KDD : proceedings. International Conference on Knowledge Discovery & Data Mining最新文献_第2页

A Free Energy Based Approach for Distance Metric Learning. 基于自由能的距离度量学习方法。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-01 DOI: 10.1145/3292500.3330975

Sho Inaba, Carl T Fakhry, Rahul V Kulkarni, Kourosh Zarringhalam

{"title":"A Free Energy Based Approach for Distance Metric Learning.","authors":"Sho Inaba, Carl T Fakhry, Rahul V Kulkarni, Kourosh Zarringhalam","doi":"10.1145/3292500.3330975","DOIUrl":"https://doi.org/10.1145/3292500.3330975","url":null,"abstract":"We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. This formulation leads to a mapping to statistical mechanics such that the metric learning optimization problem becomes equivalent to free energy minimization. Correspondingly, our approach leads to an analytical solution of the optimization problem based on the Boltzmann distribution. The mapping established in this work suggests new approaches for dimensionality reduction and provides insights into determination of optimal parameters for the penalty term. Furthermore, we demonstrate that the metric projects the data onto direction of maximum dissimilarity with optimal and tunable separation between classes and thus the transformation can be used for high dimensional data visualization, classification, and clustering tasks. We benchmark our method against previous distance learning methods and provide an efficient implementation in an R package available to download at: https://github.com/kouroshz/fenn.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2019 ","pages":"5-13"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3292500.3330975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25467271","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: 4

Retaining Privileged Information for Multi-Task Learning. 为多任务学习保留特权信息。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2019-07-01 DOI: 10.1145/3292500.3330907

Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, Li-Wei H Lehman

{"title":"Retaining Privileged Information for Multi-Task Learning.","authors":"Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, Li-Wei H Lehman","doi":"10.1145/3292500.3330907","DOIUrl":"https://doi.org/10.1145/3292500.3330907","url":null,"abstract":"Knowledge transfer has been of great interest in current machine learning research, as many have speculated its importance in modeling the human ability to rapidly generalize learned models to new scenarios. Particularly in cases where training samples are limited, knowledge transfer shows improvement on both the learning speed and generalization performance of related tasks. Recently, Learning Using Privileged Information (LUPI) has presented a new direction in knowledge transfer by modeling the transfer of prior knowledge as a Teacher-Student interaction process. Under LUPI, a Teacher model uses Privileged Information (PI) that is only available at training time to improve the sample complexity required to train a Student learner for a given task. In this work, we present a LUPI formulation that allows privileged information to be retained in a multi-task learning setting. We propose a novel feature matching algorithm that projects samples from the original feature space and the privilege information space into a joint latent space in a way that informs similarity between training samples. Our experiments show that useful knowledge from PI is maintained in the latent space and greatly improves the sample efficiency of other related learning tasks. We also provide an analysis of sample complexity of the proposed LUPI method, which under some favorable assumptions can achieve a greater sample efficiency than brute force methods.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2019 ","pages":"1369-1377"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3292500.3330907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39890793","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: 12

Voxel Deconvolutional Networks for 3D Brain Image Labeling. 用于三维脑图像标注的体素去卷积网络

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2018-08-01 DOI: 10.1145/3219819.3219974

