Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data

2020 | International Journal of Information Management | Citations: 0

Authors: Liu, Xin; zhou, yanju; Zongrun, Wang

Abstract: The data-driven mode has promoted the researches of preventive medicine. In pred ... Expand

Abstract: The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction. Collapse

Topics: big data electronic health record missing data healthcare data information management

Methods: experiment regression analysis method machine learning computational algorithm support vector machine

Theories: measurement theory

Feature assessment and ranking for classification with nonlinear sparse representation and approximate dependence analysis

2019 | Decision Support Systems | Citations: 1

Authors: Zhang, Yishi; Zhang, Qi; Chen, Zhijun; Shang, Jennifer; Wei, Haiying

Abstract: Feature selection has received significant attention in knowledge management and ... Expand

Abstract: Feature selection has received significant attention in knowledge management and decision support systems in the past decades. In this study, kernel-based sparse representation and feature dependence analysis are integrated into a feature assessment and ranking framework. The proposed method utilizes the advantages of the kernel-based sparse representation technique and of the information theoretic metric to iteratively obtain the salient feature cluster. Then, a novel approximate dependence analysis is applied to further maintain complementarity while eliminating redundancy among the features selected by nonlinear orthogonal matching pursuit (NOMP). This can effectively prevent the significant bias caused by the pairwise correlation analysis for a large-scale feature set. To illustrate the effectiveness of the proposed method, classification experiments are conducted with three representative classifiers, on nine well-known datasets. The experimental results show the superiority of the proposed method compared with the representative information theoretic and model-based methods in classification for data-driven decision support systems. Collapse

Topics: decision making electronic mail email spam website Java

Methods: computational algorithm experiment random forest classification machine learning descriptive statistic

Theories: information theory

A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival

2016 | Decision Support Systems | Citations: 0

Authors: Dag, Ali; Topuz, Kazim; Oztekin, Asil; Bulur, Serkan; Megahed, Fadel M.

Abstract: Recent research has shown that data mining models can accurately predict the out ... Expand

Abstract: Recent research has shown that data mining models can accurately predict the outcome of a heart transplant based on predictors that include patient and donor's health/demographics. These models have not been adopted in practice, however, since they did not: a) consider the interactions between the explanatory variables; b) provide a patient's specific risk of survival (reported results have been primarily deterministic); and c) offer an automated decision tool that can provide some data-driven insights to practitioners. In this study, we attempt to overcome these three limitations through the use of Bayesian Belief Networks (BBN). The proposed BBN framework is comprised of four phases. In the first two phases, the data is preprocessed, and a candidate set of predictors is generated based on employing several variable selection methods. The third phase involves the addition of medically relevant variables to the list. In phase four, the BBN model is applied. The results show that the proposed BBN method provides similar predictive performance to the best approaches in the literature. More importantly, our method provides novel information on the interactions among the predictors and the conditional probability of survival for a given set of relevant donor–recipient characteristics. We offer U.S. practitioners a decision support tool that presents an individualized survival score based on our BBN model (and the UNOS dataset). Collapse

Topics: decision support system analytical information system database system explicit knowledge Linux

Methods: machine learning computational algorithm sensitivity analysis k-nearest neighbor proportional hazards model

A model-free scheme for meme ranking in social media

2016 | Decision Support Systems | Citations: 0

Authors: He, Saike; Zheng, Xiaolong; Zeng, Daniel

Abstract: The prevalence of social media has greatly catalyzed the dissemination and proli ... Expand

Abstract: The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, and tags). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence. Collapse

Topics: user behavior social network website viral marketing innovation management

Methods: experiment descriptive statistic literature study time series analysis longitudinal research

Theories: information theory

USING THE MUTUAL INFORMATION METRIC TO IMPROVE ACCESSIBILITY AND UNDERSTANDABILITY IN BUSINESS INTELLIGENCE TOOLS

2011 | European Conference On Information Systems | Citations: 0

Authors: Sabag, Yohai; Even, Adir

Abstract: The rapidly-growing organizational data resources introduce a growing difficulty ... Expand

Abstract: The rapidly-growing organizational data resources introduce a growing difficulty to locate and understand the relevant data subsets within large datasets – what can be seen as a severe information quality issue in today's decision-support environments. The study proposes a quantitative methodology, based on the mutual-information metric, for assessing the relative importance of different data subsets within a large dataset. Such assessments can grant the end-user with faster access to relevant subsets within a large dataset, the ability to better understandits contents, and gain deeper insights from analyzing it – e.g., when such a dataset is being used for Business Intelligence (BI) applications. This manuscript provides the background and the motivation for integrating the proposed assessments of relative importance. It then defines the calculations behind the mutualinformation metric, and demonstrates their applications using illustrative examples. Collapse

Topics: online analytical processing data quality structured query language recommender system decision making

Methods: computational algorithm design science multidimensional modeling company material

Theories: information theory

Large-scale regulatory network analysis from microarray data: modified Bayesian network learning and association rule mining

2007 | Decision Support Systems | Citations: 0

Authors: Huang, Zan; Li, Jiexun; Su, Hua; Watts, George S.; Chen, Hsinchun

Abstract: We present two algorithms for learning large-scale gene regulatory networks from ... Expand

Abstract: We present two algorithms for learning large-scale gene regulatory networks from microarray data: a modified information-theory-based Bayesian network algorithm and a modified association rule algorithm. Simulation-based evaluation using six datasets indicated that both algorithms outperformed their unmodified counterparts, especially when analyzing large numbers of genes. Both algorithms learned about 20% (50% if directionality and relation type were not considered) of the relations in the actual models. In our empirical evaluation based on two real datasets, domain experts evaluated subsets of learned relations with high confidence and identified 20–30% to be “interesting” or “maybe interesting” as potential experiment hypotheses. Collapse

Methods: computational algorithm simulation Bayesian network association rule learning experiment

Theories: information theory