Alexey N. Yakovlev

Complex Data-driven Predictive Modeling in Personalized Clinical Decision Support for Acute Coronary Syndrome Episodes☆

Abstract The objective of this paper is to demonstrate the development of complex model of clinical episode, based on data-driven approach, for decision support in treatment of ACS (Acute Coronary Syndrome). The idea is aimed at improving predictive capability of a data-driven model by combining different models within a composite data-driven model. It can be implemented either hierarchical or alternative combination of models. Three examples of data-driven models are described: simple classifier, outcome prediction based on reanimation time and states-based prediction model, to be used as part of complex model of episodes. To implement the proposed approach, a generalized architecture of data-driven clinical decision support systems was developed. The solution is developed as a part of complex clinical decision support system for cardiac diseases for Federal Almazov North-West Medical Research Centre in Saint Petersburg, Russia.

Distributed data-driven platform for urgent decision making in cardiological ambulance control

Abstract This paper presents ongoing research aimed at developing a data-driven platform for clinical decision support systems (DSSs) that require integration and processing of various data sources within a single solution. Resource management is developed within a framework of an urgent computing approach to address changing requirements defined by the incoming flow of patients with urgent diseases. This work presents DSS for support of ambulance and emergency medical service management for patients with acute coronary syndrome (ACS) as a working example with integration distributed streaming data sources as well as data storages (containing electrocardiography (ECG) data, electronic medical records (EMR), real-time monitoring of medical facilities, and schedules of hospitals within a network). This DSS has been developed in collaboration with the Federal Almazov North-West Medical Research Centre in Saint Petersburg.

Simulation of emergency care for patients with ACS in Saint Petersburg for ambulance decision making

Abstract One of the stages of emergency medical care in case of Acute Coronary Syndrome (ACS) (if there are medical conditions for surgical intervention) is directly linked to the time between the first contact with the patient and the and inflating the balloon in the coronary artery (in a medical institution). Time of the operation start a medical facility depends on the time of patient delivery to hospital, as well as on the waiting time in the queue in the institution. This paper describes a development of ambulance model for obtaining aggregate estimation of these two periods of time. The estimation time is obtained by means of described in the article the decision support system (DSS) in the ambulance service. Unlike modern navigation systems DSS takes into account ambulance vehicle behavior (the ability to exit into oncoming traffic) and availability of free operation rooms. With the help of the described simulation model of the ambulance service we carried out the time distribution analysis (between the first contact with the patient and surgical intervention in case of ACS) in St. Petersburg, Russia. Simulation scenario uses real data on the work of ambulance service in the city.

Machine Learning Based Text Mining in Electronic Health Records: Cardiovascular Patient Cases

This article presents the approach and experimental study results of machine learning based text mining methods with application for EHR analysis. It is shown how the application of ML-based text mining methods to identify classes and features correlation to increases the possibility of prediction models. The analysis of the data in EHR has significant importance because it contains valuable information that is crucial for the decision-making process during patient treatment. The preprocessing of EHR using regular expressions and the means of vectorization and clustering medical texts data is shown. The correlation analysis confirms the dependence between the found classes of diagnosis and individual characteristics of patients and episodes. The medical interpretation of the findings is also presented with the support of physicians from the specialized medical center, which confirms the effectiveness of the shown approach.

Towards Model-Based Policy Elaboration on City Scale Using Game Theory: Application to Ambulance Dispatching

The paper presents early results on the development of a generalized approach for modeling and analysis of the interaction of multiple stakeholders in city environment while providing services to citizens under the regulation of city authorities. The approach considers the interaction between main stakeholders (organizations of various kind, citizens, and city authorities) including information and finances exchange, activities taken and services or goods provided. The developed approach is based on a combination of game-theoretic modeling and simulation of service providers interaction. Such combination enables consideration of confronting stakeholders as well as determined (e.g., scheduled) and stochastic variation in characteristics of system’s elements. The goal of this approach development is supporting of analysis and optimization of city-level regulation through legislative, financial, and informational interaction with organizations and environment of a city. An example of ambulance dispatching during providing emergent care for acute coronary syndrome (ACS) patients is considered. The example is analyzed in a simplified linear case and in practical application to dispatching ambulances providing service for ACS patients in Saint Petersburg.