Journal of healthcare informatics research最新文献

CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks. CliqueFluxNet：利用图神经网络的随机边缘流动和最大簇利用揭示电子病历洞察力

IF 5.4

Journal of healthcare informatics research Pub Date : 2024-08-01 eCollection Date: 2024-09-01 DOI: 10.1007/s41666-024-00169-2

Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O Ghosheh, Anshul Thakur, Vinod Kumar Chauhan, Tingting Zhu, David A Clifton

{"title":"CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks.","authors":"Soheila Molaei, Nima Ghanbari Bousejin, Ghadeer O Ghosheh, Anshul Thakur, Vinod Kumar Chauhan, Tingting Zhu, David A Clifton","doi":"10.1007/s41666-024-00169-2","DOIUrl":"10.1007/s41666-024-00169-2","url":null,"abstract":"<p><p>Electronic Health Records (EHRs) play a crucial role in shaping predictive are models, yet they encounter challenges such as significant data gaps and class imbalances. Traditional Graph Neural Network (GNN) approaches have limitations in fully leveraging neighbourhood data or demanding intensive computational requirements for regularisation. To address this challenge, we introduce CliqueFluxNet, a novel framework that innovatively constructs a patient similarity graph to maximise cliques, thereby highlighting strong inter-patient connections. At the heart of CliqueFluxNet lies its stochastic edge fluxing strategy - a dynamic process involving random edge addition and removal during training. This strategy aims to enhance the model's generalisability and mitigate overfitting. Our empirical analysis, conducted on MIMIC-III and eICU datasets, focuses on the tasks of mortality and readmission prediction. It demonstrates significant progress in representation learning, particularly in scenarios with limited data availability. Qualitative assessments further underscore CliqueFluxNet's effectiveness in extracting meaningful EHR representations, solidifying its potential for advancing GNN applications in healthcare analytics.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompt Tuning in Biomedical Relation Extraction 生物医学关系提取中的及时调整

Journal of healthcare informatics research Pub Date : 2024-02-29 DOI: 10.1007/s41666-024-00162-9

JianPing He, Fang Li, Jianfu Li, Xinyue Hu, Yi Nian, Yang Xiang, Jingqi Wang, Qiang Wei, Yiming Li, Hua Xu, Cui Tao

引用次数: 0

A Systematic Review on Pill and Medication Dispensers from a Human-Centered Perspective 从 "以人为本 "的角度对药丸和药物分配器进行系统回顾

Journal of healthcare informatics research Pub Date : 2024-02-22 DOI: 10.1007/s41666-024-00161-w

L. Gargioni, D. Fogli, Pietro Baroni

引用次数: 0

NeighBERT: Medical Entity Linking Using Relation-Induced Dense Retrieval NeighBERT：利用关系诱导密集检索进行医学实体链接

Journal of healthcare informatics research Pub Date : 2024-01-18 DOI: 10.1007/s41666-023-00136-3

Ayush Singh, Saranya Krishnamoorthy, John E. Ortega

引用次数: 2

Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data 从时态个人健康数据生成自然语言摘要的神经模型

Journal of healthcare informatics research Pub Date : 2024-01-16 DOI: 10.1007/s41666-023-00158-x

Jon Harris, Mohammed J. Zaki

引用次数: 0

Identifying and Extracting Rare Diseases and Their Phenotypes with Large Language Models 利用大型语言模型识别和提取罕见疾病及其表型

Journal of healthcare informatics research Pub Date : 2024-01-05 DOI: 10.1007/s41666-023-00155-0

Cathy Shyr, Yan Hu, L. Bastarache, Alex Cheng, Rizwan Hamid, Paul Harris, Hua Xu

引用次数: 0

Contextual Word Embedding for Biomedical Knowledge Extraction: a Rapid Review and Case Study 用于生物医学知识提取的上下文词嵌入：快速回顾与案例研究

Journal of healthcare informatics research Pub Date : 2024-01-03 DOI: 10.1007/s41666-023-00157-y

Dinithi Vithanage, Ping Yu, Lei Wang, Chao Deng

引用次数: 0

Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures 大脑活动受高维数据表示方式的影响：散点图诊断（Scagnostics）措施的脑电图研究

Journal of healthcare informatics research Pub Date : 2023-12-12 DOI: 10.1007/s41666-023-00145-2

Ronak Etemadpour, Sonali Shintree, A. D. Shereen

引用次数: 0

Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview 产生真实世界证据的电子健康记录数据偏差:综述

Journal of healthcare informatics research Pub Date : 2023-11-14 DOI: 10.1007/s41666-023-00153-2

Ban Al-Sahab, Alan Leviton, Tobias Loddenkemper, Nigel Paneth, Bo Zhang

引用次数: 0

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes 可解释的预测模型来了解孕产妇和胎儿结局的危险因素

Journal of healthcare informatics research Pub Date : 2023-10-13 DOI: 10.1007/s41666-023-00151-4

Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian Painter, Vivienne Souter, Rich Caruana

{"title":"Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes","authors":"Tomas M. Bosschieter, Zifei Xu, Hui Lan, Benjamin J. Lengerich, Harsha Nori, Ian Painter, Vivienne Souter, Rich Caruana","doi":"10.1007/s41666-023-00151-4","DOIUrl":"https://doi.org/10.1007/s41666-023-00151-4","url":null,"abstract":"Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance for high risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e.g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0