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

{"title":"Granite: Diversified, Sparse Tensor Factorization for Electronic Health Record-Based Phenotyping","authors":"Jette Henderson, Joyce Ho, A. Kho, J. Denny, B. Malin, Jimeng Sun, Joydeep Ghosh","doi":"10.1109/ICHI.2017.61","DOIUrl":"https://doi.org/10.1109/ICHI.2017.61","url":null,"abstract":"One of the most formidable challenges electronic health records (EHRs) pose for traditional analytics is the inability to map directly (or reliably) to medical concepts or phenotypes. Among other things, EHR-based phenotyping can help identify and target patients for interventions and improve real-time clinical decisions. Existing phenotyping approaches often require labor-intensive supervision from medical experts or do not focus on generating concise and diverse phenotypes. Sparsity in phenotypes is key to making them interpretable and useful to clinicians, while diversity allows clinicians to grasp the main features of a patient population quickly.In this paper, we introduce Granite, a diversified, sparse nonnegative tensor factorization method to derive phenotypes with limited human supervision. Compared to existing high-throughput phenotyping techniques, Granite yields phenotypes with much more distinct (non-overlapping) elements that can, as an artifact, capture rare phenotypes. Moreover, the resulting concise phenotypes retain predictive powers comparable to or surpassing existing dimensionality reduction techniques. We evaluate Granite by comparing its resulting phenotypes with those generated using state-of-the-art, high-throughput methods on simulated as well as real EHR data. Our algorithm offers a promising and novel data-driven solution to rapidly characterize, predict, and manage a wide range of diseases.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130168941","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}

{"title":"Towards Identifying Informal Caregivers of Alzheimer’s and Dementia Patients in Social Media","authors":"Reda Al-Bahrani, M. Danilovich, W. Liao, A. Choudhary, Ankit Agrawal","doi":"10.1109/ICHI.2017.26","DOIUrl":"https://doi.org/10.1109/ICHI.2017.26","url":null,"abstract":"An informal caregiver is a family member, friend, or neighbor who provides assistance to an older adult. Informal caregiving is associated with increased physical, mental, and emotional stressor contributing to poor health outcomes, and caregiver burnout. This project focuses on tracking and analyzing informal caregivers in Twitter.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126610226","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}

{"title":"Detecting and Treating Mental Illness on Social Networks","authors":"Akkapon Wongkoblap, Miguel A. Vadillo, V. Curcin","doi":"10.1109/ICHI.2017.24","DOIUrl":"https://doi.org/10.1109/ICHI.2017.24","url":null,"abstract":"Mental illness is becoming a serious global health problem worldwide, with a growing number of patients suffering from depression, anxiety and other disorders. New solutions are needed to tackle this issue. The main goal of this research project is to develop prediction models to classify users with poor mental health from social network data and then implement an intervention model to help these users.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301387","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}

{"title":"Detecting Key Drivers for Long Length of Stay in Emergency Rooms","authors":"Eran Simhon, Yugang Jia","doi":"10.1109/ICHI.2017.81","DOIUrl":"https://doi.org/10.1109/ICHI.2017.81","url":null,"abstract":"Reducing length of stay (LOS) in the emergency department (ED) has been a challenge for hospitals for many years. Patient’s LOS is affected by many factors such as medical, demographics and ED operations (i.e., availability of staff and other resources). In order to reduce LOS, the hospital management first needs to find the main drivers for long LOS. Due to the large number of factors influencing LOS, finding a specific cohort of patients which are likely to have long LOS is not a trivial task. In this work, we use Association rules to find relations between medical/operational factors and long LOS. We suggest several techniques to remove redundant rules. We validate our approach with a data set that includes 100,000 visits within two years from one hospital in the United States. This validation derives several actionable insights.","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":"125917022","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}

