The personalized approach in a medical decentralized diagnostic and treatment

Melnykova, Nataliia; Shakhovska, Nataliya; Sviridova, Tanya

doi:10.1109/cadsm.2017.7916139

Cited by 10 publications

(6 citation statements)

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“…The processing of medical data also requires the development of appropriate solutions that allow for the collection, transmission, storage and protection of data. [19,20,26,27] To date, most existing algorithms that allow to you effectively solve current problems do not cover the entire list of problems associated with the personalization of decisions during the maintenance of the treatment process. Against this background, there is a need for new solutions that allow valid processing and storage of data without losing the quality of the results.…”

Section: Discussionmentioning

confidence: 99%

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Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

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The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

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Section: Discussionmentioning

confidence: 99%

“…The main type of problems solved by the k-means algorithm is the assumption (hypotheses) about the number of clusters, and the diversity of the instances in different clusters. The choice of k number may be based on the results of previous studies and theoretical considerations [20].…”

Section: Related Sourcesmentioning

confidence: 99%

Data Driven Analytics for Personalized Medical Decision Making

Melnykova¹,

Shakhovska²,

ml.³

et al. 2020

Preprint

Self Cite

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“…Firstly, this contributes to the widespread use of titanium alloys for medical products, including implants, dentures, etc. Secondly, the systematization of numerous experimental studies and the results of clinical trials [3,4] allows the creation of large databases containing the functional properties of these materials.…”

Section: Introductionmentioning

confidence: 99%

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

Izonin¹,

Trostianchyn²,

Duriagina³

et al. 2018

IJISA

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This document presents two developed methods for solving the classification task of medical implant materials based on the compatible use of the Wiener Polynomial and SVM. The high accuracy of the proposed methodology for solving this task are experimentally confirmed. A comparison of the proposed methods with existing ones: Logistic Regression; Linear SVC; Random Forest; SVC (linear kernel); SVC (RBF kernel); Random Forest + Wiener Polynomial is carried out. The duration of training of all methods that described in work is investigated. The article presents the visualization of all method results for solving this task.

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“…Health insurance is one of the main directions of modern healthcare system development [1,2]. The prediction of individual health insurance costs is one of the most important tasks in this direction.…”

Section: Introductionmentioning

confidence: 99%

Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs

Tkachenko

Izonin

Vitynskyi

et al. 2018

Data

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The paper describes a new non-iterative linear supervised learning predictor. It is based on the use of Ito decomposition and the neural-like structure of the successive geometric transformations model (SGTM). Ito decomposition (Kolmogorov–Gabor polynomial) is used to extend the inputs of the SGTM neural-like structure. This provides high approximation properties for solving various tasks. The search for the coefficients of this polynomial is carried out using the fast, non-iterative training algorithm of the SGTM linear neural-like structure. The developed method provides high speed and increased generalization properties. The simulation of the developed method’s work for solving the medical insurance costs prediction task showed a significant increase in accuracy compared with existing methods (common SGTM neural-like structure, multilayer perceptron, Support Vector Machine, adaptive boosting, linear regression). Given the above, the developed method can be used to process large amounts of data from a variety of industries (medicine, materials science, economics, etc.) to improve the accuracy and speed of their processing.

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The personalized approach in a medical decentralized diagnostic and treatment

Cited by 10 publications

References 1 publication

Data Driven Analytics for Personalized Medical Decision Making

Data Driven Analytics for Personalized Medical Decision Making

The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production

Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs

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