Unified deep learning predicts Parkinson's disease from medical images.
problem Diagnosing Parkinson's disease accurately from medical images.
method Transfer learning and domain adaptation using deep convolutional and recurrent neural networks.
result The approach effectively predicts Parkinson's disease across different medical environments.
Paper uses TDA for automated Parkinson's disease classification and severity assessment.
problem Manual diagnosis of neurological diseases is time-consuming and inaccurate.
method Combines Topological Data Analysis (TDA) with machine learning on postural shift data.
result Proposes a stable and accurate method for classifying Parkinson's disease.
Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from…
Comparative statistical properties of Parkinson, Garman-Klass, Roger-Satchell and bridge oscillation estimators are discussed. Point and interval estimations, related with mentioned estimators are considered
Study uses GMM-UBM and i-vectors to assess Parkinson's patients via speech, handwriting, and gait.
problem Assessing neurological state of Parkinson's disease patients using speech, handwriting, and gait signals.
method GMM-UBM and i-vectors applied to speech, handwriting, and gait signals.
result Different feature sets from each signal are crucial for assessing Parkinson's patients.
This study evaluates handwriting features to diagnose Parkinson's disease.
problem Diagnosing Parkinson's disease through handwriting analysis.
method Kinematic, geometrical, and non-linear features were evaluated using K-nearest neighbors, support vector machines, and random forest classifiers.
result Up to 93.1% accuracy in classifying Parkinson's disease and healthy subjects.
Paper presents a light-weight CNN model for Parkinson's disease diagnosis.
problem Diagnosing Parkinson's disease efficiently and accurately.
method A light-weight CNN model cascading to LSTM for time-series signals.
result The model achieves high-quality results with fewer parameters and operations.
Study compares neural and statistical models for Parkinson's disease progression from voice data.
problem Difficult statistical analysis of longitudinal voice biomarkers due to subject correlation, small cohorts, and varied disease trajectories.
method Evaluated Neural Mixed Effects (NME), Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs).
result GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure.
Paper classifies Parkinson's disease from speech in three languages using CNNs and transfer learning.
problem Classifying Parkinson's disease from speech in multiple languages.
method Convolutional Neural Networks (CNNs) with transfer learning among Spanish, German, and Czech.
result Transfer learning improves model accuracy by up to 8% and balances specificity-sensitivity.
Web-based framework detects Parkinson's disease from speech recordings.
problem Early detection of Parkinson's disease from speech recordings.
method Collected data from 726 participants, extracted acoustic and deep learning features, trained machine learning models.
result Achieved 0.75 AUC performance in detecting Parkinson's disease.
Deep neural network classifies DaTscan SPECT images for Parkinson's Disease.
problem Early diagnosis of Parkinson's Disease through objective analysis of SPECT images.
method InceptionV3 architecture with custom binary classifier, 10-fold cross validation.
result Deep neural network achieves high accuracy in classifying DaTscan SPECT images.
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means cluster…
Pre-trained model from healthy ADLs improves gait pattern classification for Parkinson's disease.
problem Limited training data for deep learning models in healthcare applications.
method Used convolutional autoencoder to extract features from healthy ADLs data and trained a multi-layer perceptron model for Parkinson's disease classification.
result Features extracted from healthy ADLs data can be used to train an effective classification model for Parkinson's disease.
DC-SIS selects features faster than mRMR for Parkinson's vocal diagnosis.
problem Feature selection for Parkinson's disease vocal data.
method DC-SIS (Distance Correlation Sure Independence Screening) using distance correlation measure.
result 90 times faster feature selection with similar accuracy.
Deep RNN detects FoG episodes in Parkinson's disease with high accuracy.
problem Detecting freezing episodes in Parkinson's disease patients.
method Deep Recurrent Neural Network (RNN) with Long Short-Term Memory cells on 3D-accelerometer measurements.
result Frequency domain features from trunk sensor achieve an AUC score of 93% in subject-independent method.
Machine learning classifies Parkinson's Disease stages from walker sensors data.
problem Limited cost-effective methods for quantitatively assessing Parkinson's Disease stages.
method Machine learning applied to walker-mounted sensors data, feature selection methods compared.
result Feature selection method using ANOVA provides similar accuracy to full feature set and is clinically interpretable.
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
problem Subjectivity in traditional PD diagnosis methods and missed early symptoms.
method Machine learning applied to various data modalities for PD and control group classification.
result Machine learning methods show high potential for improving PD diagnosis.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.
The assessment of Parkinson's disease (PD) poses a significant challenge as it is influenced by various factors which lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, w…
One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from…
CASCADE improves uncertainty communication in Parkinson's disease medication management.
problem Uncertainty in clinical decision-making for Parkinson's disease patients.
method CASCADE uses a novel conformal prediction framework to adaptively scale prediction intervals based on classification uncertainty.
result CASCADE produces more efficient and robust prediction intervals for Parkinson's disease patients.
A new reinforcement learning framework for multi-reward processing.
problem Understanding and modeling multi-reward interactions in complex systems.
method Two-stream reward processing with biological associations.
result Agents can react differently to different types of rewards.
Tutorial on interpreting SPECT images for PD recognition using AI.
problem Difficulty in interpreting complex DL models for clinical use.
method Evaluation of six interpretation methods on four DCNN architectures.
result Guided backpropagation and SHAP methods are suitable for PD recognition.
