Voting of predictive models for clinical outcomes: consensus of algorithms for the early prediction of sepsis from clinical data and an analysis of the PhysioNet/Computing in Cardiology Challenge 2019

Early Detection of Sepsis using Ensemblers

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records. The Physionet 2019 challenge consists of medical records of over 40,000 patients. Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally. The model achieved an accuracy of 93.45% and a utility score of 0.271. The utility score as defined by the organizers takes into account true positives, negatives and false alarms.

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation

Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventive treatment. However, the early prediction is challenging because patients' sequential data in EHR contains temporal interactions of multiple clinical events. And capturing temporal interactions in the long event sequence is hard for traditional LSTM. Rather than directly applyin…

SepsisAI Orchestrator：一个容器化和可扩展的平台，用于部署AI模型和实时监控以实现早期败血症检测

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

本文提出SepsisAI-Orchestrator平台，通过整合HL7 FHIR启发的临床文档架构（CDA）预处理、NoSQL存储、容器化LightGBM分类器和Streamlit临床仪表板，解决了早期败血症检测中AI模型部署的挑战，并通过负载测试展示了U型扩展行为。

临床环境中脓毒症发病的早期预测

Early prediction of onset of sepsis in Clinical Setting

本研究利用XGBoost模型基于蒙特菲奥雷医疗中心107个特征的临床数据预测脓毒症早期发病，在测试和前瞻性数据上分别取得0.494和0.378的效用评分，显示出良好的临床决策支持潜力。

Fusing Continuous-valued Medical Labels using a Bayesian Model

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of labe…

通过可解释机器学习与电子健康记录中的错误处理改进临床决策支持

Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records

本研究提出Trust-MAPS框架，将临床知识转化为数学约束以处理EMR数据错误，提升脓毒症早期预测性能15%，并增强模型可解释性。