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

Krikunov, Alexey V.; Bolgova, E. V.; Krotov, Evgeniy; Abuhay, Tesfamariam M.; Yakovlev, A. N.; Kovalchuk, Sergey V.

doi:10.1016/j.procs.2016.05.332

Cited by 20 publications

(7 citation statements)

References 17 publications

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“…However, predictions validity is highly dependent on the observational data quality. Krikunov et al (2016) used SVM on a data set of 3,980 episodes to predict a treatment status of acute coronary syndrome episodes.…”

Section: Re Vie W On Machine Le Arning Appli C Ati On S For Pred I mentioning

confidence: 99%

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Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Section: Re Vie W On Machine Le Arning Appli C Ati On S For Pred I mentioning

confidence: 99%

“…Krikunov et al. (2016) used SVM on a data set of 3,980 episodes to predict a treatment status of acute coronary syndrome episodes. The accuracy for patient's outcome prediction was 65%; for patient's reanimation time, it was 89%, and for patient length of stay, it was 56%.…”

Section: Review On Machine Learning Applications For Predictive Data mentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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“…The mentioned ideas can be implemented within a modular medical information system as an extension of the system [96]. Finally, the framework can be considered as a methodology for experimental study [97]. Within this section, a generalized toolbox (see Fig.…”

Section: Simulation Solutionmentioning

confidence: 99%

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

Kovalchuk

Funkner

Metsker

et al. 2018

Journal of Biomedical Informatics

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“…Detailed description of the approach, algorithms, and results on CPs discovering, clustering and analysis including comparison of three version of CP discovery algorithms with performance 5 Demonstration available at https://www.youtube.com/watch?v=EH74f1w6EeY 16 comparison can be found in [10]. An important outcome of the approach being applied in this application is interpretability of the clusters and identified patterns.…”

Section: Problem #2: Modeling Health Care Processmentioning

confidence: 99%

“…). Within this research we are trying to extend model calibration and DA with EC techniques to develop more flexible and accurate multi-model ensembles.Problem #2 (clinical pathways (CPs) modelling) is important in several ongoing project aimed to model-based decision support in healthcare (see, e.g [16][17][18]…”

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confidence: 99%

A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification

et al. 2018

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Complex systems' modeling and simulation are powerful ways to investigate a multitude of natural phenomena providing extended knowledge on their structure and behavior. However, enhanced modeling and simulation require integration of various data and knowledge sources, models of various kinds (data-driven models, numerical models, simulation models, etc.), intelligent components in one composite solution. Growing complexity of such composite model leads to the need of specific approaches for management of such model. This need extends where the model itself becomes a complex system. One of the important aspects of complex model management is dealing with the uncertainty of various kinds (context, parametric, structural, input/output) to control the model. In the situation where a system being modeled, or modeling requirements change over time, specific methods and tools are needed to make modeling and application procedures (meta-modeling operations) in an automatic manner. To support automatic building and management of complex models we propose a general evolutionary computation approach which enables managing of complexity and uncertainty of various kinds. The approach is based on an evolutionary investigation of model phase space to identify the best model's structure and parameters. Examples of different areas (healthcare, hydrometeorology, social network analysis) were elaborated with the proposed approach and solutions.

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

Cited by 20 publications

References 17 publications

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification

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