{"title":"Breast Cancer Risk Prediction Using Electronic Health Records","authors":"Yirong Wu, E. Burnside, Jennifer Cox, Jun Fan, Ming Yuan, Jie Yin, P. Peissig, Alexander G. Cobian, D. Page, M. Craven","doi":"10.1109/ICHI.2017.62","DOIUrl":"https://doi.org/10.1109/ICHI.2017.62","url":null,"abstract":"Electronic health records (EHRs) represent an underused data source that has great research and clinical potential. Our goal was to quantify the value of EHRs in breast cancer risk prediction. We conducted a retrospective case-control study, gathering patients' ICD-9 diagnosis codes from an existing EHR data repository. Based on the hierarchical structure of ICD-9 codes, which are composed of 3-5 digits, three levels of data representation were studied: level 0, using only the first 3 digits; level 1, using up to the first 4 digits; and level 2, using up to the full 5 digits of each code. We created two models to predict breast cancer one year in advance based on diagnosis codes in three levels of data representation: logistic regression (LR) and LASSO logistic regression (LR+Lasso). Area under the ROC curve (AUC) was used to assess model performance. The LR+Lasso model demonstrated significantly higher predictive performance than the LR model when using the level 2 feature representation (0.648 vs 0.603, p=0.013). For both the level 1 representation and the level 0 representation, the predictive difference between LR+Lasso and LR model was not significant, (0.634 vs 0.604, p=0.081) and (0.612 vs 0.603, p=0.523), respectively. For LR model, predictive performance changed modestly across three levels. For LR+Lasso model, predictive performance also changed modestly from the level 0 to the level 1representation (p=0.168) and from the level 1 to the level 2 representation (p=0.374). However, the level 2 representation provided significantly higher predictive performance than the level 0 representation (p=0.034). The unabridged level 2 representation of the diagnosis codes contains the most valuable information that may contribute to breast cancer risk prediction. The performance of these models demonstrates that EHR data can be used to predict breast cancer risk, which provides the possibility to personalize care in clinical practice. In the future, we will combine coded EHR data with demographic risk factors, genetic variants, and imaging features to improve breast cancer risk prediction.","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":"126195205","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}

{"title":"Detecting Cognitive Distortions Through Machine Learning Text Analytics","authors":"Taetem Simms, Clayton Ramstedt, Megan Rich, Michael Richards, T. Martinez, C. Giraud-Carrier","doi":"10.1109/ICHI.2017.39","DOIUrl":"https://doi.org/10.1109/ICHI.2017.39","url":null,"abstract":"Machine learning and text analytics have proven increasingly useful in a number of health-related applications, particularly in the context of analyzing online data for disease epidemics and warning signs of a variety of mental health issues. We follow in this tradition here, but focus our attention on cognitive distortion, a precursor and symptom of disruptive psychological disorders such as anxiety, anorexia and depression. We collected a number of personal blogs from the Tumblr API, and labeled them based on whether they exhibited distorted thought patterns. We then used LIWC to extract textual features and applied machine learning to the resulting vectors. Our findings show that it is possible to detect cognitive distortions automatically from personal blogs with relatively good accuracy (73.0%) and false negative rate (30.4%).","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"13 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":"114951258","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}

{"title":"Medical Concept Normalization for Online User-Generated Texts","authors":"Kathy Lee, Sadid A. Hasan, Oladimeji Farri, A. Choudhary, Ankit Agrawal","doi":"10.1109/ICHI.2017.59","DOIUrl":"https://doi.org/10.1109/ICHI.2017.59","url":null,"abstract":"Social media has become an important tool for sharing content in the last decade. People often talk about their experiences and opinions on different health-related issues e.g. they write reviews on medications, describe symptoms and ask informal questions about various health concerns. Due to the colloquial nature of the languages used in the social media, it is often difficult for an automated system to accurately interpret them for appropriate clinical understanding. To address this challenge, this paper proposes a novel approach for medical concept normalization of user-generated texts to map a health condition described in the colloquial language to a medical concept defined in standard clinical terminologies. We use multiple deep learning architectures such as convolutional neural networks (CNN) and recurrent neural networks (RNN) with input word embeddings trained on various clinical domain-specific knowledge sources. Extensive experiments on two benchmark datasets demonstrate that the proposed models can achieve up to 21.28% accuracy improvements over the existing models when we use the combination of all knowledge sources to learn neural embeddings.","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":"122384878","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}

