Papers Readmission Prediction
“Readmission Prediction” 태그가 달린 논문 38편 · 필터 해제
ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling
The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based ar…
Mortality PredictionPredictionReadmission PredictionPT: A Plain Transformer is Good Hospital Readmission Predictor
Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-discharge. High readmission rates often indicate inadequate treatment or pos…
feature selectionPredictionReadmission PredictionPredicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model
Readmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. Our study analyzes Medicare hospital readmissio…
Feature EngineeringReadmission PredictionEvaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies
Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standardization or sparseness of the data. We previously introduced a person-c…
Knowledge Graph EmbeddingsKnowledge GraphsPerson-Centric Knowledge GraphsReadmission Prediction+1Zero Shot Health Trajectory Prediction Using Transformer
Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), …
ICU AdmissionICU MortalityMortality PredictionReadmission PredictionClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks
Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility in non-generative clinical prediction, often presumed inferior to specialized models, remains under-evaluated, leading to ongoing…
BenchmarkingModel SelectionReadmission PredictionSentence+1Static and multivariate-temporal attentive fusion transformer for readmission risk prediction
Background: Accurate short-term readmission prediction of ICU patients is significant in improving the efficiency of resource assignment by assisting physicians in making discharge decisions. Clinically, both individual …
PredictionReadmission PredictionLarge Language Models for Integrating Social Determinant of Health Data: A Case Study on Heart Failure 30-Day Readmission Prediction
Social determinants of health (SDOH) $-$ the myriad of circumstances in which people live, grow, and age $-$ play an important role in health outcomes. However, existing outcome prediction models often only use proxies o…
Readmission PredictionEnhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts
Hospital readmission, defined as patients being re-hospitalized shortly after discharge, is a critical concern as it impacts patient outcomes and healthcare costs. Identifying patients at risk of readmission allows for t…
Deep LearningReadmission PredictionGeneralization in Healthcare AI: Evaluation of a Clinical Large Language Model
Advances in large language models (LLMs) provide new opportunities in healthcare for improved patient care, clinical decision-making, and enhancement of physician and administrator workflows. However, the potential of th…
DescriptiveLanguage ModelingLanguage ModellingLarge Language Model+1MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system. Previous studies have extensively u…
PredictionReadmission PredictionAn Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
With the increasing availability of patients' data, modern medicine is shifting towards prospective healthcare. Electronic health records contain a variety of information useful for clinical patient description and can b…
Readmission PredictionWord EmbeddingsExplainable Machine Learning for ICU Readmission Prediction
The intensive care unit (ICU) comprises a complex hospital environment, where decisions made by clinicians have a high level of risk for the patients' lives. A comprehensive care pathway must then be followed to reduce p…
Decision MakingPredictionReadmission PredictionCPLLM: Clinical Prediction with Large Language Models
We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for clinical disease and readmission prediction. We utilized quantization and…
Disease PredictionLanguage ModelingLanguage ModellingLarge Language Model+3Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation
A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare doma…
Readmission PredictionTime SeriesTime Series AnalysisRepresentation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare
Knowledge graphs (KGs) are a popular way to organise information based on ontologies or schemas and have been used across a variety of scenarios from search to recommendation. Despite advances in KGs, representing knowle…
Knowledge GraphsPerson-Centric Knowledge GraphsReadmission PredictionRepresentation LearningLanguage Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction
Traditional evaluation metrics for classification in natural language processing such as accuracy and area under the curve fail to differentiate between models with different predictive behaviors despite their similar pe…
Decision MakingLanguage ModelingLanguage ModellingReadmission Prediction+1When BERT Fails -- The Limits of EHR Classification
Transformers are powerful text representation learners, useful for all kinds of clinical decision support tasks. Although they outperform baselines on readmission prediction, they are not infallible. Here, we look into o…
ClassificationReadmission PredictionDistillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML ne…
Readmission PredictionTransfer LearningMultimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged. Wh…
AllGraph Neural NetworkReadmission Prediction