VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
Bayesian meta-learning predicts Alzheimer's disease progression.
problem Predicting individual Alzheimer's disease progression from limited data.
method Bayesian meta-learning approach that dynamically predicts disease score distributions.
result Bayesian meta-learner outperforms single-task models and deterministic meta-learners, especially for long-term predictions.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an indivi…
Deep Belief Network predicts lncRNA-disease associations with high accuracy.
problem Accurately identifying lncRNA-disease associations to understand lncRNA functionality and disease mechanism.
method Proposes a DBN-based model using heterogeneous networks and DBN for feature learning.
result Obtained AUC of 0.96 and AUPR of 0.967 on standard dataset.
Paper predicts multiple types of miRNA-disease associations using tensor decomposition.
problem Predicting miRNA-disease associations, especially multi-type ones.
method Represented miRNA-disease-type triplets as a tensor and used Tensor Decomposition methods.
result Tensor Decomposition methods improve a recent baseline by up to 38% in top-1 F1.
This research uses machine learning to identify Alzheimer's disease subtypes and predict progression.
problem Heterogeneity in Alzheimer's disease clinical manifestations and progression rate limit personalized care and treatment planning.
method Unsupervised and supervised machine learning approaches applied to ADNI data.
result Identification of patient subtypes and prediction of disease progression zones.
Graph network predicts circRNA-disease associations using multi-source similarity features.
problem Identifying circRNA-disease associations is challenging and time-consuming.
method Proposes a graph convolution network framework using multi-source similarity information.
result Framework predicts circRNA-disease associations with promising results and outperforms existing methods.
InceptionGCN improves disease prediction on graph data.
problem Improving disease prediction accuracy on graph data.
method Introduced a new spectral domain architecture with inception modules and varying kernel sizes.
result Significantly improved disease prediction results on two datasets.
Bayesian approach models neurodegenerative diseases without clinical labels.
problem Personalized, predictive modeling of neurodegenerative diseases.
method Probabilistic programmed deep kernel learning combining Gaussian processes and neural networks.
result Surpasses deep learning in accuracy and timeliness of predicting neurodegeneration.
PASS model predicts disease progression with both accuracy and interpretability.
problem Balancing accurate disease prediction with clinically interpretable models.
method Phased LSTM units with attention mechanism for non-stationary state dynamics.
result PASS model achieves superior predictive accuracy and interpretable representations.
Deep learning predicts Alzheimer's Disease progression with high accuracy.
problem Predicting multiple aspects of Alzheimer's Disease progression.
method Unsupervised deep learning on 1908 patients' 18-month clinical data.
result Model accurately predicts ADAS-Cog scores and identifies word recall as a predictor.
Graph convolution model uses self-attention to predict diseases from multi-modal data.
problem Predicting diseases from diverse multi-modal data.
method Graph convolution with self-attention layer.
result Significantly outperforms state-of-the-art methods in disease prediction.
Study compares machine learning and process-based models for predicting rice blast disease.
problem Predicting rice blast disease to support rice growers in controlling the disease.
method Compared four models: two process-based (Yoshino and WARM) and two machine learning (M5Rules and RNN).
result Machine learning models outperformed process-based models in predicting rice blast disease.
Improves disease progression prediction using auxiliary surrogate labels and health markers.
problem Challenges in predicting disease progression due to unknown true disease states.
method Integrates hidden Markov model with time-varying discriminative classification model.
result Significant improvement in distinguishing LBD from AD using objective markers.
Deep EHR predicts chronic diseases using medical notes and structured data.
problem Early detection of chronic diseases for better management and resource allocation.
method Proposes a multi-task framework combining free-text medical notes and structured EHR data using deep learning.
result Deep learning models using text outperform models using only structured data, and models with numerical values and negations in text perform best.
BEHRT improves disease prediction in EHRs by 8-10%.
problem Early disease detection in EHRs for better patient outcomes.
method Deep neural sequence transduction model for EHRs.
result BEHRT improves average precision by 8.0-10.8% compared to state-of-the-art models.
Graph Attention Networks predict disease state from single-cell data.
problem Predicting disease state from single-cell data.
method Graph Attention Networks (GAT) for learning from both features and graph structures.
result Achieved 92% accuracy in predicting MS from single-cell data.
The paper introduces Precision Disease Networks (PDN) for predicting medical outcomes.
problem Predicting medical outcomes for patients with diseases.
method Building patient-specific disease networks, clustering, and data visualization.
result PDN improves prediction of patient outcomes compared to standard statistical analysis.
Prediction of the future trajectory of a disease is an important challenge for personalized medicine and population health management. However, many complex chronic diseases exhibit large degrees of heterogeneity, and furthermore there is not always a single readily available biomarker to quantify disease severity. Eve…
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced…
New method uses probabilistic independence to discover disease signatures from medical records.
problem Insufficiently precise diagnosis of clinical disease leading to treatment failures.
method Unsupervised machine learning using probabilistic independence to disentangle disease patterns.
result Inferred 2000 clinical disease signatures from medical records, improving cancer prediction.
The study predicts surgical complications in Crohn's disease patients using machine learning.
problem Predicting surgical complications in Crohn's disease patients.
method Developed a novel algorithm using ensemble machine learning on 29 baseline covariates.
result Proposed pseudo-observation based estimators for evaluating predictive performance.