Yongjun Chen, Min Shi, Hongyang Gao, Dinggang Shen, Lei Cai, Shuiwang Ji

{"title":"Voxel Deconvolutional Networks for 3D Brain Image Labeling.","authors":"Yongjun Chen, Min Shi, Hongyang Gao, Dinggang Shen, Lei Cai, Shuiwang Ji","doi":"10.1145/3219819.3219974","DOIUrl":"10.1145/3219819.3219974","url":null,"abstract":"Deep learning methods have shown great success in pixel-wise prediction tasks. One of the most popular methods employs an encoder-decoder network in which deconvolutional layers are used for up-sampling feature maps. However, a key limitation of the deconvolutional layer is that it suffers from the checkerboard artifact problem, which harms the prediction accuracy. This is caused by the independency among adjacent pixels on the output feature maps. Previous work only solved the checkerboard artifact issue of deconvolutional layers in the 2D space. Since the number of intermediate feature maps needed to generate a deconvolutional layer grows exponentially with dimensionality, it is more challenging to solve this issue in higher dimensions. In this work, we propose the voxel deconvolutional layer (VoxelDCL) to solve the checkerboard artifact problem of deconvolutional layers in 3D space. We also provide an efficient approach to implement VoxelDCL. To demonstrate the effectiveness of VoxelDCL, we build four variations of voxel deconvolutional networks (VoxelDCN) based on the U-Net architecture with VoxelDCL. We apply our networks to address volumetric brain images labeling tasks using the ADNI and LONI LPBA40 datasets. The experimental results show that the proposed iVoxelDCNa achieves improved performance in all experiments. It reaches 83.34% in terms of dice ratio on the ADNI dataset and 79.12% on the LONI LPBA40 dataset, which increases 1.39% and 2.21% respectively compared with the baseline. In addition, all the variations of VoxelDCN we proposed outperform the baseline methods on the above datasets, which demonstrates the effectiveness of our methods.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2018 ","pages":"1226-1234"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426146/pdf/nihms-988372.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37087638","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

Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time. 通过捕捉疾病随时间的进展为医疗保健提供可解释的表征学习。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2018-08-01 DOI: 10.1145/3219819.3219904

Tian Bai, Brian L Egleston, Shanshan Zhang, Slobodan Vucetic

{"title":"Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time.","authors":"Tian Bai, Brian L Egleston, Shanshan Zhang, Slobodan Vucetic","doi":"10.1145/3219819.3219904","DOIUrl":"10.1145/3219819.3219904","url":null,"abstract":"Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. Based on the observation that different patient conditions have different temporal progression patterns, in this paper we propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. This allows the Timeline to learn that chronic conditions have a longer lasting impact on future visits than acute conditions. Timeline also has an attention mechanism that improves vector embeddings of visits. By analyzing the attention weights and disease progression functions of Timeline, it is possible to interpret the predictions and understand how risks of future visits change over time. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. In addition, we demonstrate that time decay factors and attentions learned by Timeline are in accord with the medical knowledge and that Timeline can provide a useful insight into its predictions.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2018 ","pages":"43-51"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6484836/pdf/nihms-1019542.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37198313","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

Generalized Score Functions for Causal Discovery. 因果发现的广义得分函数。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2018-08-01 DOI: 10.1145/3219819.3220104

Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour

{"title":"Generalized Score Functions for Causal Discovery.","authors":"Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour","doi":"10.1145/3219819.3220104","DOIUrl":"10.1145/3219819.3220104","url":null,"abstract":"Discovery of causal relationships from observational data is a fundamental problem. Roughly speaking, there are two types of methods for causal discovery, constraint-based ones and score-based ones. Score-based methods avoid the multiple testing problem and enjoy certain advantages compared to constraint-based ones. However, most of them need strong assumptions on the functional forms of causal mechanisms, as well as on data distributions, which limit their applicability. In practice the precise information of the underlying model class is usually unknown. If the above assumptions are violated, both spurious and missing edges may result. In this paper, we introduce generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes. In particular, we exploit regression in RKHS to capture the dependence in a non-parametric way. The resulting causal discovery approach produces asymptotically correct results in rather general cases, which may have nonlinear causal mechanisms, a wide class of data distributions, mixed continuous and discrete data, and multidimensional variables. Experimental results on both synthetic and real-world data demonstrate the efficacy of our proposed approach.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2018 ","pages":"1551-1560"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3219819.3220104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36470229","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: 99

SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping. SUSTain:张量的可伸缩无监督评分及其在表型中的应用。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2018-07-01 DOI: 10.1145/3219819.3219999

Ioakeim Perros, Evangelos E Papalexakis, Haesun Park, Richard Vuduc, Xiaowei Yan, Christopher Defilippi, Walter F Stewart, Jimeng Sun

