CNN predicts kidney function from biopsies.
problem Predict future kidney function from biopsies.
method Used Convolutional Neural Networks (CNNs).
result CNNs accurately predict kidney function.
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48 results for “kidney function”
Paper proposes efficient method for automatic renal segmentation in DCE-MRI.
problem Automatic segmentation of renal parenchyma in DCE-MRI images.
method Cascaded application of two 3D CNNs for localization and segmentation.
result Achieved high segmentation accuracy with mean dice coefficients of 91.4 and 83.6 for normal and abnormal kidneys, respectively.
Deep learning predicts kidney graft survival better than traditional methods.
problem Improving survival analysis for kidney graft patients.
method Deep learning method that jointly predicts survival times and ranks.
result Deep learning outperforms Cox Proportional Hazards and other methods.
A novel 3D U-Net approach improves kidney tumor segmentation in medical imaging.
problem Challenging manual annotation and great medical impact of kidney tumor segmentation.
method End-to-end cascaded U-Nets with a localization network.
result Achieves Sørensen-Dice coefficients of 0.902 for kidney and 0.408 for tumor segmentation.
Paper develops a model to predict kidney transplant success.
problem Mismatched deceased donor-recipient kidney leads to post-transplant death.
method Data analysis of 584 imported kidneys from 12 transplant centers.
result Predicting model reduces mortality rate due to kidney mismatch.
KiTS19 dataset offers 300 kidney tumor cases with CT data and outcomes.
problem Difficulty in quantifying kidney tumor morphometry due to data scarcity and manual labor.
method Presented KiTS19 dataset with multi-phase CT imaging, segmentation masks, and clinical outcomes.
result Automated segmentation of kidney tumors is now possible with this dataset.
3D U-Net improves kidney and tumor segmentation from CT scans.
problem Manual segmentation by clinicians is laborious and error-prone.
method Multi-scale supervised 3D U-Net with deep supervision and post-processing.
result MSS U-Net achieves high Dice coefficients (0.969 for kidney, 0.805 for tumor) on KiTS19 dataset.
Study identifies three sub-phenotypes of AKI with different severity.
problem Tackles the heterogeneity of AKI to improve targeted interventions.
method Used a memory network-based deep learning approach on EHR data.
result Identified three distinct sub-phenotypes of AKI with varying severity.
Machine learning improves kidney transplant outcomes prediction.
problem Improving prediction of kidney transplant success.
method Random forest machine learning model trained on kidney donor risk index data.
result Random forest predicted 2,148 more successful transplants than the risk index.
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.
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.
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.
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.
Model predicts disease progression using multiple patient health markers.
problem Predicting disease trajectory in chronic diseases with heterogeneity and multiple biomarkers.
method Probabilistic generative model using Gaussian processes and latent class models.
result Model improves predictions of chronic kidney disease progression compared to state of the art.
Natural language processing predicts AKI onset in ICU patients.
problem Early detection of AKI in ICU patients to improve outcomes.
method Clinical notes were processed to generate word and concept embeddings. Five classifiers and a deep learning model were used to predict AKI.
result The best model achieved an AUC of 0.779 for predicting AKI onset.
Study predicts AKI risk in rehospitalized patients using EHR data.
problem Predicting AKI in rehospitalized patients to improve outcomes.
method Gradient boosting, logistic regression, and recurrent neural network trained on EHR data.
result Developed a risk score for AKI at hospital re-entry.
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.
Proposes a framework for personalized treatment recommendations using observational data.
problem Estimating patient-level treatment effects from observational data.
method Integrates existing methods for learning patient-level causal models.
result Improves patient outcomes in heart failure patients with acute kidney injury.
This study improves AKI prediction precision using CNN on EHR data.
problem Improving early prediction of AKI in ICU patients.
method Convolutional Neural Network (CNN) on EHR data.
result Best AUROC up to 0.988 on MIMIC-III and 0.936 on eICU data sets.
Improves feature selection in high-dimensional data using LLM-generated weights.
problem Inaccurate LLM-generated weights degrade feature selection performance.
method Integrates LLM-generated weights into prior inclusion probabilities using LLM Sparsity Prior (LSP).
result Improves prediction accuracy and identifies clinically relevant features.
New method provides reliable probabilistic bounds for VUR detection.
problem Detect VUR in children without radiation exposure.
method Machine learning with probabilistic bounds for conditional probability.
result Guaranteed bounds contain well-calibrated probabilities.
New method classifies patients with kidney transplant based on many features.
problem Classifying patients with many features (ultrahigh-dimensional data).
method Multivariate screening and classification method leveraging feature correlations.
result Achieves optimal misclassification rates and more powerful discovery.
Study proposes a self-correcting deep learning model for ICU patient condition prediction.
problem Challenging task of continuously monitoring high-dimensional vital signs and lab measurements in critical care.
method Utilized accumulative ICU data, self-correcting mechanism, and regularization method.
result Outperformed conventional deep learning models in predicting acute kidney injury.
Paper presents an ensemble model for predicting readmission using clinical notes.
problem Limited use of clinical notes in predicting readmission due to their unstructured nature.
method Ensemble model combining vector space modeling and topic modeling.
result Improves readmission prediction by 0.0211 in c-statistics.
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.
