Early and High‐Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model

Doumari, Sajjad Amiri; Berahmand, Kamal; Ebadi, M. J.

doi:10.1155/2023/1493676

Cited by 9 publications

(3 citation statements)

References 55 publications

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“…Second, our PD risk prediction models were built based on the logistic regression, a commonly used and easily interpretable approach. Further studies are needed to explore the other algorithms, especially machine learning and deep learning models 33 , in modeling the PD risk.…”

Section: Discussionmentioning

confidence: 99%

Polygenic risk score-based prediction for Parkinson’s disease

Wen,

Zhang,

et al. 2023

Preprint

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Parkinson’s disease (PD) is a complex neurodegenerative disorder with unclear etiology and ineffective treatments. Integrating multimodal data for PD prediction remains challenging. We analyzed data obtained from the Parkinson’s Progression Markers Initiative, using polygenic risk scores (PRS) to reflect genetic susceptibility to PD. We compared the prediction accuracy of models with PRS, demographics, clinical assessment, and biomarkers progressively integrated and investigated relationships. The SDPR-based PRS exhibited the highest prediction performance with an AUC of 0.75. Models combining PRS, demographic, and clinical variables achieved an AUC of 0.91, surpassing models without PRS and matching those with biomarkers. PRS correlated with olfactory function and Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), with its influence on PD risk dependent on gender and MDS-UPDRS. Our study illuminates PD etiology and provides a practical risk assessment framework, highlighting its omnigenic architecture, and the potential for accurate prediction using PRS and non-invasive clinical data.

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Section: Discussionmentioning

confidence: 99%

Polygenic risk score-based prediction for Parkinson’s disease

Wen,

Zhang,

et al. 2023

Preprint

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“…In [46], the authors present a novel model, known as the crow-search-algorithm-based decision tree (CSADT), for the early diagnosis of PD. The proposed method was rigorously tested on four distinct PD datasets: meander, spiral, voice, and speech-Sakar.…”

Section: Graph Convolutional Network (Gcns)mentioning

confidence: 99%

Graph Neural Networks for Parkinson’s Disease Monitoring and Alerting

Zafeiropoulos,

Bitilis,

Tsekouras

et al. 2023

Sensors

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Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson’s disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.

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“…[8][9][10]. In recent years, ML models have emerged as effective alternatives, particularly for handling high-dimensional and unnormalized data [11][12][13][14][15][16]. Due to its unique characteristics, such as efficiency, accuracy, and the ability to handle large datasets, LightGBM [17] stands out.…”

Section: Introductionmentioning

confidence: 99%

Prediction of 30-Day Mortality for ICU Patients with Sepsis-3

Yu,

Ashrafi,

et al. 2024

Preprint

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Background There has emerged an increasing demand for advanced methodologies aimed at augmenting our comprehension and prognostication of illnesses. This study is distinctly centered on tackling the complexity of Sepsis, an immediate bodily reaction to infection. Our objective is to refine the early identification and mortality forecasting for patients diagnosed under the Sepsis-3 criteria, with the overarching aim of enhancing the allocation of hospital resources. Methods In this study, we introduced a Machine Learning (ML) framework aimed at predicting the 30-day mortality rate among Intensive Care Unit (ICU) patients diagnosed with Sepsis-3. Leveraging the Medical Information Mart for Intensive Care III (MIMIC-III) database, we systematically identified eligible patients using advanced big data extraction tools such as Snowflake. Additionally, we employed decision tree models to ascertain the importance of various features and conducted entropy analyses across decision nodes to refine feature selection. Collaborating with esteemed clinical experts, we curated a list of 30 relevant features. Moreover, we used the Light Gradient Boosting Machine (LightGBM) model due to its gradient boosting architecture and computational efficiency. Results The study comprised a cohort of 9118 patients diagnosed with Sepsis-3. Through our meticulous preprocessing techniques, we observed a marked enhancement in both the Area Under the Curve (AUC) and accuracy metrics. The LightGBM model yielded an impressive AUC of 0.983, with a 95% confidence interval [0.980-0.990]. Moreover, it exhibited a commendable accuracy of 0.966 and an F1-score of 0.910. Notably, LightGBM showcased a substantial 6% enhancement over our best baseline model and a significant 14% enhancement over the best existing literature. These noteworthy advancements can be attributed to several factors: (I) the incorporation of a novel and pivotal feature in our model, Hospital Length of Stay (HOSP_LOS), which has not been included in previous literature; (II) the inherent strengths of LightGBM's gradient boosting architecture, enabling robust predictions even with high-dimensional data, while maintaining computational efficiency, as evidenced by its learning curve. Conclusions The introduced preprocessing methodology not only led to a substantial reduction in the number of relevant features compared to the best existing literature, thereby alleviating computational complexities, but also enabled the identification of a crucial feature previously ignored in existing literature. Through the integration of these pivotal features and meticulous parameter tuning, our proposed model achieved remarkable predictive power, with its learning curve demonstrating its capacity for generalization to unseen data. This underscores the potential of ML as indispensable tools in the dynamic environment of the ICU. Employing our model stands to streamline resource allocation within ICUs, offering clinicians greater efficiency and tailored interventions for patients afflicted with Sepsis-3. Keywords: Sepsis-3 prediction, Machine Learning, Entropy Analysis, Gradient Boosting Machine Model

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Early and High‐Accuracy Diagnosis of Parkinson’s Disease: Outcomes of a New Model

Cited by 9 publications

References 55 publications

Polygenic risk score-based prediction for Parkinson’s disease

Polygenic risk score-based prediction for Parkinson’s disease

Graph Neural Networks for Parkinson’s Disease Monitoring and Alerting

Prediction of 30-Day Mortality for ICU Patients with Sepsis-3

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