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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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11 results for “AKI sub-phenotypes”

Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networkscs.LG

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.

AKI sub-phenotypes Electronic Health Records Memory Networks Deep Learning Heterogeneous AKI

2019-04-10abs ↗pdf ↗

Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performancecs.LG

Study uses LCA to identify ARDS sub-phenotypes improving predictive models.

problem Complex and heterogeneous nature of ARDS makes early recognition difficult.

method Applied latent class analysis to identify sub-groups, then built predictive models.

result Significantly improved prediction performance for two sub-phenotypes of ARDS.

ARDS Latent Class Analysis Machine Learning Predictive Models Sub-phenotypes

2019-03-28abs ↗pdf ↗

Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Datacs.LG

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.

AKI prediction CNN Electronic Health Record Early detection ICU

2020-05-27abs ↗pdf ↗

Continual Prediction from EHR Data for Inpatient Acute Kidney Injuryq-bio.QM

Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. In order to optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which AKI is continually predicted automatically from EHR data over the entire hospit…

2019-02-26abs ↗pdf ↗

Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forestsstat.AP

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.

Deep Rule Forests AKI drug interactions disease interactions prediction accuracy

2020-07-04abs ↗pdf ↗

Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notescs.LG

Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate…

2018-11-07abs ↗pdf ↗

Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analyticscs.CY

Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models are limited in their generalizability and do not fully utilize the physiological intraoperative time-series data. Thus,…

2018-05-11abs ↗pdf ↗

Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Datacs.LG

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing studies consider all patients and therefore restrict to features available in the…

2018-07-25abs ↗pdf ↗

Modelling EHR timeseries by restricting feature interactioncs.LG

Model predicts patient outcomes from EHR data by limiting feature interactions.

problem Predicting patient outcomes from noisy, missing EHR data.

method Recurrent neural network with feature interaction restriction.

result Improvement in AU-ROC for mortality prediction (1.1% improvement, p<0.01).

Electronic Health Records Recurrent Neural Networks Feature Interaction Predictive Modeling Clinical State

2019-11-14abs ↗pdf ↗

Enhancing Phenotype Discovery in Electronic Health Records through Prior Knowledge-Guided Unsupervised Learningstat.AP

Bayesian model enhances phenotype discovery in asthma EHRs.

problem Lack of interpretability in unsupervised learning phenotyping of EHR data.

method Operationalized a Bayesian latent class framework with clinical knowledge priors.

result Identified an asthma sub-phenotype with elevated eosinophil levels and allergy markers.

phenotype discovery electronic health records unsupervised learning Bayesian latent class T2 inflammation

2025-11-03abs ↗pdf ↗

A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Carecs.LG

In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. W…

2019-01-14abs ↗pdf ↗