Medical decision-making based on the exploration of a personalized medicine dataset

Kadi, Hafid; Rebbah, Mohammed; Meftah, Boudjelal; Lézoray, Olivier

doi:10.1016/j.imu.2021.100561

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models [ 1 ]. Personalized medicine tailors medical intervention based on patient profiles [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine

Milella

Famiglini

Banfi

et al. 2022

JPM

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The rise of personalized medicine and its remarkable advancements have revealed new requirements for the availability of appropriate medical decision-making models. Computer science is an area that plays an essential role in the field of personalized medicine, where one of the goals is to provide algorithms and tools to extrapolate knowledge and improve the decision-support process. The minimum clinically important difference (MCID) is the smallest change in PROM scores that patients perceive as meaningful. Treatment that does not achieve the minimum level of improvement is considered inappropriate as well as a potential waste of resources. Using the MCID threshold to identify patients who fail to achieve the minimum change in PROM that results in a meaningful outcome may aid in pre-surgical shared decision-making. The decision tree algorithm is a method for extracting valuable information and providing further meaningful information to the domain expert that supports the decision-making. In the present study, different tools based on machine learning were developed. On the one hand, we compared three XGBoost models to predict the non-achievement of the MCID at six months post-operation in the SF-12 physical score. The prediction score threshold was set to 0.75 to provide three decision-making areas on the basis of the high confidence (HC) intervals; the minority class was re-balanced by weighting the positive class to penalize the loss function (XGBoost cost-sensitive), oversampling the minority class (XGBoost with SMOTE), and re-sampling the negative class (XGBoost with undersampling). On the other hand, we modeled the data through a decision tree (assessment tree), based on different complexity levels, to identify the hidden pattern and to provide a new way to understand possible relationships between the gathered features and the several outcomes. The results showed that all the proposed models were effective as binary classifiers, as they showed moderate predictive performance both regarding the minority or positive class (i.e., our targeted patients, those who will not benefit from surgery) and the negative class. The decision tree visualization can be exploited during the patient assessment status to better understand if those patients will benefit or not from the medical intervention. Both of these tools can come in handy for increasing knowledge about the patient’s psychophysical state and for creating an increasingly specialized assessment of the individual patient.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine

Milella

Famiglini

Banfi

et al. 2022

JPM

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show abstract

“…This, in particular, is due to the fact that such framework is both simple and expressive and, accordingly, is perfectly suited for reducing highly complex and inaccessible scientific problems to their absolute core. Modern examples for this phenomenon are ranging from applications in personalized medicine (see, e.g., Kadi et al (2021)), climate sciences (see, e.g., Schmidt et al (2011); Neubersch et al (2014)) or environmental management (see, e.g., Sahlin et al (2021)) to applications in statistics (see, e.g., Cattaneo (2005); Hable (2009); Cattaneo (2013); Schwaferts and Augustin (2021)), classification (see, e.g., Hüllermeier et al (2008); Fürnkranz and Hüllermeier (2010); Hüllermeier and Waegeman (2021)) or even quantum mechanics (see, e.g., Benavoli et al (2017)).…”

Section: Introductionmentioning

confidence: 99%

Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

Jansen¹,

Hannah²,

Augustin³

et al. 2021

Preprint

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In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on the general framework introduced in Jansen, Schollmeyer & Augustin (2018, Int. J. Approx. Reason), we now design elicitation procedures and algorithms that enable decision makers to reveal their underlying preference system (i.e. two relations, one encoding the ordinal, the other the cardinal part of the preferences) while having to answer as few as possible simple ranking questions. Here, two different approaches are followed. The first approach directly utilizes the collected ranking data for obtaining the ordinal part of the preferences, while their cardinal part is constructed implicitly by measuring meta data on the decision maker's consideration times. In contrast, the second approach explicitly elicits also the cardinal part of the decision maker's preference system, however, only an approximate version of it. This approximation is obtained by additionally collecting labels of preference strength during the elicitation procedure. For both approaches, we give conditions under which they produce the decision maker's true preference system and investigate how their efficiency can be improved. For the latter purpose, besides data-free approaches, we also discuss ways for effectively guiding the elicitation procedure if data from previous elicitation rounds is available. Finally, we demonstrate how the proposed elicitation methods can be utilized in problems of decision under (severe) uncertainty. Precisely, we show that under certain conditions optimal decisions can be found without fully specifying the preference system.

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Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

Jansen¹,

Hannah²,

Augustin³

et al. 2022

International Journal of Approximate Reasoning

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Medical decision-making based on the exploration of a personalized medicine dataset

Cited by 3 publications

References 29 publications

Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine

Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine

Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty

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