2017 IEEE International Conference on Healthcare Informatics (ICHI)最新文献_第8页

Towards Reliable Wearable-User Identification 迈向可靠的可穿戴用户识别

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.17

Sudip Vhaduri, C. Poellabauer

引用次数: 16

Using Unstructured Data to Identify Readmitted Patients 使用非结构化数据识别再入院患者

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.99

M. Rastegar-Mojarad, J. Lovely, Joshua J. Pankratz, S. Sohn, Donna M. Ihrke, A. Merchea, D. Larson, Hongfang Liu

引用次数: 2

Pattern Discovery from Directional High-Order Drug-Drug Interaction Relations 定向高阶药物-药物相互作用关系的模式发现

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.20

Xia Ning, T. Schleyer, Li Shen, Lang Li

引用次数: 7

Descriptive Analysis of Communication Patterns Between a Local Poison Control Center and Community Emergency Departments 某地中毒控制中心与社区急救部门沟通模式的描述性分析

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.89

V. Tiase, B. Crouch, Heather Bennett, C. Weng, Rumei Yang, M. Cummins

引用次数: 0

CVRT: Cognitive Visual Recognition Tracker 认知视觉识别跟踪器

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.65

Matthew Velazquez, Yugyung Lee

{"title":"CVRT: Cognitive Visual Recognition Tracker","authors":"Matthew Velazquez, Yugyung Lee","doi":"10.1109/ICHI.2017.65","DOIUrl":"https://doi.org/10.1109/ICHI.2017.65","url":null,"abstract":"Studies on visual attention of patients with Alzheimer's disease and Dementia is a promising way for keeping track of the individual patient's image recognition ability over. This research seeks to expand upon the current applications of combining the Android operating system with TensorFlow by providing a visual question answering platform for image analysis. This application, Cognitive Visual Recognition Tracker (CVRT), provides an entry point by which the user can ask questions concerning any image of their choosing, and then receive cumulative metrics over time to better assess any diminishing cognitive ability (i.e. Alzheimer's patients). In this work, recurrent neural networks as well as semantic analysis are leveraged to provide an interactive VQA experience. One of the main objectives of CVRT is for physicians to be able to determine trends from patient data that could either be applicable to the individual patient, or to many patients if an aggregate is formed from many individual datasets. On an individual level, these metrics would provide a way for the physician to monitor daily cognitive capability, whereas on a grander scale, these joint datasets could be used to provide better overall treatment for the disease with the future inclusion of predictive analytics. The final contribution is an interactive metrics platform by which other users can assess the primary user's cognitive capacity based on features of their questioning, and to then provide them with accurate trending or possible remediation plans based on their condition.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128528922","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: 1

Forecasting Influenza Levels Using Real-Time Social Media Streams 利用实时社交媒体流预测流感水平

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.68

Kathy Lee, Ankit Agrawal, A. Choudhary

{"title":"Forecasting Influenza Levels Using Real-Time Social Media Streams","authors":"Kathy Lee, Ankit Agrawal, A. Choudhary","doi":"10.1109/ICHI.2017.68","DOIUrl":"https://doi.org/10.1109/ICHI.2017.68","url":null,"abstract":"Seasonal influenza is a contagious respiratory illness that can cause various complications, worsen chronic illnesses, and sometimes lead to deaths. During 2009 H1N1 flu pandemic, up to 203,000 deaths occurred worldwide. Early detection and prediction of disease outbreak is critical because it can provide more time to prepare a response and significantly reduce the impact caused by a pandemic. The traditional influenza surveillance system by Centers for Disease Control and Prevention (CDC) collects U.S. Influenza-Like Illness related physicians visits data from sentinel practices and provides a retrospective analysis delayed by two weeks. Google Flu Trends proposed a method that uses online search queries data to estimate current (real-time) influenza activity. Here we present a system that (1) predicts future influenza activities, (2) provides more accurate real-time assessment than before, and (3) combines real-time big social media data streams and CDC historical datasets for predictive models to accomplish accurate predictions. Although retrospective analysis and observations are important, prediction of future flu levels can represent a big leap because such predictions provide actionable insights for public health that can be used for planning, resource allocation, treatments and prevention. Thus, compared to previous work, our work represents an advancement in accuracy of assessments, prediction of future flu activity accurately and an ability to combine big social data and observed CDC data to build predictive models.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125389900","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: 47

