The democratic aspect of machine learning: Limitations and opportunities for Parkinson's disease

Bonanni, Laura

doi:10.1002/mds.27600

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Machine learning techniques may provide opportunities to identify individuals during prodromal PD [ 48 ], as well as to predict disease progression [ 49 ]. In prior work, we successfully implemented a machine learning approach to detect PD autonomic features prior to diagnosis using a single lead of a standard 10-s 12-lead electrocardiogram [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables

Karabayir

Butler

Goldman

et al. 2022

JPD

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Background: Parkinson’s disease (PD) is a chronic, disabling neurodegenerative disorder. Objective: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. Methods: Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995–2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density. Results: The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76–0.89) or 5 years (AUC 0.77, 95%CI 0.71–0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = –0.57, p < 0.01). Conclusion: Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.

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Section: Discussionmentioning

confidence: 99%

Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables

Karabayir

Butler

Goldman

et al. 2022

JPD

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“…Traditionally, neuroscientists have often used superficial models, for example, support vector machine (SVM), as classifiers for the identification of emotional states (e.g., SAD) and for performing certain analyses [8]. However, when confronted with challenging classification tasks, these superficial models have some restrictions on understanding the intrinsic features of training data [9]. Recently, in many fields, the DL approach has been used widely to recognize features and effectively identify different types of data.…”

Section: Introductionmentioning

confidence: 99%

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

et al. 2021

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Neuroimaging investigations have proven that social anxiety disorder (SAD) is associated with aberrations in the connectivity of human brain functions.The assessment of the effective connectivity (EC) of the brain and its impact on the detection and medication of neurodegenerative pathophysiology is hence a crucial concern that needs to be addressed. Nevertheless, there are no clinically certain diagnostic biomarkers that can be linked to SAD. Therefore, investigating neural connectivity biomarkers of SAD based on deep learning models (DL) has a promising approach with its recent underlined potential results. In this study, an electroencephalography (EEG)-based detection model for SAD is constructed through directed causal influences combined with a deep convolutional neural network (CNN) and the long shortterm memory (LSTM). The EEG data were classified by applying three different DL models, namely, CNN, LSTM, and CNN+LSTM to discriminate the severity of SAD (severe, moderate, mild) and healthy controls (HC) at different frequency bands (delta, theta, alpha, low beta, and high beta) in the default mode network (DMN) under resting-state condition. The DL model uses the EC features as input, which are derived from the cortical correlation within different EEG rhythms for certain cortical areas that are more susceptible to SAD. Experimental results revealed that the proposed model (CNN+LSTM) outperforms the other models in SAD recognition. For our dataset, the highest recognition accuracies of 92.86%, 92.86%, 96.43%, and 89.29%, specificities of 95.24%, 95.24%, 100%, and 90.91%, and sensitivities of 85.71%, 85.71%, 87.50%, and 83.33% were achieved by using CNN+LSTM model for severe, moderate, mild, and HC, respectively. The fundamental contribution of this analysis is the characterization of neural brain features using different DL models to categorize the severity of SAD, which can represent a potential biomarker for SAD.INDEX TERMS Effective connectivity network, convolutional neural networks (CNNs), social anxiety disorder (SAD), default mode network (DMN), deep learning models, partial directed coherence (PDC), Electroencephalogram (EEG), human brain mapping.

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Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram

Zhang

Gao

et al. 2021

Eur Radiol

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The democratic aspect of machine learning: Limitations and opportunities for Parkinson's disease

Cited by 8 publications

References 15 publications

Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables

Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables

Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity

Identifying Parkinson’s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram

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