发布求助
文献互助 智能选刊 最新文献

2017 IEEE International Conference on Healthcare Informatics (ICHI)最新文献

筛选
英文 中文
Cost Reduction via Patient Targeting and Outreach: A Statistical Approach 通过患者目标和外展降低成本:一种统计方法
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.86
David Kartchner, Andy Merrill, Jonathan Wrathall
{"title":"Cost Reduction via Patient Targeting and Outreach: A Statistical Approach","authors":"David Kartchner, Andy Merrill, Jonathan Wrathall","doi":"10.1109/ICHI.2017.86","DOIUrl":"https://doi.org/10.1109/ICHI.2017.86","url":null,"abstract":"Identifying future high-cost patients allows healthcare organizations to take preventative measures to both reduce future patient costs and lessen the burden of illness. This paper expands upon past risk adjustment strategies to predict the persistently high-cost patients by combining clinical and claims data on patients and assessing risk using machine learning techniques. Our approach not only leads to substantial gains in predictive accuracy, but also reduces the amount of data needed to identify high-risk patients, enabling providers to confidently identify long-term health risk in as little as three months after their initial encounter.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"1 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":"116220396","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
Extracting Intrauterine Device Usage from Clinical Texts Using Natural Language Processing 使用自然语言处理从临床文本中提取宫内节育器使用情况
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.21
Jianlin Shi, D. Mowery, Mingyuan Zhang, J. Sanders, W. Chapman, L. Gawron
{"title":"Extracting Intrauterine Device Usage from Clinical Texts Using Natural Language Processing","authors":"Jianlin Shi, D. Mowery, Mingyuan Zhang, J. Sanders, W. Chapman, L. Gawron","doi":"10.1109/ICHI.2017.21","DOIUrl":"https://doi.org/10.1109/ICHI.2017.21","url":null,"abstract":"Intrauterine devices (IUDs) are highly-effective contraceptive methods for preventing unintended pregnancy and related adverse outcomes. Clinical Decision Support (CDS) systems could aid care providers in identifying patients at risk for pregnancy due to lack of contraceptive use. However, research suggests that this information is not reliably documented in structured data fields for query, but rather in the clinical notes. As a first step towards developing a robust CDS tool to identify high-risk patients for contraceptive counseling, we developed a clinical information extraction tool, EasyCIE, that readily identifies mentions of IUD usage and classifies whether a note contains evidence that an IUD is present or not for review by domain experts. In this preliminary study, EasyCIE produced high recall and excellent precision distinguishing notes of patients with current IUD usage from notes of patients with historical or no usage.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"9 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":"114898839","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: 10
Unpacking Happiness: Lessons from Smartphone Photography Among College Students 打开幸福的包装:大学生智能手机摄影的经验教训
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.25
Yu Chen, G. Mark, Sanna Ali, Xiaojuan Ma
{"title":"Unpacking Happiness: Lessons from Smartphone Photography Among College Students","authors":"Yu Chen, G. Mark, Sanna Ali, Xiaojuan Ma","doi":"10.1109/ICHI.2017.25","DOIUrl":"https://doi.org/10.1109/ICHI.2017.25","url":null,"abstract":"Smartphone photography is gaining popularity among young adults and can be used for expressing mood. However, how photos are used for expressing happiness is understudied. This paper presents the findings of a study involving 27 college students who were instructed to take one photo daily representing a happy moment for three weeks. The participants also viewed their daily photos three times a day and reported their mood at the time. Results show that while users reported positive feelings in general, the feeling was calmer for individuals who took photos to make others happy than for those whose photos were intended to make themselves happy. Although food is the most frequent photo theme in this study, photos about social connections evoke the strongest happy sensation. Other common photo themes associated with happiness include entertainment, personal achievement, and nature. We offer insights into enhancing young adults' mental well-being using smartphone photography that integrates prosocial behavior and vitalizes links to strong ties.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"1 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":"131150794","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: 5
Approximate Temporal Functional Dependencies on Clinical Data 临床数据的近似时间功能依赖性
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.30
M. Mantovani