Feasibility of Internet of Things Technologies to Support Aging 物联网技术支持老龄化的可行性

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.34

Y. Choi

引用次数: 0

MRSA Transmission in a Personal Care Home Facility: A Spatially Explicit Agent Based Modeling Approach MRSA在个人护理院的传播:一种基于空间显式代理的建模方法

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.36

N. Shojaati, M. Andkhoie, Osagie Osemwegie, N. Osgood

{"title":"MRSA Transmission in a Personal Care Home Facility: A Spatially Explicit Agent Based Modeling Approach","authors":"N. Shojaati, M. Andkhoie, Osagie Osemwegie, N. Osgood","doi":"10.1109/ICHI.2017.36","DOIUrl":"https://doi.org/10.1109/ICHI.2017.36","url":null,"abstract":"The purpose of this study is to estimate the effects of hand washing, cleaning, cohorting, isolation rooms and duration of cleaning on Methicillin Resistant Staphylococcus Aureus (MRSA) colonization and infections. The study uses spatially explicit agent-based modeling approach together with a Susceptible-Exposed-Infected-Recovered (SEIR) to characterize infection spread in an 8-bedroom personal care home facility. The model consists of 8 residents, 2 nurses and 1 cleaner. The model explicitly simulates the dynamics of pathogen reservoirs associated with both surfaces and people, and further counts the colonization and infection events over a 1-year period. To account for stochastics, the model is iterated 100 times for each of five different 'what if' scenarios. Model results suggest that cohorting is the most effective method to reduce the events of MRSA colonization and infections in the personal care home facility. When compared to baseline, cleaning at a higher intensity led to a 35 percent reduction (p-value is less than 0.0005) in the median counts of MRSA colonization (median 202 vs 132 colonization events) and 41 percent reduction (p-value is equal to 0.0335) in the median counts of MRSA infections (median 17 vs 10 MRSA infection events). In terms of intervention that were not effective, we found no statistically significant difference in the efficacy of having duration of cleaning more than 8 hours per day, higher intensity hand washing and use of an isolation room.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125549496","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

An Efficient Approach of Outbreak Preparedness for Dengue 防范登革热疫情爆发的有效方法

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.16

N. Dinayadura, Armin R. Mikler, Jayantha Muthukudage

引用次数: 0

A Flexible Parental Engaged Consent Model for the Secondary Use of Their Infant’s Physiological Data in the Neonatal Intensive Care Context 一个灵活的父母参与同意模型的二次使用他们的婴儿的生理数据在新生儿重症监护的背景下

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.88

Yvonne Choi, C. McGregor

{"title":"A Flexible Parental Engaged Consent Model for the Secondary Use of Their Infant’s Physiological Data in the Neonatal Intensive Care Context","authors":"Yvonne Choi, C. McGregor","doi":"10.1109/ICHI.2017.88","DOIUrl":"https://doi.org/10.1109/ICHI.2017.88","url":null,"abstract":"The secondary use of health data, especially the use of physiological data for research holds many opportunities for improving the current understanding of neonatal conditions. As a neonate is unable to provide their consent regarding participation in research studies, a substitute decision maker (SDM) must provide parental or legal guardian consent. However it has been well documented that there are many emotional, mental and physical challenges associated with the parental consent process in the neonatal intensive care unit (NICU). It is proposed that a flexible parental engaged consent model could help alleviate some of these issues by providing parents with the ability to choose and change their clinical engagement level preference for their infant’s participation in research at their convenience at any point in time. In this paper, an extension to Service based Multidimensional Temporal Data Mining Framework (STDMn0) to allow for the functionality of flexible patient or surrogate consent is presented based on the use of a flexible consent model initially proposed by Heath [1]. This functionality is demonstrated via an example implementation for a generic retrospective research study in the NICU setting.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"22 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113936411","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: 2