Readmission Prediction

9 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

kexinhuang12345/clinicalBERT 10 Apr 2019

Clinical notes contain information about patients that goes beyond structured data like lab values and medications.

Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer

google-health/records-research 11 Jun 2019

A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

A_2/hcup_research 22 Dec 2018

Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0. 642 AUC (95% CI, 0. 640-0. 645).

Multimodal data matters: language model pre-training over structured and unstructured electronic health records

liusc/3-6-liusicen-multi-modal-pretrain 25 Jan 2022

As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain.

Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission

tsy935/readmit-stgnn 14 Apr 2022

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.

Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction

nyuolab/model_sensitivity 13 Nov 2022

We assess the sensitivity score on a set of representative words in the test set using two classifiers trained for hospital readmission classification with similar performance statistics.

Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare

ibm/hspo-ontology 9 May 2023

KGs are used to discover diagnoses or prioritize genes relevant to disease, but they often rely on schemas that are not centred around a node or entity of interest, such as a person.