2017 IEEE International Conference on Healthcare Informatics (ICHI)最新文献_第7页

Radiology Clinical Notes Mining Using Weighted Association Rules 基于加权关联规则的放射学临床记录挖掘

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.23

Mohammad S. Alodadi

引用次数: 2

Predictive Modeling of Therapy Decisions in Metastatic Breast Cancer with Recurrent Neural Network Encoder and Multinomial Hierarchical Regression Decoder 用循环神经网络编码器和多项层次回归解码器对转移性乳腺癌治疗决策的预测建模

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.51

Yinchong Yang, P. Fasching, Volker Tresp

{"title":"Predictive Modeling of Therapy Decisions in Metastatic Breast Cancer with Recurrent Neural Network Encoder and Multinomial Hierarchical Regression Decoder","authors":"Yinchong Yang, P. Fasching, Volker Tresp","doi":"10.1109/ICHI.2017.51","DOIUrl":"https://doi.org/10.1109/ICHI.2017.51","url":null,"abstract":"The increasing availability of novel health-related data sources —e.g., from molecular analysis, health Apps and electronic health records— might eventually overwhelm the physician, and the community is investigating analytics approaches that might be useful to support clinical decisions. In particular, the success of the latest developments in Deep Learning has demonstrated that machine learning models are capable of handling —and actually profiting from— high dimensional and possibly sequential data. In this work, we propose an encoder-decoder network approach to model the physician's therapy decisions. Our approach also provides physicians with a list of similar historical patient cases to support the recommended decisions. By using a combination of a Recurrent Neural Network Encoder and a Multinomial Hierarchical Regression Decoder, we specifically tackle two common challenges in modeling clinical data:First, the issue of handling episodic data of variable lengths and, second, the need to represent hierarchical decision procedures. We conduct experiments on a large real-world dataset collected from thousands of metastatic breast cancer patients and show that our model outperforms more traditional approaches.","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":"130181012","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: 23

Efficient Bayesian Detection of Disease Onset in Truncated Medical Data 截断医疗数据中疾病发作的高效贝叶斯检测

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.10

Bob Price, Lottie Price, Dylan Cashman, M. Nabi

{"title":"Efficient Bayesian Detection of Disease Onset in Truncated Medical Data","authors":"Bob Price, Lottie Price, Dylan Cashman, M. Nabi","doi":"10.1109/ICHI.2017.10","DOIUrl":"https://doi.org/10.1109/ICHI.2017.10","url":null,"abstract":"This paper describes a principled statistical methodof preprocessing incidentally collected electronic medical recordsto facilitate short-term predictions of disease onset withoutexplicit interaction with patients (e.g., medical tests, questionnaires). The model is also applicable to detection of remission. In incidentally collected data, records are possibly left and righttruncated - the first time an event of interest is seen in a patient'sdata may not be the first time in the patient's history that ithappened. It is therefore difficult to know if a disease onsethappens in a given history. If we are unable to determine ifand when the onset occurs, supervised learning and regressionapproaches cannot be applied.Our method determines if an onset occurs in a set of sparseand incomplete patient records, calculates the time of this onsetand provides a principled measure of confidence. It combinesindividual patient history with expectations computed from areference population. We compare the proposed method againststandard change detection algorithms on generated data withrealistic event sparsity and show that it can reliably detect onsetswhere traditional methods fail. We then go on to apply thealgorithm to a large corpus of U.S. Medicare data and show thatthe algorithm scales to large datasets efficiently. The algorithmis currently in trials at a large medical informatics company.","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":"128853774","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

A Robot a Day Keeps the Blues Away 一天一个机器人，远离忧郁

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.43

Casey C. Bennett, S. Šabanović, J. Piatt, S. Nagata, Lori Eldridge, Natasha Randall

引用次数: 26

User-Centered Mapping of Nurses’ Workarounds to Design Principles for Interactive Systems in Home Wound Care 以用户为中心的护士工作方法映射到家庭伤口护理交互系统设计原则

