected population, this is the first study to date to provide a machine-learning model for HLH detection. Further studies will be required to improve the machine learning model performances with a large number of HLH cases and with appropriate controls