Predictive Modeling for Decision Support in the Tasks of Selecting the Drug for Obesity Treatment

Zhukova, Irina V.; Derevitskii, Ilia V.; Matveev, Georgy A.; Голикова, Т. И.; Babenko, A. Yu.

doi:10.1016/j.procs.2021.10.038

Cited by 2 publications

(2 citation statements)

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“…It was found that recent papers can be classified into three types of databases, namely clinical [13], [18], [27], [33], registry [10], [12], [20], [24], [25], [29], [34], and knowledge [14], [15], [16], [17], [19], [21], [22], [23], [26], [28], [30], [31], [32], [35]. Additionally, based on review papers of existing XAI models [48], [49], the XAI algorithms used were divided into the following four categories: surrogate The main advantage of an explanatory technique such as SHAP is that it has solid roots in game theory, which ensures that the explanation of a prediction instance is fairly distributed across the features.…”

Section: ) Xai Methodsmentioning

confidence: 99%

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Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

et al. 2023

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Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.

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Section: ) Xai Methodsmentioning

confidence: 99%

“…XAI has been studied for various purposes, such as pharmacodynamics and safety [32], deducing effective properties in the same context [9], drug repurposing [19], and drug classification [13], [28], [33].…”

Section: ) Extra Tasks: Drug Repurposing Etcmentioning

confidence: 99%

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

et al. 2023

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Model for Predicting the Effect of Sibutramine Therapy in Obesity

Danilov,

Matveev,

Babenko

et al. 2024

JPM

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Background: The development of models predicting response to weight loss therapy using sibutramine is found in only a few cases. The objective of the work is to develop a data-driven method of personalized recommendation for obesity treatment that would predict the response to sibutramine based on the current set of patient parameters. Methods: The decision system is built on the XGBoost classification algorithm along with recursive feature selection and Shapley data valuation. Using the results of clinical trials, it was trained to estimate the probability of overcoming a weight loss threshold. The model was evaluated by the accuracy metric using the Leave-One-Out cross-validation. Results: The model for predicting response to sibutramine treatment over 3 months has an accuracy of 71%. The model for predicting outcomes at the sixth month visit based on results at 3 months has an accuracy of 80%. Conclusions: Although our developed prediction model may not exhibit high precision compared to certain benchmarks, it significantly outperforms random chance or models relying only on BMI parameters. Our model used the available range of laboratory tests, which makes it possible to use this model for routine clinical use and help doctors decide whether to prescribe sibutramine.

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Predictive Modeling for Decision Support in the Tasks of Selecting the Drug for Obesity Treatment

Cited by 2 publications

References 14 publications

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

Model for Predicting the Effect of Sibutramine Therapy in Obesity

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