New CNN architecture improves pediatric image segmentation by homogenizing pose and size.
problem Challenges in segmenting pediatric images due to pose and size heterogeneity.
method Spatial Transformer Network (STN) for pose and scale invariance, combined with UNet for segmentation.
result Improved pediatric segmentation, especially renal tumor delineation, with accelerated processing.
Improved predictive models for AKI using intraoperative data.
problem Limited generalizability of existing AKI risk score models and lack of intraoperative data utilization.
method Machine learning and statistical analysis techniques, incorporating intraoperative physiologic time series data.
result The proposed model improves AKI risk prediction, achieving better AUROC and NRI.
A novel continual prediction model outperforms traditional one-time models in predicting AKI.
problem Optimally predicting AKI before it develops during a hospital stay.
method A novel continual prediction model that predicts AKI every time a patient's AKI-relevant variable changes in the EHR.
result The continual prediction model outperformed traditional one-time models, achieving a higher AUC of 0.724 compared to 0.653.
The paper tackles survival analysis with censored data, proposing methods to incorporate incomplete information into models.
problem Survival analysis with censored data, where the target output is often incomplete.
method The paper explores three categories of loss functions: partial likelihood methods, rank methods, and a classification method based on a Wasserstein metric and Kaplan Meier estimate.
result The proposed method optimizes the expected C-index, a common evaluation metric for ranking survival models.
Bayesian nonparametric method estimates individualized treatment-response curves from observational data.
problem Estimating individualized treatment-response curves from observational time series data.
method Developed a Bayesian nonparametric method using the G-computation formula.
result BNP method provides more accurate estimates of treatment responses than alternative approaches.
Due to advances in sensors, growing large and complex medical image data have the ability to visualize the pathological change in the cellular or even the molecular level or anatomical changes in tissues and organs. As a consequence, the medical images have the potential to enhance diagnosis of disease, prediction of c…
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.
F-GAM improves clinical prediction models for OR outcomes.
problem Limited expressive capability of logistic regression for clinical predictions.
method Factored generalized additive model (F-GAM) that extends GAM with feature interactions.
result F-GAM outperforms other models in AUPRC and AUROC for predicting OR outcomes.
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank studycs.LG
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
Study dynamic matching in heterogeneous networks using ODE model.
problem Dynamic matching in heterogeneous networks with compatibility restrictions.
method Introduced an ODE model to evaluate matching algorithms.
result Demonstrated trade-off between quick matching and optimal matching.
Efficient algorithm for Bayesian networks reduces marginal probability distribution computation.
problem Exact computation of marginal probability distribution is NP-hard for categorical variables in Bayesian networks.
method Divide-and-conquer approach exploiting graphical properties of Bayesian networks.
result Novel algorithm outperforms state-of-the-art methods in classification and cancer subtype identification.
Framework improves CT image segmentation robustness with domain-specific cues.
problem Challenges in CT image segmentation by deep learning models.
method Combines domain-specific preprocessing and augmentation with CNN architectures.
result Framework stabilizes prediction performance on varying CT volumes.
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).
TransformerLSR models longitudinal, recurrent, and survival data jointly.
problem Joint modeling of longitudinal measurements, recurrent events, and survival data with dependencies.
method Transformer-based deep learning framework integrating deep temporal point processes and latent structure representation.
result TransformerLSR effectively models all three components simultaneously, demonstrating necessity and effectiveness through simulations and real-world data.
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.
We consider the problem of packing node-disjoint directed paths in a directed graph. We consider a variant of this problem where each path starts within a fixed subset of root nodes, subject to a given bound on the length of paths. This problem is motivated by the so-called kidney exchange problem, but has potential ot…
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…
Study uses ML to predict cancer patient mortality from FN onset.
problem Predicting mortality in cancer patients with FN to improve survival.
method Multi-domain machine learning models using HCUP data.
result Clinical diagnoses have highest predictive power for FN mortality.
Study classifies pathology reports using TF-IDF features and machine learning.
problem Classifying pathology reports for cancer surveillance and diagnostic workflow.
method Extracted TF-IDF features from pathology reports and classified them using SVM, XGBoost, and Logistic Regression.
result XGBoost achieved 92% accuracy in classifying pathology reports.
Study describes severe dengue ICU patients in Brazil, 2012-2024.
problem Characterize severe dengue ICU patients and identify risk factors.
method Prospective study, descriptive statistics, logistic regression, machine learning.
result Advanced age, comorbidities, leukocytes, and platelets are significant risk factors for complications.
Novel topic modeling approach improves survival prediction for cancer patients.
problem Survival prediction for cancer patients using high-dimensional gene expression data.
method Inspired by topic modeling, a novel methodology using discretized Latent Dirichlet Allocation (dLDA) to derive expressive features from gene expression data.
result Survival estimates are more accurate than standard models, as shown by the Concordance measure.
New neural network models for complex functional data analysis.
problem Complex relations between functional predictors and responses.
method Function-on-Function regression models using neural networks with continuous hidden layers.
result Demonstrated power and flexibility in handling complex functional models.
Signature functions of knotsmath.GT
Knot signature function defined and conditions for its existence are given.
problem Defining and characterizing the signature function of knots.
method Presentation of necessary and sufficient conditions for a function to be a knot signature function.
result Conditions for a function to be the signature function of a knot are established.