{"title":"Approximate Temporal Functional Dependencies on Clinical Data","authors":"M. Mantovani","doi":"10.1109/ICHI.2017.30","DOIUrl":"https://doi.org/10.1109/ICHI.2017.30","url":null,"abstract":"The aim of this PhD project is to provide a framework for temporal data mining.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"81 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":"133310383","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
Structured Information Displays for the Comparison of Clinical Trials 用于临床试验比较的结构化信息显示
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.9
P. Unni, Jiantao Bian, C. Weir, G. Fiol
{"title":"Structured Information Displays for the Comparison of Clinical Trials","authors":"P. Unni, Jiantao Bian, C. Weir, G. Fiol","doi":"10.1109/ICHI.2017.9","DOIUrl":"https://doi.org/10.1109/ICHI.2017.9","url":null,"abstract":"At the point of patient care, clinicians have many unanswered questions that if unanswered could potentially compromise care quality. Major barriers of pursuing these questions include lack of time, doubt that an answer exists, and an effort to benefit calculation. PubMed® contains answers to most questions, however, the answers are often in the form of a list of \"study-centered\" abstracts that often do not match clinicians' patient-centered mental models. The aim of this study is to investigate RCTComp, a novel prototype of patient-centered, structured and graphical displays that is based on the well-known format of a PICO information display (Population, Intervention, Comparison, and Outcome). A PICO display was hypothesized to help physicians synthesize and compare evidence from multiple clinical trials found in PubMed® because of the familiarity with the model.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"38 10 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":"131967121","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
Integration of Accountable Care Organization and Additional Hospital Data into CMS Referral Analytics System 整合责任医疗组织和额外的医院数据到CMS转诊分析系统
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.44
B. Ru, Qingxin Wu, Xin Wang, Lixia Yao, Yugang Jia
{"title":"Integration of Accountable Care Organization and Additional Hospital Data into CMS Referral Analytics System","authors":"B. Ru, Qingxin Wu, Xin Wang, Lixia Yao, Yugang Jia","doi":"10.1109/ICHI.2017.44","DOIUrl":"https://doi.org/10.1109/ICHI.2017.44","url":null,"abstract":"In recent years, the Center for Medicare and Medicaid Services (CMS) published 439 datasets containing rich information on healthcare providers, Accountable Care Organizations (ACO), Shared Patient Patterns (\"referral data\"), Medicare Utilization and Payments, etc. Combining these data can provide stakeholders a broader and more synergistic vision of the healthcare market. But the data integration is challenging, as the mapping between several CMS datasets was missing. In this paper, we proposed integration solutions for combining referral data with ACO, Hospital Compare, and physician-hospital affiliation data. Extending referral analysis to these new dimensions could provide more analytics insights to facilitate the decision-making processes of users including physicians, hospitals and government agencies.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"5 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":"133578895","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
Discovering Quantitative Temporal Functional Dependencies on Clinical Data 发现定量时间功能依赖于临床数据
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.80
Combi Carlo, M. Mantovani, P. Sala
{"title":"Discovering Quantitative Temporal Functional Dependencies on Clinical Data","authors":"Combi Carlo, M. Mantovani, P. Sala","doi":"10.1109/ICHI.2017.80","DOIUrl":"https://doi.org/10.1109/ICHI.2017.80","url":null,"abstract":"Approximate functional dependencies, even with suitable temporal extensions, have been recently proposed as a methodological tool for mining clinical data. It allows healthcare stakeholders to derive new knowledge from overwhelming amount of healthcare and clinical data. Some examples of the kind of knowledge derivable from data through dependencies may be \"month by month, patients with the same symptoms get the same type of therapy\" or \"within 15 days, patients with the same diagnosis and the same therapy receive the same daily amount of drug\". The main limitation of such kind of dependencies is that they cannot deal with quantitative data, when some tolerance can be allowed for numerical values. In particular, such limitation arises in clinical data warehouses, where analysis and mining have to consider one or more measures (related to quantitative data as lab test results, vital signs as blood pressures, temperature and so on), with respect to many dimensional (alphanumeric) attributes (as patient, hospital, physician, diagnosis) and to some time dimensions (as the day since hospitalization, the calendar date, and so on). According to this scenario, we introduce here a new kind of approximate temporal functional dependency, named multi approximate temporal functional dependency (MATFD), which consider dependencies between dimensions and quantitative measures from temporal clinical data. Such new dependencies may provide new knowledge as \"within 15 days, patients with the same diagnosis and the same therapy receive a daily amount of drug within a fixed range\". Moreover, we provide an original algorithm to mine such kind of dependencies and to derive some core dependencies, both for the discovered temporal window and for the involved dimensional attributes. Finally, we discuss some first results we obtained by pre-processing and mining ICU data from MIMIC III database.