Sampling methods and feature selection for mortality prediction with neural networks [0.03%]
神经网络死亡率预测的采样方法和特征选择
Christian Steinmeyer,Lena Wiese
Christian Steinmeyer
Along with digitization, automatic data-driven decision support systems become increasingly popular. Mortality prediction is a vital part of that decision process. With more data available, sophisticated machine learning models like (Artifi...
Margarida F Pereira,Cosima Prahm,Jonas Kolbenschlag et al.
Margarida F Pereira et al.
Background: The human hand is the part of the body most frequently injured in work related accidents, accounting for a third of all accidents at work and often involving surgery and long periods of rehabilitation. Several...
Rohit J Kate
Rohit J Kate
SNOMED CT is the most comprehensive clinical ontology and is also amenable for automated reasoning. However, in order to unleash its full potential for automated reasoning over clinical text, a mechanism to convert clinical terms into SNOME...
Chinese medical named entity recognition based on multi-granularity semantic dictionary and multimodal tree [0.03%]
基于多粒度语义词典和多模树的中文医学命名实体识别研究
Caiyu Wang,Hong Wang,Hui Zhuang et al.
Caiyu Wang et al.
In recent years, named entity recognition (NER) has attracted significant attention in various fields, especially in the clinical medical field, because NER is essential for useful mining knowledge in the clinical medical area. However, the...
Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors [0.03%]
缺血性中风：流程视角，临床特征，个性特征和外部因素
Denise M V Sato,Letícia K Mantovani,Juliana Safanelli et al.
Denise M V Sato et al.
Objective: To describe a method of analysis for understanding the health care process, enriched with information on the clinical and profile characteristics of the patients. To apply the proposed technique to analyze an i...
Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts [0.03%]
多本体精炼嵌入（MORE）：用于生物医学概念的基于多本体和语料库的混合语义表示模型
Steven Jiang,Weiyi Wu,Naofumi Tomita et al.
Steven Jiang et al.
Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduc...
A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management [0.03%]
基于深度学习的无监督方法来填补电子健康记录中的缺失值以改善患者管理
Da Xu,Paul Jen-Hwa Hu,Ting-Shuo Huang et al.
Da Xu et al.
Electronic health records (EHRs) often suffer missing values, for which recent advances in deep learning offer a promising remedy. We develop a deep learning-based, unsupervised method to impute missing values in patient records, then exami...
A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases [0.03%]
基于语义相似性的蛋白质-蛋白质相互作用预测方法：以P53结合激酶为例评估
Steven Cox,Xialan Dong,Ruhi Rai et al.
Steven Cox et al.
Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical dis...
Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs [0.03%]
开发和测试离散事件仿真模型以评估糖尿病预防项目的预算影响
Karoliina Kaasalainen,Janne Kalmari,Toni Ruohonen
Karoliina Kaasalainen
Type 2 diabetes (T2D) is one of the most rapidly increasing non-communicable diseases worldwide. Lifestyle interventions are effective in preventing T2D but also resource intensive. This study evaluated with discrete event simulation (DES) ...
Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction [0.03%]
使用卷积神经网络从患者多变量时间序列数据中学习隐藏模式：一个医疗成本预测的案例研究
Mohammad Amin Morid,Olivia R Liu Sheng,Kensaku Kawamoto et al.
Mohammad Amin Morid et al.
Objective: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a con...