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.22

Dawood Al-Masslawi, Shannon Handfield, S. Fels, R. Lea, L. Currie

{"title":"User-Centered Mapping of Nurses’ Workarounds to Design Principles for Interactive Systems in Home Wound Care","authors":"Dawood Al-Masslawi, Shannon Handfield, S. Fels, R. Lea, L. Currie","doi":"10.1109/ICHI.2017.22","DOIUrl":"https://doi.org/10.1109/ICHI.2017.22","url":null,"abstract":"The trend to discharge patients early from acute care settings to the home has increased the demand put on homecare nurses. Substantial portions of homecare patients have chronic or difficult to heal wounds. Homecare nurses use electronic patient documentation systems to input data, to support clinical decisions and to provide appropriate care for patients. These systems often do not support aspects of nurses' clinical work. Finding themselves facing barriers related to these systems, nurses create and use alternatives to overcome the barriers. These alternatives are called workarounds. The study presented here aims to identify possible mappings of workarounds as user feedback to design principles for wound documentation applications. Homecare nurses providing wound care were followed for 120 hours. Workarounds created and used by these nurses to provide care for patients with wounds were identified and mapped to design principles. The instances of workarounds were topically coded to identify their attributes. These attributes were used to extend previous work situation analysis models and form a new conceptual model called the \"workaround situation model\". The workaround situation model was used to identify the most common workaround situations and these were validated using a follow-up survey. After validation, a mapping process was formalized to identify and contextualize relevant design principles for patient documentation systems with the attributes of common workaround situations. The results were a set of mapped patient documentation system design principles for patients with wounds. Our results indicate it is possible that use of the workarounds as user feedback can inform new design principles and support nurse-centered design.","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":"128834299","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: 4

Approaching the Design of an Information Display to Support Critical Care 探讨支持重症监护的信息显示设计

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.64

Thomas J. Reese, K. Kawamoto, G. Fiol, C. Weir, J. Tonna, Noa Segall, Paige Nesbitt, Rosalie G. Waller, D. Borbolla, Eugene Moretti, M. Wright

引用次数: 4

An Active Learning Based Prediction of Epidural Stimulation Outcome in Spinal Cord Injury Patients Using Dynamic Sample Weighting 基于主动学习的脊髓损伤患者硬膜外刺激结果动态加权预测

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.38

Mohammad Kachuee, Lisa D. Moore, Tali Homsey, H. G. Damavandi, B. Moatamed, Anahita Hosseini, Ruyi Huang, J. Leiter, Daniel C. Lu, M. Sarrafzadeh

{"title":"An Active Learning Based Prediction of Epidural Stimulation Outcome in Spinal Cord Injury Patients Using Dynamic Sample Weighting","authors":"Mohammad Kachuee, Lisa D. Moore, Tali Homsey, H. G. Damavandi, B. Moatamed, Anahita Hosseini, Ruyi Huang, J. Leiter, Daniel C. Lu, M. Sarrafzadeh","doi":"10.1109/ICHI.2017.38","DOIUrl":"https://doi.org/10.1109/ICHI.2017.38","url":null,"abstract":"Recent studies suggest that epidural stimulation of the spinal cord could increase the motor pattern both in motor and sensory complete spinal cord injury (SCI) patients. However, choosing the optimal epidural stimulation variables, such as the frequency, intensity, and location of the stimulation, significantly affects maximal motor functionality. This paper presents a novel technique using machine learning methods to predict the functionality of a SCI patient after epidural stimulation. Additionally, we suggest a committee-based active learning method to reduce the number of clinical experiments required through exploring the stimulation configuration space more efficiently. This paper also introduces a novel method to dynamically weight the results of different experiments based on neural networks to create an optimal estimate of the quantity of interest. The proposed method for the prediction of stimulation outcomes is evaluated based on various accuracy measures such as mean absolute error, standard deviation, and correlation coefficient. The results show that the proposed method can be used to reliably predict the outcome of epidural stimulation on maximum voluntary contraction force with the prediction error of about 15%.","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":"125190998","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: 4

