Decision Support for Medication Change of Parkinson's Disease Patients

Boshkoska, Biljana Mileva; Miljković, Dragana; Valmarska, Anita; Gatsios, Dimitrios; Rigas, George; Konitsiotis, Spyridon; Tsiouris, Κostas Μ.; Fotiadis, Dimitrios I.; Bohanec, Marko

doi:10.1016/j.cmpb.2020.105552

Cited by 25 publications

(13 citation statements)

References 17 publications

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“…It is supported by freely available software called DEXi (Bohanec, 2020). Because of their simple design, the power to interpret the model and explain the decision, as well as postoptimal analysis (plus–minus analysis; Bohanec, 2020), DEX models have found its application in many DSSs (Bohanec et al., 2013, 2017; Boshkoska et al., 2020; Bohanec, 2021). Building DEX models requires expert modeling, that is, articulating domain knowledge and representing it in terms of decision rules (Bohanec and Rajkovič, 1990).…”

Section: Methodsmentioning

confidence: 99%

“…We decided to develop DEX models from data as they satisfy all of these properties. DEX models have already been used for ski injury predictions (Bohanec et al., 2015) but also in other domains, such as the assessment of tourist farm service quality (Rozman et al., 2009), Parkinson's disease treatment (Boshkoska et al., 2020), identification of genetically modified food (Bohanec et al., 2017), and assessment of electric energy production (Bohanec et al., 2017). By being able to capture domain knowledge into a hierarchical set of rules, it is possible to predict ski injuries (in this research).…”

Section: Related Workmentioning

confidence: 99%

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Extracting decision models for ski injury prediction from data

Radovanović

Bohanec

Delibašić

2023

Int Trans Operational Res

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Creating decision models for risk assessment of ski injuries is a challenging task. Ski injuries are rare events, but they carry a high cost, that is, can cause working or movement disabilities. Usually, ski risk assessment is performed on small‐scale, case‐controlled studies where the effect of a single factor is evaluated. Recently, data mining and machine learning algorithms are being employed for ski risk assessment and injury prediction. However, these models do not generally satisfy the need for interpretation of the decision model, do not provide explanations for the predictions, and in general do not ensure the completeness and consistency of decision rules. To make data mining and machine learning models useful, one needs to implement the aforementioned properties. Decision support systems are expected to have these properties; however, the process of building such decision support systems is still tedious: it has to consider human biases, assumptions, and subjective values, as well as focus on the decision problem being solved. We propose a method for extraction of decision models from data at hand. Our method DIDEX, Data Induced DEcision eXpert, builds models that have desirable properties for inclusion in decision support systems. The proposed method is used to build a decision model for ski injury prediction based on data from Mt. Kopaonik ski resort, Serbia. The results show that DIDEX generates up to a five times simpler model compared to the existing domain expert DEX models while having a 6% better predictive accuracy. Additionally, its predictive accuracy is comparable to similar machine learning algorithms, such as decision tree classifiers, random forest, and logistic regression.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Extracting decision models for ski injury prediction from data

Radovanović

Bohanec

Delibašić

2023

Int Trans Operational Res

Self Cite

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“…DEX je bil na primer uporabljen pri problemu ocene prestrukturiranja kmetij (Nikoloski et al, 2017), zaščite pridelkov (Deguine et alidr., 2021). Na področju zdravstva je bil DEX na primer uporabljen za oceno tveganja za razvoj pljučnice pri umetnem predihavanju pacientov (Drnovšek et al, 2020) in za pomoč zdravnikom pri odločitvah o predpisovanju in kombiniranju medikamentne terapije pri zdravljenju Parkinsonove bolezni (Boshkoska et al, 2020) in druge. Ocena potenciala MSP za uporabo storitev visokozmogljivega računalništva v oblaku je bila implementirana kot spletni uporabniški vmesnik za zajem podatkov, povezan z večparametrskim modelom DEX (Kljajić Borštnar et al, 2015).…”

Section: Modeliranje Večkriterijskega Odločanjaunclassified

Znanstveno-raziskovalni izzivi na poti digitalne preobrazbe

Kljajić Borštnar,

Pucihar

2022

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Digitalna preobrazba se nanaša na uporabo digitalnih tehnologij za poenostavitev poslovanja, povečanje učinkovitosti, inoviranje proizvodov in storitev, prilagajanje potrebam strank, spreminjanje načinov dela in povezovanja znotraj in med organizacijami. Ima širši vpliv, zato je pomembno, da jo obravnavamo celovito (z vidika ekonomije, družbe in okolja). V monografiji predstavljamo teme s področja digitalne preobrazbe, ki bodo organizacijam in posameznikom v tem procesu v pomoč pri lažjem prehodu v digitalno poslovanje. V prispevkih opisujemo merjenje digitalne in podatkovne zrelosti v malih in srednje velikih podjetjih, analizo vpliva digitalnih tehnologij na elemente poslovnega modela, vpliv digitalne probrazbe na posameznika in družbo na problemu tehnologije podkožnih mikročipov ter spremembe obnašanja potrošnikov. Sledijo prispevki o uporabi tehnologije veriženja podatkovnih blokov in pregled raziskav na področju interneta stvari in kiberfizičnih sistemov, metodologija za obravnavo strateške usklajenosti med poslovnim delom podjetij in informacijsko tehnologijo ter pristop Essence, ki povezuje standarde kakovosti programske opreme in inženiring programske opreme.

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“…Unfortunately, at present, a cure for Parkinson's has not been obtained, although the pharmaceutical industry has developed therapies that slow down the development of PD, giving patients a minimum quality of life for a more extended period of time [5]. Neurological damage and loss of neurons are triggers in Parkinson's disease, resulting in cognitive dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

et al. 2022

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In this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.

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Decision Support for Medication Change of Parkinson's Disease Patients

Cited by 25 publications

References 17 publications

Extracting decision models for ski injury prediction from data

Extracting decision models for ski injury prediction from data

Znanstveno-raziskovalni izzivi na poti digitalne preobrazbe

Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm

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