Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder

Niklason, Gregory R.; Rawls, Eric; Ma, Sisi; Kummerfeld, Erich; Maxwell, Andrea M.; Brucar, Leyla R.; Drossel, Gunner; Zilverstand, Anna

doi:10.1038/s41598-022-19804-2

Cited by 8 publications

(3 citation statements)

References 104 publications

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“…XGBoost is an integrated decision tree-based learning model with powerful predictive capabilities [41]. It has a larger delayed pruning penalty compared to traditional gradient boosting decision trees, which makes the model less prone to overfitting [42]. Our study fills the gap in the use of XGBoost models for predicting time series data of influenza cases and provides a more accurate method for predicting influenza cases in Fuzhou.…”

Section: Major Research Findingsmentioning

confidence: 96%

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Chen,

Zheng,

Shi

et al. 2024

BMC Public Health

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Background Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

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Section: Major Research Findingsmentioning

confidence: 96%

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Chen,

Zheng,

Shi

et al. 2024

BMC Public Health

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“…Machine-learning has found steadily increasing applications in the addiction literature. Supervised ML methods have been used to predict adolescent alcohol use (102) and misuse (103), distinguish between smokers and non-smokers (104)(105)(106), between people with and without cocaine use disorder (107,108) or cannabis use disorder (109)(110)(111), and between people with different types of SUD (107,(112)(113)(114)(115)(116). These ML studies have identified multivariate neurobiological, neurocognitive, psychiatric, and personality profiles that differentiate addictions to different classes of drugs.…”

Section: Data-driven Approaches / Machine Learningmentioning

confidence: 99%

Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders

et al. 2023

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Section: Data-driven Approaches / Machine Learningmentioning

confidence: 99%

Utility of computational approaches for precision psychiatry: Applications to substance use disorders

Vassileva¹,

Lee²,

Psederska³

et al. 2022

Preprint

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Revolutionary advances in neuroscience and genetics over the past two decades have provided unprecedented opportunities for increasing our understanding of the etiology and pathogenesis of psychiatric disorders. Despite these advances, the translation of this knowledge into clinical practice has been hindered by the significant heterogeneity within disorders and the neurobiologically-imprecise categorization of patients. Traditional diagnostic categories do not capture the underlying neurobiological processes and etiological mechanisms of psychiatric disorders and cannot adequately inform prognosis and treatment. To address this gap, the National Institute on Mental Health (NIMH) has proposed an alternative research framework based on Research Domain Criteria (RDoC), which yields psychiatric classification grounded on discoveries from neuroscience and genomics. Recently, the RDoC approach has been adapted for the study of addictions with the Addictions Neuroclinical Assessment (ANA) framework (1), which proposes that the assessment of addictions should cover multiple systems and focus on three key neurofunctional domains: Executive Function, Incentive Salience, and Negative Emotionality. Building upon the aims of the RDoC framework, a new field of “precision psychiatry” has emerged, considered to be a paradigm shift in psychiatry. Both the RDoC framework and precision psychiatry are predicated on precise measurement, which has necessitated the development of novel analytic approaches for classification and novel dimensional tools for phenotyping that can identify the unique mechanisms of psychiatric disorders at an individual level. In this chapter, we make the case that theory-driven and data-driven computational approaches have enormous potential for increasing the precision and reliability of measurement, and the accuracy of diagnosis and prognosis in psychiatry. We review three types of computational approaches and their utility for precision psychiatry: (1) Theory-driven approaches, such as computational modeling; (2) Data-driven approaches, using various machine learning methods; and (3) Hybrid approaches, such as joint modeling and adaptive design optimization. We focus more narrowly on the application of these approaches to substance use disorders (SUD), where we attempt to map them on the current RDoC framework for addictions.

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Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder

Cited by 8 publications

References 104 publications

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders

Utility of computational approaches for precision psychiatry: Applications to substance use disorders

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