The paper predicts diseases using both clinical and genomics data.
problem Clinical predictions using genomics data are not common.
method Integrated clinical and genomics datasets, machine learning, Principal Component Analysis for feature selection.
result 73% accuracy in predicting 75 disease classes.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
HMRNN combines HMMs and neural networks for Alzheimer's disease forecasting.
problem Improving disease progression modeling with hidden states not fully known.
method Developed HMRNN combining HMMs and recurrent neural networks.
result HMRNN improves disease forecasting and offers novel clinical interpretation.
New model predicts banana disease risk from climate data.
problem Managing Black Sigatoka disease under climate change.
method Latent Neural ODEs to model infection dynamics.
result Superior generalization performance up to one month ahead.
PHIBP predicts infectious disease outbreaks in sparse data regions.
problem Predicting outbreaks in regions with no historical data.
method Poisson Hierarchical Indian Buffet Process (PHIBP) framework.
result PHIBP provides accurate outbreak predictions and meaningful insights in sparse data settings.
StageNet improves health risk prediction by integrating disease stage information.
problem Improving health risk prediction for patients with chronic conditions.
method StageNet uses a stage-aware LSTM and stage-adaptive convolutional modules to extract and integrate disease stage information.
result StageNet achieves up to 12% higher AUPRC for risk prediction and over 58% higher Calinski-Harabasz score for patient subtyping compared to state-of-the-art models.
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data. The disease progression is modeled as a trajectory on a group of diffeomorphisms in the context of large deformation diffeomorphic metri…
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science. However, there are many missing values in EHRs when provided in matrix form, which is an important is…
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.
DIM shows promise in predicting Alzheimer's progression.
problem Predicting Alzheimer's disease progression from brain imaging.
method Used variants of Deep InfoMax (DIM) for brain imaging analysis.
result DIM outperforms supervised AlexNet and ResNet in Alzheimer's progression prediction.
New method predicts Parkinson's using deep neural network latent info.
problem Medical diagnosis of Parkinson's disease.
method Transfer learning, k-means clustering, k-Nearest Neighbour classification of DNN representations.
result Improved prediction of Parkinson's disease based on MRI and DaT Scan data.
High-throughput machine learning predicts thousands of diagnosis codes with high accuracy.
problem Predicting disease risk for thousands of diagnosis codes at various time points.
method Training machine learning algorithms on EHR data to predict diagnosis risks.
result Achieved AUCs of 0.803 and 0.758 for 1 and 6-month predictions, respectively.
Unified model predicts disease spread using EMD and ensemble learning.
problem Predicting fluctuating disease spread and individual behavior.
method SEIS-A framework, EMD decomposition, ensemble learning, on-line query data.
result The method outperforms other methods in predicting HFMD consultation rates.
Identifies patient-specific root causes of disease using structural equation models.
problem Detecting significant variables in complex diseases that differ between patients.
method Defining patient-specific root causes as exogenous errors in a structural equation model, quantifying predictivity using Shapley values, and developing a fast algorithm called Root Causal Inference.
result Significant improvements in accuracy by uncovering root causes with large effect sizes at the individual level but clinically insignificant effect sizes at the group level.
Enhances disease progression modeling using LLMs for complex brain connectivity.
problem Inaccurate predictions of disease spread due to oversimplified brain connectivity models.
method Uses LLMs to synthesize multi-modal relationships and learn disease trajectories from longitudinal data.
result Superior prediction accuracy and interpretability compared to traditional methods.
Semi-supervised GAN creates synthetic genetic data for disease prediction.
problem Expensive and time-consuming to build large labeled genetic databases.
method Semi-supervised Genetic Generative Adversarial Network (gGAN).
result Model achieved satisfactory results with real genetic data.
VEGN uses graph neural networks to predict disease-causing mutations from genetic variants.
problem Identifying disease-causing mutations from millions of genetic variants.
method VEGN employs a graph neural network on a heterogeneous graph of genes and variants, learning gene-gene interactions.
result VEGN outperforms existing state-of-the-art models in variant effect prediction.
New method predicts AD progression using MEG brain networks.
problem Early diagnosis and prediction of Alzheimer's disease progression.
method MG2G, a deep learning method that maps brain networks into a latent space.
result MG2G detects subtle brain connectivity patterns and predicts AD progression.
A novel method predicts shape development using Riemannian shape spaces.
problem Predicting future shape development from a single observation.
method Proposes a novel prediction method that encodes shapes in a Riemannian shape space and learns hierarchical statistical models.
result Outperforms deep learning-supported variants and state-of-the-art methods in predicting shape development.
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.
Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)to select genetic and phenotypical markers for disease diagnosis and ii) to identify associations between genetic and phenotypical data. Thes…
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.
Deep Rule Forests identifies drug-drug and drug-disease interactions causing AKI.
problem Identifying drug-drug and drug-disease interactions leading to AKI.
method Deep Rule Forests (DRF) algorithm discovering rules from multilayer tree models.
result DRF model outperforms other algorithms in prediction accuracy and interpretability.
MATCH-Net uses CNNs to predict disease trajectories accurately.
problem Inaccurate prediction of disease trajectories in survival analysis.
method Developed a Missingness-Aware Temporal Convolutional Hitting-time Network (MATCH-Net).
result Demonstrated state-of-the-art performance in real-world Alzheimer's data.