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…
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48 results for “chronic disease prediction”
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.
Study uses machine learning and survival analysis to predict CKD progression.
problem Early detection and management of CKD to reduce ESRD risk.
method Combines machine learning and classical statistical models to identify novel CKD progression predictors.
result Deep learning models outperform other methods in predicting CKD progression.
Machine learning predicts obesity causes using genetic and imaging data.
problem Predicting causes of obesity in children and adults.
method Use ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN on genetic and imaging data.
result ML models accurately predict obesity causes and chronic diseases.
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.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
problem Early detection of CKD in diabetic patients.
method Sparse longitudinal representations of EHR data.
result Proposed model achieves higher predictive performance than baselines.
Deep learning system tracks stool consistency for GI disease assessment.
problem Lack of objective stool consistency measurements in chronic GI disease.
method Computer vision and deep convolutional neural networks (CNN).
result Developed a stool detection and tracking system.
Proposes a new model for predicting chronic conditions over time.
problem Predicting complex relationships between multiple chronic conditions.
method Continuous time Bayesian network with adaptive regularization for structure and parameter learning.
result Proposed model provides sparse, intuitive representation of chronic condition relationships.
This study predicts diabetes complications using financial records and neural networks.
problem Managing chronic diseases like diabetes in patients.
method Used financial records from health plans, applied self-attentive recurrent neural networks.
result Successfully predicted diabetes complications with an AUC of 0.81-0.94, 60-240 days ahead.
Deep learning predicts drug prescriptions across global health records.
problem Predicting drug prescriptions in chronic disease patients.
method Adaptive cross-global attention graph kernel network with support vector machine.
result Model outperforms current methods in accuracy and interpretability.
New fair regression method improves fairness in chronic kidney disease classification.
problem Mitigating societal bias in health care for multiple groups.
method Penalized fair regression framework for multiple groups, with penalties for true positive rate disparity.
result Achieves fairness-accuracy frontier beyond existing methods in simulations and real-world data.
Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov modelscs.LG
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
Simulation framework assesses ROI of chronic disease adherence and policy timing.
problem Uncertainty in ROI of adherence-enhancing interventions under heterogeneous patient behavior and socioeconomic variation.
method Simulation-based framework integrating disease progression, time-varying adherence, and policy timing.
result Early and adaptive interventions yield highest ROI, exceeding 20% under certain conditions.
T-LSTM autoencoder improves chronic kidney disease patient representation.
problem Improving latent representations from irregularly sampled clinical data.
method Time-Aware Long Short-Term Memory Autoencoder.
result Significant improvements in learnt representations on synthetic and real datasets.
DPVis integrates HMMs into visualizations for disease progression analysis.
problem Challenges in interpreting HMMs for disease progression modeling.
method Design study with clinical experts, visualizations of HMM parameters and outcomes.
result DPVis successfully evaluates and summarizes disease progression models.
Study predicts diabetes biomarkers using wearable data.
problem Understanding and predicting diabetes progression.
method Wide and deep neural network with LSTM structure.
result Model predicts biomarkers with low error rates.
A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for obse…
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.
Enhances generative model for clinical data privacy and accuracy.
problem Data privacy in electronic patient records.
method Improves a time-series generative model with privacy safeguards.
result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.
This paper develops a framework for predicting patient workload across multiple VA facilities.
problem Handling demand uncertainty and predicting workload in healthcare facilities, especially chronic disease treatment.
method Developed a heuristic clustering algorithm for single task learning and a multi-task learning approach.
result Demonstrated improved accuracy in workload prediction for patients with similar conditions across multiple facilities.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
Improves tree-based models' interpretability for medical applications.
problem Lack of explainability in tree-based models.
method Developed new algorithms and tools for local and global model understanding.
result Combining local explanations reveals global model structure and identifies non-linear interactions.
Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimesstat.ME
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
New methods improve genetic studies of complex diseases.
problem Improving genetic studies of complex diseases using high-dimensional clinical data.
method Evaluation of unsupervised disentangled representation learning methods (autoencoders, VAE, beta-VAE, FactorVAE) for genetic association studies.
result FactorVAEs and beta-VAEs outperform standard VAEs and non-variational autoencoders in genetic studies of asthma and COPD.
Deep learning predicts ICD codes with high accuracy for patient phenotyping.
problem Variability in ICD code assignment by coders.
method Deep learning model trained on demographics, lab results, and medications.
result Model predictions outperform coder assigned ICD codes in accuracy.
