Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics最新文献

{"title":"Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction.","authors":"Peng Wu, Tianchen Xu, Yuanjia Wang","doi":"10.1109/dsaa.2019.00054","DOIUrl":"10.1109/dsaa.2019.00054","url":null,"abstract":"<p><p>To address substantial heterogeneity in patient response to treatment of chronic disorders and achieve the promise of precision medicine, individualized treatment rules (ITRs) are estimated to tailor treatments according to patient-specific characteristics. Randomized controlled trials (RCTs) provide gold standard data for learning ITRs not subject to confounding bias. However, RCTs are often conducted under stringent inclusion/exclusion criteria, and participants in RCTs may not reflect the general patient population. Thus, ITRs learned from RCTs lack generalizability to the broader real world patient population. Real world databases such as electronic health records (EHRs) provide new resources as complements to RCTs to facilitate evidence-based research for personalized medicine. However, to ensure the validity of ITRs learned from EHRs, a number of challenges including confounding bias and selection bias must be addressed. In this work, we propose a matching-based machine learning method to estimate optimal individualized treatment rules from EHRs using interpretable features extracted from EHR documentation of medications and ICD diagnoses codes. We use a latent Dirichlet allocation (LDA) model to extract latent topics and weights as features for learning ITRs. Our method achieves confounding reduction in observational studies through matching treated and untreated individuals and improves treatment optimization by augmenting feature space with clinically meaningful LDA-based features. We apply the method to EHR data collected at New York Presbyterian Hospital clinical data warehouse in studying optimal second-line treatment for type 2 diabetes (T2D) patients. We use cross validation to show that ITRs outperforms uniform treatment strategies (i.e., assigning same treatment to all individuals), and including topic modeling features leads to more reduction of post-treatment complications.</p>","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7035126/pdf/nihms-1557992.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37670824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data.","authors":"Eun Jeong Min,&nbsp;Changgee Chang,&nbsp;Qi Long","doi":"10.1109/DSAA.2018.00021","DOIUrl":"10.1109/DSAA.2018.00021","url":null,"abstract":"Integrative clustering is a clustering approach for multiple datasets, which provide different views of a common group of subjects. It enables analyzing multi-omics data jointly to, for example, identify the subtypes of diseases, cells, and so on, capturing the complex underlying biological processes more precisely. On the other hand, there has been a great deal of interest in incorporating the prior structural knowledge on the features into statistical analyses over the past decade. The knowledge on the gene regulatory network (pathways) can potentially be incorporated into many genomic studies. In this paper, we propose a novel integrative clustering method which can incorporate the prior graph knowledge. We first develop a generalized Bayesian factor analysis (GBFA) framework, a sparse Bayesian factor analysis which can take into account the graph information. Our GBFA framework employs the spike and slab lasso (SSL) prior to impose sparsity on the factor loadings and the Markov random field (MRF) prior to encourage smoothing over the adjacent factor loadings, which establishes a unified shrinkage adaptive to the loading size and the graph structure. Then, we use the framework to extend iCluster+, a factor analysis based integrative clustering approach. A novel variational EM algorithm is proposed to efficiently estimate the MAP estimator for the factor loadings. Extensive simulation studies and the application to the NCI60 cell line dataset demonstrate that the propose method is superior and delivers more biologically meaningful outcomes.","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/DSAA.2018.00021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37253590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options.","authors":"Xuan Zhou, Yuanjia Wang, Donglin Zeng","doi":"10.1109/DSAA.2018.00072","DOIUrl":"10.1109/DSAA.2018.00072","url":null,"abstract":"<p><p>To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the largest benefit can be considered. Recently, a machine learning approach, O-learning, has been proposed to estimate an optimal individualized treatment rule (ITR), but it is developed to make binary decisions and thus limited to compare two treatments. When many treatment options are available, existing methods need to be adapted by transforming a multiple treatment selection problem into multiple binary treatment selections, for example, via one-vs-one or one-vs-all comparisons. However, combining multiple binary treatment selection rules into a single decision rule requires careful consideration, because it is known in the multicategory learning literature that some approaches may lead to ambiguous decision rules. In this work, we propose a novel and efficient method to generalize outcome-weighted learning for binary treatment to multi-treatment settings. We solve a multiple treatment selection problem via sequential weighted support vector machines. We prove that the resulting ITR is Fisher consistent and obtain the convergence rate of the estimated value function to the true optimal value, i.e., the estimated treatment rule leads to the maximal benefit when the data size goes to infinity. We conduct simulations to demonstrate that the proposed method has superior performance in terms of lower mis-allocation rates and improved expected values. An application to a three-arm randomized trial of major depressive disorder shows that an ITR tailored to individual patient's expectancy of treatment efficacy, their baseline depression severity and other characteristics reduces depressive symptoms more than non-personalized treatment strategies (e.g., treating all patients with combined pharmacotherapy and psychotherapy).</p>","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437674/pdf/nihms-1009424.