{"title":"Assessing STAMP EMR with Electronic Medical Record Related Incident Reports: Case Study: Manufacturer and User Facility Device Experience Database","authors":"F. Mason-Blakley, R. Habibi, J. Weber, Morgan Price","doi":"10.1109/ICHI.2017.97","DOIUrl":"https://doi.org/10.1109/ICHI.2017.97","url":null,"abstract":"At the turn of the millennium the institute of medicine (IOM) discovered that medical error was responsible for the deaths of as many as 98000 Americans each year. In response to this discovery they recommended the implementation of a wide range of Health Information and Communication Technology (HICT) including electronic medical records (EMRs). In spite of the broad based adherence of practitioners to these recommendation, the Agency for Healthcare Research and Quality (AHRQ) has not only failed to observe an improvement in the rate of errors, they have in fact observed the opposite. We propose that this failing arises from a fundamental misunderstanding of error which may be remedied by the application of a system theoretic framework. We propose a previously evaluated system model developed using the principles of the System Theoretic Accidents Models and Process (STAMP) framework for this purpose. In this work we aim to further assess the value of our model, STAMP EMR, by investigating the degree to which it aligns with the hazards and accidents reported in incident reports. We perform an initial coding of a corpus of incident reports against STAMP EMR as an a priori model. We analyze the raw results and further provide a multiple coordination analysis (MCA). We find firstly that no reports in the MAUDE could not be coded using the STAMP EMR model. We find secondly that four primary clusters of contributing factors are represented for the reports - validation, verification, engineering management and clinical management issues.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"46 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":"122176455","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}

{"title":"Bridging the Gap: A Reference Information Exchange Architecture for Fusion Imaging","authors":"Max Taggart, M. Evans, G. Fiol, Derek Mann, M. Leavitt","doi":"10.1109/ICHI.2017.52","DOIUrl":"https://doi.org/10.1109/ICHI.2017.52","url":null,"abstract":"Fusion imaging promises to improve medical diagnosisand treatment by combining data from multiple imaging modalities, thus overcoming the limitations of any single modality. However, fusion imaging presents new technical challenges stemming from the fact that existing information exchange architectures may not support the collection and processing of data that is produced at different times and in different locations. There is a need for new information exchange architectures that address the unique demands of fusion imaging, and that can facilitate the use of fusion imaging processes to improve health outcomes. Using fusion prostate biopsy as a use case, this paper proposes a reference information exchange architecture to support the collection, processing, and dissemination of imaging data. It also briefly discusses a novel imaging product produced using a simple implementation of the architecture. Specifically, the product is an interactive three-dimensional rendering that overlays pathology results onto radiology data with high spatial fidelity. This prototype visualization highlights the potential for using fusion imaging data to produce novel imaging products with desirable diagnostic properties.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"2 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":"129416491","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}

{"title":"Creating a Labeled Dataset for Medical Misinformation in Health Forums","authors":"Alexander Kinsora, Kate Barron, Q. Mei, V.G.Vinod Vydiswaran","doi":"10.1109/ICHI.2017.93","DOIUrl":"https://doi.org/10.1109/ICHI.2017.93","url":null,"abstract":"The dissemination of medical misinformation online presents a challenge to human health. Machine learning techniques provide a unique opportunity for decreasing the cognitive load associated with deciding upon whether any given user comment is likely to contain misinformation, but a paucity of labeled data of medical misinformation makes supervised approaches a challenge. In order to ameliorate this condition, we present a new labeled dataset of misinformative and non-misinformative comments developed over posted questions and comments on a health discussion forum. This required extraction of candidate misinformative entries from the corpus using information retrieval techniques, development of a codex and labeling strategy for the dataset, and the creation of features for use in machine learning tasks. By identifying the nine most descriptive features with regard to classification as misinformative or non-misinformative through the use of Recursive Feature Elimination, we achieved a classification accuracy of 90.1%, where the dataset is comprised 85.8% of non-misinformative comments. In our opinion, this dataset and analysis will aid the machine learning community in the development of an online misinformation classification system over user-generated content such as medical forum posts.","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":"129880630","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}