Evaluation of Sensing and Processing Parameters for Human Action Recognition 人体动作识别的感知与处理参数评价

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.56

Xiao Bo, Alan Huebner, C. Poellabauer, Megan K. O’Brien, C. Mummidisetty, A. Jayaraman

引用次数: 3

Fatigue Detection Model for Older Adults Using Eye-Tracking Data Gathered While Watching Video: Evaluation Against Diverse Fatiguing Tasks 老年人观看视频时眼动追踪数据的疲劳检测模型:对不同疲劳任务的评估

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.74

Yasunori Yamada, Masatomo Kobayashi

{"title":"Fatigue Detection Model for Older Adults Using Eye-Tracking Data Gathered While Watching Video: Evaluation Against Diverse Fatiguing Tasks","authors":"Yasunori Yamada, Masatomo Kobayashi","doi":"10.1109/ICHI.2017.74","DOIUrl":"https://doi.org/10.1109/ICHI.2017.74","url":null,"abstract":"Monitoring mental fatigue has become important for improving cognitive performance and health outcomes especially for older adults. Previous models using eye-tracking data allow inference of fatigue during cognitive tasks, such as driving, but they require us to engage in specific cognitive tasks. A model capable of inferring fatigue in natural-viewing situations when individuals are not performing cognitive tasks would help monitor mental fatigue in everyday situations. Moreover, although eyetracking measures exhibit age-related changes, previous models were mainly tested by user groups that did not include older adults. Here, we present a fatigue-detection model including (i) novel feature sets to better capture mental fatigue in naturalviewing situations and (ii) multiple fatigue-detection classifiers of each estimated age group to make it robust to the target’s age. To test our model, we collected eye-tracking data from younger and older adults as they watched video clips before and after performing cognitive tasks. Our model improved accuracy by up to 22.3% compared with a model based on the previous studies, and it achieved 99.4% accuracy. Furthermore, after it was trained using the eye-tracking data before and after cognitive tasks, our model could detect increased mental fatigue of full-time workers after their work with 92.6% accuracy.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"57 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":"116265549","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: 14

Deep Learning Based Recognition of Meltdown in Autistic Kids 基于深度学习的自闭症儿童崩溃识别

2017 IEEE International Conference on Healthcare Informatics (ICHI) Pub Date : 2017-08-01 DOI: 10.1109/ICHI.2017.35

Venkata Sindhoor Preetham Patnam, F. George, K. George, Abhishek Verma

{"title":"Deep Learning Based Recognition of Meltdown in Autistic Kids","authors":"Venkata Sindhoor Preetham Patnam, F. George, K. George, Abhishek Verma","doi":"10.1109/ICHI.2017.35","DOIUrl":"https://doi.org/10.1109/ICHI.2017.35","url":null,"abstract":"Children with autism often experience sudden meltdowns which not only makes the moment tough for the caretakers/parents but also make the children hurt themselves physically. Studies have discovered that children with autistic spectrum disorder exhibit certain actions through which we can anticipate mutilating meltdowns in them. The objective of our project is to build a system that can recognize such kind of actions using deep learning techniques thereby, notifying the caretakers/parents so that they can get the situation under control in lesser time. Using deep learning RCNNs, we can train the system faster yet reliable because unlike all the machine learning algorithms, deep learning algorithms are more efficient and have more scope into future. We have trained a classifier on images that are gathered from videos and reliable internet sources with most predictive gestures, through which we can detect the meltdowns more precisely. We have trained a model that validated the accuracy by ~93% which is accompanied by a loss/train classifier with a minimal 0.4% loss. Functional testing was done through feeding the deep neural network with chosen actions performed by five individuals that resulted in an accuracy of ~92% in all cases, which can assure the real-time usage of the system.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"14 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":"123923956","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: 12