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"58 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":"129542153","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: 7
Note Highlights: Surfacing Relevant Concepts from Unstructured Notes for Health Professionals 注释亮点:从卫生专业人员的非结构化笔记中浮现相关概念
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.28
Vanessa López, J. Bettencourt-Silva, G. McCarthy, N. Mulligan, Fabrizio Cucci, Stéphane Deparis, M. Sbodio, Pierpaolo Tommasi, J. Segrave-Daly, C. Cullen, Ciaran Hennessy, Beth McKeon, K. Kelly, R. Olsen, J. Dinsmore, A. Brady, Nagesh Yadav, S. Kotoulas
{"title":"Note Highlights: Surfacing Relevant Concepts from Unstructured Notes for Health Professionals","authors":"Vanessa López, J. Bettencourt-Silva, G. McCarthy, N. Mulligan, Fabrizio Cucci, Stéphane Deparis, M. Sbodio, Pierpaolo Tommasi, J. Segrave-Daly, C. Cullen, Ciaran Hennessy, Beth McKeon, K. Kelly, R. Olsen, J. Dinsmore, A. Brady, Nagesh Yadav, S. Kotoulas","doi":"10.1109/ICHI.2017.28","DOIUrl":"https://doi.org/10.1109/ICHI.2017.28","url":null,"abstract":"Health and social care professionals are under increasing pressure to assimilate the ever-growing volume of data from case notes and electronic medical records. In this paper, we propose and evaluate with domain experts a cognitive system for patient-centric care that leverages and combines natural language processing, semantics, and learning from users over time to support care professionals making informed and timely decisions while reducing the burden of interacting with large volumes of unstructured patient notes. We propose methods for highlighting the entities embedded in the unstructured data and providing a personalized view of an individual. We evaluate through a user study and show a consensus between what the domain experts and the system consider relevant and discuss early feedback on the value of our Note Highlights methods to domain experts.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"32 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":"115656126","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
Automated EEG-Based Epileptic Seizure Detection Using Deep Neural Networks 基于脑电图的深度神经网络癫痫发作自动检测
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.55
J. Birjandtalab, M. Heydarzadeh, M. Nourani
{"title":"Automated EEG-Based Epileptic Seizure Detection Using Deep Neural Networks","authors":"J. Birjandtalab, M. Heydarzadeh, M. Nourani","doi":"10.1109/ICHI.2017.55","DOIUrl":"https://doi.org/10.1109/ICHI.2017.55","url":null,"abstract":"Millions of people around the world suffer from epilepsy. It is very important to provide a method to efficiently monitor the seizures and alert the caregivers to help patients. It is proven that EEG signals are the best markers for diagnosis of the epileptic seizures. In this paper, we used the frequency domain features (normalized in-band power spectral density) to extract information from EEG signals. We applied a deep learning technique based on multilayer perceptrons to improve the accuracy of seizure detection. The results indicate that our nonlinear technique is able to efficiently and automatically detect seizure and non-seizure episodes with an F-measure accuracy of around 95%.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"10 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":"132476148","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: 40
Off-Label Drug Use Detection Based on Heterogeneous Network Mining 基于异构网络挖掘的超说明书用药检测
2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.33
Mengnan Zhao
{"title":"Off-Label Drug Use Detection Based on Heterogeneous Network Mining","authors":"Mengnan Zhao","doi":"10.1109/ICHI.2017.33","DOIUrl":"https://doi.org/10.1109/ICHI.2017.33","url":null,"abstract":"Off-label drug use refers to prescribing marketed medications for the indications that are not included in their FDA-approved labeling information. Off-label drug use is quite common in clinical practice and inevitable to some extent. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train classifiers to recognize the known drug-disease associations. Lastly, we identified the off-label drug uses from the false-positive results.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"23 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":"132347091","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信