{"title":"SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping.","authors":"Ioakeim Perros, Evangelos E Papalexakis, Haesun Park, Richard Vuduc, Xiaowei Yan, Christopher Defilippi, Walter F Stewart, Jimeng Sun","doi":"10.1145/3219819.3219999","DOIUrl":"https://doi.org/10.1145/3219819.3219999","url":null,"abstract":"This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the values correspond to event counts or ordinal measures. The conventional approach is to treat integer data as real, and then apply real-valued factorizations. However, doing so fails to preserve important characteristics of the original data, thereby making it hard to interpret the results. Instead, our approach extracts factor values from integer datasets as scores that are constrained to take values from a small integer set. These scores are easy to interpret: a score of zero indicates no feature contribution and higher scores indicate distinct levels of feature importance. At its core, SUSTain relies on: a) a problem partitioning into integer-constrained subproblems, so that they can be optimally solved in an efficient manner; and b) organizing the order of the subproblems' solution, to promote reuse of shared intermediate results. We propose two variants, SUSTain M and SUSTain T , to handle both matrix and tensor inputs, respectively. We evaluate SUSTain against several state-of-the-art baselines on both synthetic and real Electronic Health Record (EHR) datasets. Comparing to those baselines, SUSTain shows either significantly better fit or orders of magnitude speedups that achieve a comparable fit (up to 425× faster). We apply SUSTain to EHR datasets to extract patient phenotypes (i.e., clinically meaningful patient clusters). Furthermore, 87% of them were validated as clinically meaningful phenotypes related to heart failure by a cardiologist.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2018 ","pages":"2080-2089"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3219819.3219999","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25445214","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: 613

Network Inference via the Time-Varying Graphical Lasso. 基于时变图形套索的网络推理。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2017-08-01 DOI: 10.1145/3097983.3098037

David Hallac, Youngsuk Park, Stephen Boyd, Jure Leskovec

{"title":"Network Inference via the Time-Varying Graphical Lasso.","authors":"David Hallac, Youngsuk Park, Stephen Boyd, Jure Leskovec","doi":"10.1145/3097983.3098037","DOIUrl":"10.1145/3097983.3098037","url":null,"abstract":"Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2017 ","pages":"205-213"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3097983.3098037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36106209","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: 164

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices. 为嵌入式设备的异构网络学习树状结构检测级联。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2017-08-01 DOI: 10.1145/3097983.3098169

Hamid Dadkhahi, Benjamin M Marlin

{"title":"Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices.","authors":"Hamid Dadkhahi, Benjamin M Marlin","doi":"10.1145/3097983.3098169","DOIUrl":"10.1145/3097983.3098169","url":null,"abstract":"In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources. We concentrate on the problem of jointly learning the parameters for all of the classifiers in the cascade given a fixed cascade architecture and a known set of costs required to carry out the computation at each node. To accomplish the objective of joint learning of all detectors, we propose a novel approach to combining classifier outputs during training that better matches the hard cascade setting in which the learned system will be deployed. This work is motivated by research in the area of mobile health where energy efficient real time detectors integrating information from multiple wireless on-body sensors and a smart phone are needed for real-time monitoring and the delivery of just-in-time adaptive interventions. We evaluate our framework on mobile sensor-based human activity recognition and mobile health detector learning problems.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2017 ","pages":"1773-1781"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5765542/pdf/nihms928860.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35736377","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

Federated Tensor Factorization for Computational Phenotyping. 用于计算表型的联合张量因式分解。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2017-08-01 DOI: 10.1145/3097983.3098118

Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang

{"title":"Federated Tensor Factorization for Computational Phenotyping.","authors":"Yejin Kim, Jimeng Sun, Hwanjo Yu, Xiaoqian Jiang","doi":"10.1145/3097983.3098118","DOIUrl":"10.1145/3097983.3098118","url":null,"abstract":"Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2017 ","pages":"887-895"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652331/pdf/nihms880922.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35543676","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

GRAM: Graph-based Attention Model for Healthcare Representation Learning. 基于图的医疗表征学习注意模型。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2017-08-01 DOI: 10.1145/3097983.3098126

Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, Jimeng Sun

{"title":"GRAM: Graph-based Attention Model for Healthcare Representation Learning.","authors":"Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, Jimeng Sun","doi":"10.1145/3097983.3098126","DOIUrl":"10.1145/3097983.3098126","url":null,"abstract":"Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: Data insufficiency: Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results.Interpretation: The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose GRaph-based Attention Model (GRAM) that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2017 ","pages":"787-795"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954122/pdf/nihms-1675242.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25486805","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