2017 IEEE International Conference on Healthcare Informatics (ICHI)最新文献_第6页

Exploring the Performance of Stacking Classifier to Predict Depression Among the Elderly 探讨堆叠分类器预测老年人抑郁的性能

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.95

E. Lee

{"title":"Exploring the Performance of Stacking Classifier to Predict Depression Among the Elderly","authors":"E. Lee","doi":"10.1109/ICHI.2017.95","DOIUrl":"https://doi.org/10.1109/ICHI.2017.95","url":null,"abstract":"Geriatric depression is a disease prevailing in the elderly. It is characterized by typical symptoms of lower functioning, diminished interest in activities, insomnia or hypersomnia, fatigue or loss of energy and observable psycho motor agitation or retardation. Many studies exist with an aim to predict the geriatric depression from the perspective of healthcare informatics based on data mining analytics. However, there is no study emphasizing on the performance of stacking mechanism, which is one of ensemble classifiers. Therefore, this study is concerned with investigating the performance of stacking approach to predicting the geriatric depression-related dataset from the Korea National Health and Nutrition Examination Survey (KNHANES) ranging from 2010 to 2015. The KNHANES is a publicly available big dataset out of a national surveillance system aimed at assessing the health and nutritional status of Koreans since 1998. It is a nationally representative cross-sectional survey including approximately 10,000 individuals each year as a survey sample. By using 9,089 dataset regarding the geriatric depression in the Korean elderly (2010 ~2015), this study analyzed the changes in performance of the stacking mechanism when combining five classifiers (i.e., LR, DT, NN, SVM, NBN) in the base-level learner and meta-level learner. The performance of stacking mechanism measured in accuracy and AUC shows more robust pattern when the base-level learner is relatively simple (like LR, DT), and the meta-level learner is rather complex (like NBN, NN, SVM). To be specific, before the feature selection, the stacking performance was very competitive with accuracy 0.8624 when LR(SVM) indicating that the base-level learner is LR, and the meta-level learner is SVM. After the feature selection, the stacking performance was best with accuracy 0.8643 when DT (NN). With AUC, the similar results were obtained- i.e., LR(NN) with 0.8182 before the feature selection, and LR(NBN) with 0.8147 after the feature selection.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"12 8 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":"126073370","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: 13

Predicting High-Order Directional Drug-Drug Interaction Relations 预测高阶定向药物-药物相互作用关系

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.76

Xia Ning, Li Shen, Lang Li

引用次数: 4

Spatio-Temporal Optimization of Seasonal Vaccination Using a Metapopulation Model of Influenza 基于流感元群体模型的季节性疫苗接种时空优化

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.83

S. Venkatramanan, Jiangzhuo Chen, Sandeep Gupta, B. Lewis, M. Marathe, H. Mortveit, A. Vullikanti

{"title":"Spatio-Temporal Optimization of Seasonal Vaccination Using a Metapopulation Model of Influenza","authors":"S. Venkatramanan, Jiangzhuo Chen, Sandeep Gupta, B. Lewis, M. Marathe, H. Mortveit, A. Vullikanti","doi":"10.1109/ICHI.2017.83","DOIUrl":"https://doi.org/10.1109/ICHI.2017.83","url":null,"abstract":"Prophylactic interventions such as vaccine allocation are one of the most effective public health policy planning tools. The supply of vaccines is limited, and an importantproblem is when and how to allocate the available vaccination supply, referred to as the Vaccine Allocation Problem. The spread of epidemics is modeled by the SEIR process, which has a very complex dynamics, and depends on human contacts and mobility. This makes the design of efficient solutions tovaccine allocation problem to minimize the number of infections a very challenging problem. In particular, this requires good models for human mobility, and optimization tools for vaccine allocation.In this paper, we study the vaccine allocation problem in the context of seasonal Influenza spread inthe United States. We develop a novel national scale flu model that integrate both short andlong distance travel, which are known to be important determinants of the spread of Influenza. We also design a greedy algorithm for allocating the vaccine supply at a county level. Our results show significant improvement over the current baseline, whichinvolves allocating vaccines based on the state population.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"320 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":"122328616","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: 16

Multitask LS-Svm for Predicting Bleeding and Re-operation Due to Bleeding 多任务LS-Svm预测出血及出血后再手术

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.101

Che Ngufor, Dennis H. Murphree, S. Upadhyaya, Jyotishman Pathak, D. Kor

引用次数: 0

Systematic Review of mHealth Interventions Involving Fitbit Activity Tracking Devices 涉及Fitbit活动跟踪设备的移动健康干预系统综述

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.42

A. Mishra, Antonio Nieto, S. Kitsiou

引用次数: 8

A K-Means Approach to Clustering Disease Progressions 聚类疾病进展的k -均值方法

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.18

D. Luong, V. Chandola

{"title":"A K-Means Approach to Clustering Disease Progressions","authors":"D. Luong, V. Chandola","doi":"10.1109/ICHI.2017.18","DOIUrl":"https://doi.org/10.1109/ICHI.2017.18","url":null,"abstract":"K-means algorithm has been a workhorse of unsupervised machine learning for many decades, primarily owing to its simplicity and efficiency. The algorithm requires availability of two key operations on the data, first, a distance metric to compare a pair of data objects, and second, a way to compute a representative (centroid) for a given set of data objects. These two requirements mean that k-means cannot be readily applied to time series data, in particular, to disease progression profiles often encountered in healthcare analysis. We present a k-means inspired approach to clustering disease progression data. The proposed method represents a cluster as a set of weights corresponding to a set of splines fitted to the time series data and uses the \"goodness-of-fit\" as a way to assign time series to clusters. We use the algorithm to group patients suffering from Chronic Kidney Disease (CKD) based on their disease progression profiles. A qualitative analysis of the representative profiles for the learnt clusters reveals that this simple approach can be used to identify groups of patients with interesting clinical characteristics. Additionally, we show how the representative profiles can be combined with patient's observations to obtain an accurate patient specific profile that can be used for extrapolating into the future.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"48 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":"128449983","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: 17

