SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining最新文献_第8页

Introduction to the special section on clinical data mining 介绍临床数据挖掘的特殊部分

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408738

Shipeng Yu, R. B. Rao

引用次数: 1

Next challenges for adaptive learning systems 适应性学习系统的下一个挑战

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408746

I. Žliobaitė, A. Bifet, M. Gaber, B. Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial

{"title":"Next challenges for adaptive learning systems","authors":"I. Žliobaitė, A. Bifet, M. Gaber, B. Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial","doi":"10.1145/2408736.2408746","DOIUrl":"https://doi.org/10.1145/2408736.2408746","url":null,"abstract":"Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"67 1","pages":"48-55"},"PeriodicalIF":0.0,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85722690","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: 96

Mining anatomical, physiological and pathological information from medical images 从医学图像中挖掘解剖、生理和病理信息

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408741

X. Zhou, Y. Zhan, V. Raykar, G. Hermosillo, L. Bogoni, Zhigang Peng

引用次数: 5

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408744

Guozhu Dong

引用次数: 10

Supervised patient similarity measure of heterogeneous patient records 异质病人记录的监督病人相似度测量

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408740

Jimeng Sun, Fei Wang, Jianying Hu, Shahram Edabollahi

{"title":"Supervised patient similarity measure of heterogeneous patient records","authors":"Jimeng Sun, Fei Wang, Jianying Hu, Shahram Edabollahi","doi":"10.1145/2408736.2408740","DOIUrl":"https://doi.org/10.1145/2408736.2408740","url":null,"abstract":"Patient similarity assessment is an important task in the context of patient cohort identif cation for comparative effectiveness studies and clinical decision support applications. The goal is to derive clinically meaningful distance metric to measure the similarity between patients represented by their key clinical indicators. How to incorporate physician feedback with regard to the retrieval results? How to interactively update the underlying similarity measure based on the feedback? Moreover, often different physicians have different understandings of patient similarity based on their patient cohorts. The distance metric learned for each individual physician often leads to a limited view of the true underlying distance metric. How to integrate the individual distance metrics from each physician into a globally consistent unif ed metric?\u0000 We describe a suite of supervised metric learning approaches that answer the above questions. In particular, we present Locally Supervised Metric Learning (LSML) to learn a generalized Mahalanobis distance that is tailored toward physician feedback. Then we describe the interactive metric learning (iMet) method that can incrementally update an existing metric based on physician feedback in an online fashion. To combine multiple similarity measures from multiple physicians, we present Composite Distance Integration (Comdi) method. In this approach we f rst construct discriminative neighborhoods from each individual metrics, then combine them into a single optimal distance metric. Finally, we present a clinical decision support prototype system powered by the proposed patient similarity methods, and evaluate the proposed methods using real EHR data against several baselines.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"9 1","pages":"16-24"},"PeriodicalIF":0.0,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90484109","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: 150

Data mining methodologies for pharmacovigilance 药物警戒的数据挖掘方法

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-10 DOI: 10.1145/2408736.2408742

Mei Liu, M. Matheny, Yong Hu, Hua Xu

{"title":"Data mining methodologies for pharmacovigilance","authors":"Mei Liu, M. Matheny, Yong Hu, Hua Xu","doi":"10.1145/2408736.2408742","DOIUrl":"https://doi.org/10.1145/2408736.2408742","url":null,"abstract":"Medicines are designed to cure, treat, or prevent diseases; however, there are also risks in taking any medicine - particularly short term or long term adverse drug reactions (ADRs) can cause serious harm to patients. Adverse drug events have been estimated to cause over 700,000 emergency department visits each year in the United States. Thus, for medication safety, ADR monitoring is required for each drug throughout its life cycle, including early stages of drug design, different phases of clinical trials, and postmarketing surveillance. Pharmacovigilance (PhV) is the science that concerns with the detection, assessment, understanding and prevention of ADRs. In the pre-marketing stages of a drug, PhV primarily focuses on predicting potential ADRs using preclinical characteristics of the compounds (e.g., drug targets, chemical structure) or screening data (e.g., bioassay data). In the postmarketing stage, PhV has traditionally involved in mining spontaneous reports submitted to national surveillance systems. The research focus is currently shifting toward the use of data generated from platforms outside the conventional framework such as electronic medical records (EMRs), biomedical literature, and patient-reported data in online health forums. The emerging trend of PhV is to link preclinical data from the experimental platform with human safety information observed in the postmarketing phase. This article provides a general overview of the current computational methodologies applied for PhV at different stages of drug development and concludes with future directions and challenges.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"156 1","pages":"35-42"},"PeriodicalIF":0.0,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76575345","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: 32

Discovering interesting information with advances in web technology 利用先进的网络技术发现有趣的信息

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-12-01 DOI: 10.1145/2481244.2481255

R. Nayak, P. Senellart, Fabian M. Suchanek, A. Varde

引用次数: 14

Sparse Methods for Biomedical Data. 生物医学数据稀疏方法。

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-06-01 DOI: 10.1145/2408736.2408739

Jieping Ye, Jun Liu

{"title":"Sparse Methods for Biomedical Data.","authors":"Jieping Ye, Jun Liu","doi":"10.1145/2408736.2408739","DOIUrl":"10.1145/2408736.2408739","url":null,"abstract":"<p><p>Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the [Formula: see text] norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data.</p>","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"14 1","pages":"4-15"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783968/pdf/nihms-497380.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31767264","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}

引用次数: 0

A conversation with Professor Bo Zhang 与张博教授的对话

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-05-01 DOI: 10.1145/2207243.2207266

Bo Zhang

{"title":"A conversation with Professor Bo Zhang","authors":"Bo Zhang","doi":"10.1145/2207243.2207266","DOIUrl":"https://doi.org/10.1145/2207243.2207266","url":null,"abstract":"In the 11 international joint conference on artificial intelligence (IJCAI’89), in 1989, Piatetsky-Shapiro and colleagues led a seminar on knowledge discovery in databases, which signaled the coming of age for the field of data mining. Soon after, in 1993, data mining research officially begun by first being supported by the National Natural Science Foundation of China (NSFC). This was viewed as the beginning of data mining research in China. Thus, China was among the pioneers in data mining research given that the time gap was only 4 years. Afterwards, major research activities on both fundamental research and practical applications can be found in many research institutions and universities, including Tsinghua University, Peking University, Institute of Computing Technology in Chinese Academy of Science, Nanjing University, and many others. In these institutions, applications of data mining cover a broad range of areas including finance, business, transportation, medicine, energy, etc. However, before the 21 century, due to the shortage of data, China’s KDD research was still limited in terms of theoretical depth and types of applications. This period can be considered a warm-up period, in which Chinese researchers were learning and following international major KDD research directions.","PeriodicalId":90050,"journal":{"name":"SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining","volume":"25 1","pages":"96-98"},"PeriodicalIF":0.0,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85524743","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 conversation with Professor Li-Zhu Zhou 与周立柱教授的对话

SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Pub Date : 2012-05-01 DOI: 10.1145/2207243.2207267

Lizhu Zhou

引用次数: 0