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37107047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach for Estimating Multiple Sparse Precision Matrices Using ℓ0, 0 Regularization","authors":"Phan Duy Nhat, Hoai An Le Thi","doi":"10.1109/DSAA.2017.40","DOIUrl":"https://doi.org/10.1109/DSAA.2017.40","url":null,"abstract":"","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73172584","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":"The Highly Adaptive Lasso Estimator.","authors":"David Benkeser, Mark van der Laan","doi":"10.1109/DSAA.2016.93","DOIUrl":"10.1109/DSAA.2016.93","url":null,"abstract":"<p><p>Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical process theory, we establish a fast minimal rate of convergence of our proposed estimator and illustrate how such an estimator can be constructed using standard software. In simulations, we show that the finite-sample performance of our estimator is competitive with other popular machine learning techniques across a variety of data generating mechanisms. We also illustrate competitive performance in real data examples using several publicly available data sets.</p>","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662030/pdf/nihms870895.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35563483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward personal knowledge bases","authors":"S. Abiteboul","doi":"10.1109/DSAA.2015.7344775","DOIUrl":"https://doi.org/10.1109/DSAA.2015.7344775","url":null,"abstract":"A Web user today has his/her data and information distributed in a number of services that operate in silos. Computer wizards already know how to control their personal data to some extent. It is now becoming possible for everyone to do the same, and there are many advantages to doing so. Everyone should now be in a position to manage his/her personal information. Furthermore, we will argue that we should move towards personal knowledge bases and discuss advantages to do so. We will mention recent works around a datalog dialect, namely Webdamlog.","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79019885","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":"Special session on trends & controversies in data science (TCDS)","authors":"F. Forbes, Wray L. Buntine","doi":"10.1109/DSAA.2015.7344776","DOIUrl":"https://doi.org/10.1109/DSAA.2015.7344776","url":null,"abstract":"As an emerging area, data science is facing great opportunities as well as challenges. Often arguments exist: What is data science? Why data science? We have information science already, why do we need data science? Do we need analytics science? Is analytics new? What is the difference between statistics and data analytics? What makes a data scientist? We believe that a special session on Trends and Controversy about data science and advanced analytics could bring insights from different mindsets for the healthy development of the science and society. Accordingly, this T&C special session will host talks by invitation to outline different views about today and future of data science. Invited speakers can contribute a paper (in the same format as the main conference submissions but could be less than 10 pages) to the special session, which will be handled by program co-chairs and accepted into the main conference proceeding probably by addressing comments from the program cochairs.","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82819738","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":"The sexy job in the next ten years will be statisticians","authors":"É. Moulines","doi":"10.1109/DSAA.2015.7344777","DOIUrl":"https://doi.org/10.1109/DSAA.2015.7344777","url":null,"abstract":"The goal of exploratory data analysis or data mining is making sense of data. We develop theory and algorithms that help us understand our data, with the goal that this helps formulating better hypotheses. The role of statisticians is to provide methods that give detailed insight in how data is structured: characterising distributions in easily understandable terms, showing the most informative patterns, associations, correlations, etc. Statisticians are part of the big data science wave but which part exactly next to data accessibility, data communication and visualization?","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91081288","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":"Welcome from DSAA 2014 chairs","authors":"Philip S. Yu, M. Kitsuregawa, H. Motoda, Bart Goethals, M. Guo, Longbing Cao, G. Karypis, Irwin King, Wei Wang","doi":"10.1109/DSAA.2014.7058034","DOIUrl":"https://doi.org/10.1109/DSAA.2014.7058034","url":null,"abstract":"Data driven scientific discovery approach has already been agreed to be an important emerging paradigm for computing in areas including social, service, Internet of Things (or sensor networks), and cloud. Under this paradigm, Big Data is the core that drives new researches in many areas, from environmental to social. There are many new scientific challenges when facing this big data phenomenon, ranging from capture, creation, storage, search, sharing, analysis, and visualization. The complication here is not just the storage, I/O, query, and performance, but also the integration across heterogeneous, interdependent complex data resources for real-time decision-making, collaboration, and ultimately value co-creation. Data sciences encompass the larger areas of data analytics, machine learning and managing big data. Advanced data analytics has become essential to glean a deep understanding of large data sets and to convert data into actionable intelligence. With the rapid growth in the volumes of data available to enterprises, Government and on the web, automated techniques for analyzing the data have become essential.","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82806771","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":"Similarity analysis of service descriptions for efficient Web service discovery","authors":"S. SowmyaKamath, S. AnanthanarayanaV.","doi":"10.1109/DSAA.2014.7058065","DOIUrl":"https://doi.org/10.1109/DSAA.2014.7058065","url":null,"abstract":"","PeriodicalId":92122,"journal":{"name":"Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77089068","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}