Heterogenous Knowledge Discovery from Medical Data Ontologies 医学数据本体的异构知识发现

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.60

Gaurang Gavai, M. Nabi, D. Bobrow, S. Shahraz

引用次数: 0

Interconnected Personal Health Record Ecosystem Using IoT Cloud Platform and HL7 FHIR 基于物联网云平台和HL7 FHIR的互联个人健康记录生态系统

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.82

Jae-Ki Hong, Peter Morris, Jong-Kyoun Seo

{"title":"Interconnected Personal Health Record Ecosystem Using IoT Cloud Platform and HL7 FHIR","authors":"Jae-Ki Hong, Peter Morris, Jong-Kyoun Seo","doi":"10.1109/ICHI.2017.82","DOIUrl":"https://doi.org/10.1109/ICHI.2017.82","url":null,"abstract":"Personal health record (PHR) systems have yet to reach mass adoption despite years of varied implementations. In this paper, we examine the limitations of current PHRs and propose an interconnected PHR system built in adherence with healthcare data communication standards and an IoT Cloud platform. The new ecosystem includes: an interoperable hospital information system to store electronic medical records (EMRs) and transfer them to a PHR system via email; a public cloud that supports encrypted data sharing for big data analysis services; a PHR gateway repository based on an IoT module which communicates with hospitals and public clouds; and a mobile application to manage and view PHR. This system can store and share raw EMR and life log data based on the healthcare communication standard \"HL7 FHIR\". To validate the usability of the interconnected PHR system, a clinical trial is underway to develop an obesity management model from individuals' hospital-provided genomic data, on the one hand, and diet and exercise logs gathered by the mobile application, on the other. We demonstrate how the proposed IoT Cloud based PHR ecosystem is interoperable and practical for promoting healthcare big data analysis services.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"42 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":"134387954","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: 26

Drug-Drug Interactions (DDIs) Detection from On-Line Health Forums: Bi-Submodular Optimization (BSMO) 在线健康论坛中药物-药物相互作用(ddi)检测:双次模优化(BSMO)

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.91

Yan Hu, Rui Wang, F. Chen

{"title":"Drug-Drug Interactions (DDIs) Detection from On-Line Health Forums: Bi-Submodular Optimization (BSMO)","authors":"Yan Hu, Rui Wang, F. Chen","doi":"10.1109/ICHI.2017.91","DOIUrl":"https://doi.org/10.1109/ICHI.2017.91","url":null,"abstract":"With the growth of mobile Internet, online health forums become more accessible for patient to health related discussions, subsequently host rich resources of drug-drug interactions (DDIs). However, traditional methods are not feasible for the large volume online data. They are designed for highly structured data sources such as clinical trials and spontaneous reporting systems, whose inherent limitations include low coverage and under-reporting. In this paper, we propose a bi-submodular optimization (BSMO) method to detect DDIs using the forum data collected online. The relationships between co-mentioned drugs and symptoms can be modeled with a conditional (predefined thresholds) graph, where a vertex represents either a drug or a symptom, and an edge represents the co-occurrence among drugs and/or symptoms. A connectedsub-graph consists of both symptom and drug vertexes reveals the occurrence of DDIs. A novel score function is proposed to characterize the degree of DDIs within a connected subgraph. Therefore the DDIs detection using on-line health forum data is then formulated as a sub-graph detection problem. An approximated algorithm was proposed based on bi-submodular optimization, then showed the complexity of the algorithm is nearly linear. Extensive experiments on the health forum data demonstrate the effectiveness and efficiency of our proposed approach.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"138 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":"124325956","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

Electrooculography Based iOS Controller for Individuals with Quadriplegia or Neurodegenerative Disease 基于眼电图的iOS控制器用于四肢瘫痪或神经退行性疾病患者

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.90

Bryce O'Bard, A. Larson, Joshua Herrera, Dominic Nega, K. George

{"title":"Electrooculography Based iOS Controller for Individuals with Quadriplegia or Neurodegenerative Disease","authors":"Bryce O'Bard, A. Larson, Joshua Herrera, Dominic Nega, K. George","doi":"10.1109/ICHI.2017.90","DOIUrl":"https://doi.org/10.1109/ICHI.2017.90","url":null,"abstract":"As the use of tablet computers and cell phones has become a standard medium of access to information, entertainment, and communication around the world, the reliance on having access to such devices has increased tremendously. For individuals with quadriplegia or neurodegenerative diseases, the access to these mobile devices is greatly hindered due to their inherent touchscreen design. Assistive technology solutions available to such patients today require families of patients to invest thousands of dollars in standalone tablet systems. There are few known options for allowing such patients to connect to their existing tablets or smartphones, which already have access to apps that can assist them in communication and daily activities. For this reason, we present in this paper a low-cost commercial off the shelf (COTS) assistive communication device to allow individuals with such conditions to access iOS based devices through electrooculography signals captured from their eye movements. Signals are captured through electrodes placed on the users face around the eyes. These signals are filtered, amplified, and processed to detect key eye movements mapped to perform control outputs sent to the iOS device. The communication capabilities are tested through the administration of a typing test to measure characters typed per minute (cpm). Testing of the device includes timed trials of directed tasks carried out by both healthy subjects and patients with ALS (PALS). It was determined that a user can type an average of 3.25 ~ 6.11 cpm using the device with an average accuracy of 89%. This could be significantly improved using a better suited keyboard application on the phone.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"18 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120912456","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: 11