With the arrival of the big data era, more and more data are becoming readily available in various real-world applications and those data are usually highly heterogeneous. Taking computational medicine as an example, we have both Electronic Health Records (EHR) and medical images for each patient. For complicated disea…
We search for digital biomarkers from Parkinson's Disease by observing approximate repetitive patterns matching hypothesized step and stride periodic cycles. These observations were modeled as a cycle of hidden states with randomness allowing deviation from a canonical pattern of transitions and emissions, under the hy…
Model learns low-dimensional representation from heterogeneous data with missing values.
problem Handling high-dimensional, noisy, and missing data from clinical records.
method Latent Gaussian process with composite likelihoods and numerical quadrature.
result Improves upon existing GPLVM methods for heterogeneous data.
We present a theory of homogeneous volatility bridge estimators for log-price stochastic processes. The main tool of our theory is the parsimonious encoding of the information contained in the open, high and low prices of incomplete bridge, corresponding to given log-price stochastic process, and in its close value, fo…
Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this pape…
A model learns symptom-drug relations for PD patients.
problem Automatic prescription recommendation for Parkinson's Disease patients.
method Builds a dataset of PD symptoms and prescriptions, learns latent symptom space, uses alternating optimization.
result Effective in recommending suitable prescription drugs for new PD patients.
Motion Code models time series dynamics with sparse approximations.
problem Challenges in time series classification and forecasting on noisy data.
method Motion Code views time series as stochastic processes, assigning unique signatures to distinct dynamics.
result Motion Code outperforms benchmarks in noisy datasets, including real-world Parkinson's disease tracking.
Imbalance-XGBoost combines XGBoost with weighted and focal losses for binary imbalanced classification.
problem Binary label-imbalanced classification tasks.
method Combines XGBoost with weighted and focal losses.
result Empirical evaluations show state-of-the-art performance on Parkinson's disease classification.
Deep learning model improves medical diagnosis and reduces patient time.
problem Ineffective and slow methods for synthesizing multimodal medical data.
method Cross-modal deep learning architecture with co-attention mechanism.
result Model outperforms previous methods by 2.35% and is 53% more efficient.
1-D CNNs classify pupil size variations in scotopic conditions.
problem Handling wide inter-subjects variability in pupil analysis.
method 1-D Convolutional Neural Networks applied directly to raw pupil size data.
result 1-D CNNs provide high accuracy in classifying short-range pupil size sequences.
Paper uses deep learning to make predictions transparently.
problem Lack of transparency in DNN predictions.
method Extracts latent variables from trained DNNs for unified analysis.
result Improved prediction accuracy and transparency.
In this paper we introduce a new feature selection algorithm to remove the irrelevant or redundant features in the data sets. In this algorithm the importance of a feature is based on its fitting to the Catastrophe model. Akaike information crite- rion value is used for ranking the features in the data set. The propose…
Early and accurate identification of parkinsonian syndromes (PS) involving presynaptic degeneration from non-degenerative variants such as Scans Without Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for effective patient management as the course, therapy and prognosis differ substantially …
Recordings of electrical brain activity carry information about a person's cognitive health. For recording EEG signals, a very common setting is for a subject to be at rest with its eyes closed. Analysis of these recordings often involve a dimensionality reduction step in which electrodes are grouped into 10 or more re…
Study uses machine learning to identify posturographic features in Parkinson's patients who fall.
problem Identifying patients at risk of falling in Parkinson's syndrome.
method Employed ts-AUC, a non-parametric multivariate two-sample test, to analyze posturographic features.
result Significant differences in antero-posterior movements and posturographic area between fallers and non-fallers identified.
Selecting important features in non-linear or kernel spaces is a difficult challenge in both classification and regression problems. When many of the features are irrelevant, kernel methods such as the support vector machine and kernel ridge regression can sometimes perform poorly. We propose weighting the features wit…
We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.
problem Noise and interval censoring hinder clustering in disease phenotyping.
method Deep generative, continuous-time model that clusters time-series data while correcting for censorship.
result Our model corrects for interval censoring and recovers known clinical subtypes.
This paper explores the impact of metric choice on Fréchet regression.
problem Choosing the right metric for Fréchet regression in complex data.
method Review and extensive numerical studies of existing dimension reduction methods.
result Different metrics significantly affect the estimation of central and central mean space.
Framework combines HMM and MTGCN for spatiotemporal causal inference in clinical data.
problem Challenges in observing direct treatment effects in clinical domains.
method Integrates Hidden Markov Model and Multi Task and Multi Graph Convolutional Network for spatiotemporal data.
result Advances predictive causal inference by structurally adapting to spatiotemporal complexities.
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of obser…
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
This paper evaluates t-SNE and MDS for reducing dimensions in datasets and classifying them with KNN, ENN, and SVM.
problem Reducing dimensions in datasets for better classification performance.
method t-SNE and MDS applied to nine datasets, followed by KNN, ENN, and SVM classification.
result Performance comparison of t-SNE and MDS with KNN, ENN, and SVM.
This study analyzes LTS in sparse models with finite sample error bounds.
problem Robust regression in high-dimensional sparse models with limited data.
method Non-asymptotic analysis of LTS error bounds.
result Established finite sample error bounds for LTS in sparse models.
Semi-pessimistic RL tackles distributional shift and data scarcity in offline RL.
problem Distributional shift and scarcity of labeled data in offline RL.
method Proposes a semi-pessimistic RL method that simplifies learning by seeking a lower bound of the reward function.
result Demonstrates clear competitiveness and improved policy learning with vast unlabeled data.
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.