Hidden Markov Models classify cough events with high accuracy.
problem Identifying coughing events in noisy environments for health monitoring.
method Used Hidden Markov Models (HMMs) for cough classification.
result Multivariate HMMs achieved 92% AUR in classifying cough events.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction Systemcs.LG
The study compares Euclidean and cosine distances in medical drug prescription prediction.
problem Comparing Euclidean and cosine distances in medical drug prescription prediction.
method Established geometric properties and compared distances in real-world medical data.
result Different distances lead to different optimizing nonlinear kernel embedding frameworks.
FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.
problem Modeling high-dimensional data with unknown cut points and binary responses.
method Fusion penalized logistic threshold regression (FILTER) model with fused lasso penalty for variable selection.
result Established non-asymptotic error bounds for coefficient estimation and model selection consistency.
Contrasting Global and Patient-Specific Regression Models via a Neural Network Representationstat.ME
Proposes a tool to contrast global vs personalized models in clinical prediction.
problem Balancing global vs personalized models in clinical prediction.
method Localized regression approach using autoencoder for dimension reduction.
result Identification of patient subgroups where global models fall short.
A method models continuous-time glucose distributions in children with diabetes.
problem Capturing subtle temporal changes in glucose distributions.
method Probabilistic framework using Gaussian mixtures and neural ODEs.
result Detects treatment-related improvements in glucose dynamics.
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
Deep learning predicts asthma ED visits better than traditional methods.
problem Predicting asthma-related ED visits to improve patient management.
method Deep learning (Artificial Neural Networks) compared to Lasso logistic regression.
result Deep learning model (ANN) outperforms traditional Lasso logistic regression (AUC = 0.845 vs. AUC = 0.842).
This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexit…
This paper develops explainable treatment policies for RPM using clinical knowledge.
problem Barriers to adoption of DHIs and lack of interpretability in purely black-box algorithms.
method Developed a pipeline for learning explainable treatment policies using clinician-informed representations.
result Policies learned from clinician-informed representations are more efficacious and efficient than black-box policies.
Paper characterizes early-stage dementia signatures from sensor data.
problem Detecting early-stage dementia from sensor data.
method Developed bespoke behavioural models from longitudinal sensor data.
result Found subtle differences in sleep quality and wandering between dementia patients and controls.
Deep learning model diagnoses celiac disease severity from intestinal biopsy images.
problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.
System identifies health risks using semantic and machine learning.
problem Identifying risk factors associated with health conditions in subpopulations.
method Developed a combined semantic and machine learning system using a health risk ontology and knowledge graph.
result Dynamic discovery of risk factors and their subpopulations.
HIP method extended to multi-class, Poisson, and Zero-Inflated Poisson outcomes with an R Shiny app.
problem Subgroup heterogeneity in complex diseases like COPD.
method Integrating multiple data views while accounting for subgroup heterogeneity.
result Identified common and subgroup-specific markers of exacerbation frequency in males and females.
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate. COPD can be quantified by classifying patches of computed tomography images, and combining patch labels into an overall diagnosis for the image. As labeled patches are often not available, image labels are p…
Paper predicts medication non-adherence in cancer patients using ML.
problem Predicting and understanding medication non-adherence in cancer patients.
method Developed ML models to predict non-adherence, fine-tuned by oncologists.
result Improved support for cancer patients through ML risk scores.
CNNs accurately measure airways and vessels on CT images, improving lung disease diagnosis.
problem Accurately characterizing small pulmonary structures from CT images for disease diagnosis.
method Generative model combined with Convolutional Neural Regressor (CNR) for cross-sectional measurements.
result CNNs provide accurate measurements with physiological correlates.
A hybrid deep learning model improves ESD diagnosis accuracy.
problem Automated diagnosis of Erythemato-Squamous Disease (ESD) is challenging.
method Proposes Derm2Vec, a hybrid model combining Autoencoders and Deep Neural Networks.
result Derm2Vec outperforms other methods in real-world dermatology dataset.
Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Densityq-bio.QM
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A panel of expert physicians collaborated to specify the qualitative part…
Deep learning improves CVD risk prediction from health records.
problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.
A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in st…
In this work we investigate intra-day patterns of activity on a population of 7,261 users of mobile health wearable devices and apps. We show that: (1) using intra-day step and sleep data recorded from passive trackers significantly improves classification performance on self-reported chronic conditions related to ment…
Longitudinal patient data has the potential to improve clinical risk stratification models for disease. However, chronic diseases that progress slowly over time are often heterogeneous in their clinical presentation. Patients may progress through disease stages at varying rates. This leads to pathophysiological misalig…