International Journal of Data Science and Analytics国际数据科学与分析杂志
International Journal of Data Science and Analytics(英文缩写 INT J DATA SCI ANAL),ISSN 2364-415X,eISSN 2364-4168,中文译名:国际数据科学与分析杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 未收录 | N/A |
| 2022 | 2.400 | N/A |
| 2023 | 3.400 | Q2 |
| 2024 | 2.800 | Q2 |
| 2025 | 2.900 | Q3 |
International Journal of Data Science and Analytics 最新收录文献
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1. Identifying clinical feature clusters toward predicting stroke in patients with asymptomatic carotid stenosis.
PMID:日期:2024-08-09Despite the widespread application of machine learning models and feature selection methods to identify important clinical features in electronic health records (EHR) for disease prediction, the use of graph neural networks (GNNs) to uncover significant clinical features associated with a disease remains largely unexplored. In this investigation, we developed a computational method utilizing EHR data from Indiana University Medical Hospital to predict stroke in patients with asymptomatic carotid stenosis. We first constructed a patient clinical feature graph for each patient based on the co-occurrence of features (medications, diagnoses, and results of laboratory tests) in the EHR data within a predefined timeframe (e.g., 6 months before the detection of the disease). Then, we applied an unsupervised GNN-based clustering approach and our algorithm to select notable clinical feature clusters crucial for stroke prediction. These clinical features served as the basis for constructing patient representation for prediction. Various supervised learning models were evaluated for their prediction capabilities. Unlike conventional feature selection methods, our GNN-based feature selection approach relies solely on positive cases. We compared our method against baseline models for stroke prediction and achieved robust performance metrics, including an AUC of 0.87 and an F1 score of 0.80, surpassing all baselines. Additionally, we conducted an ablation study on the amount of EHR data, measured in months, to determine the most effective approach for generating patient clinical feature graphs. By capturing inherent relationships between clinical features using the graph model, our approach offers a promising avenue for advancing disease prediction, particularly in scenarios with limited positive cases available. Our code can be found on Github (https://github.com/xudav001/Identifying-Phenotype-Clusters).
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2. Narratives from GPT-derived networks of news and a link to financial markets dislocations.
PMID:日期:2024-03-17We introduce a novel framework to study the dynamics of news narratives, by leveraging GPT3.5 advanced text analysis capabilities and graph theory. In particular, we focus on a corpus of economic articles from The Wall Street Journal and dynamically extract the main topics of discussion over time, in a completely systematic and scalable fashion. As a simple application of the suggested approach, we show how the structure of such topics of discussion has a statistically significant relationship with the contemporaneous state of financial markets, which can be used to construct an investment strategy or monitor financial risks. Our work is based on the intrinsic ability of GPT models to track the context of sentences within a document, thanks to which we can accurately extract a ranking of the most important entities discussed within each article, and evaluate their entity-specific sentiments. Then, we create a graph for each week of data, in which nodes are the entities retrieved and edges are built from the co-occurrence of such entities within articles. Graph centrality measures are computed over time to track the most representative keywords of topics of discussion, which result in an accurate summary view of the evolution of economic narratives. Fuzzy community detection is finally used to cluster linked entities into a more detailed representation of topics. Such groups of entities are mapped to the related journal articles, which are in turn summarised to reach a highly nuanced and interpretable view of the topics discussed within each week. Linking the features of these topics to the relevant financial market time series, we find that high fragmentation within our networks' communities relates to moments of financial markets dislocations (i.e. dates with unusually high volatility across asset classes). This result should thus motivate stronger effort within financial research to move beyond ubiquitous sentiment analysis of news and delve deeper into broader and more holistic studies of textual data.
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3. Power Analysis for Causal Discovery.
PMID:日期:2023-06-27Causal discovery algorithms have the potential to impact many fields of science. However, substantial foundational work on the statistical properties of causal discovery algorithms is still needed. This paper presents what is to our knowledge the first method for conducting power analysis for causal discovery algorithms. The power sample characteristics of causal discovery algorithms typically cannot be described by a closed formula, but we resolve this problem by developing a new power sample analysis method based on standardized simulation experiments. Our procedure generates data with carefully controlled statistical effect sizes in order to enable an accurate numerical power sample analysis. We present that method, apply it to generate an initial power analysis table, provide a web interface for searching this table, and show how the table or web interface can be used to solve several types of real world power analysis problems, such as sample size planning, interpretation of results, and sensitivity analysis.
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4. Analyzing international airtime top-up transfers for migration and mobility.
PMID:日期:2023-05-25International airtime top-up transfers enable prepaid mobile phone users to send top-ups and data bundles to users in other countries, as well as make payments, in real time. These are heavily used by migrants to financially assist their families in their home countries and consequently could be a valuable source of information for migration and mobility analysis. However, top-up transfers are understudied as a form of money remittance in migration. In this paper, we explore the determinants and the potential of top-up transactions to complement remittance and migration statistics. Our results show that such data can provide insights into migrant groups, particularly for irregular migration and for estimating the real-time distribution of migrant groups for a given country.
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5. Evaluating narrative visualization: a survey of practitioners.
PMID:日期:2023-03-31Narrative visualization is characterized by the integration of data visualization and storytelling techniques. These characteristics provide challenges in its evaluation. Little is known about how these evaluation challenges are addressed by narrative visualization practitioners. We surveyed experienced narrative visualization practitioners to investigate their methods of evaluation. To gain deeper insight we conducted a series of semi-structured interviews with practitioners. We found that there is usually an informal approach to narrative visualization evaluation, where practitioners rely on prior experience and their peers for evaluation. Our study also revealed novel approaches to evaluation. We introduce a practice-led heuristic framework to aid practitioners to evaluate narrative visualization systematically. Our practice-led heuristic framework couples first-hand practitioner experience with recent research literature. This work sheds light on how to address narrative visualization evaluation to better inform both academic research and practice.
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6. AI and data science for smart emergency, crisis and disaster resilience.
6. 人工智能和数据科学用于智能应急、危机和灾难恢复PMID:日期:2023-03-27The uncertain world has seen increasing emergencies, crises and disasters (ECDs), such as the COVID-19 pandemic, hurricane Ian, global financial inflation and recession, misinformation disaster, and cyberattacks. AI for smart disaster resilience (AISDR) transforms classic reactive and scripted disaster management to digital proactive and intelligent resilience across ECD ecosystems. A systematic overview of diverse ECDs, classic ECD management, ECD data complexities, and an AISDR research landscape are presented in this article. Translational is essential to enable smart disaster resilience.
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7. Fake news detection: deep semantic representation with enhanced feature engineering.
7. 假新闻检测：具有增强特征工程的深度语义表示PMID:日期:2023-03-09Due to the widespread use of social media, people are exposed to fake news and misinformation. Spreading fake news has adverse effects on both the general public and governments. This issue motivated researchers to utilize advanced natural language processing concepts to detect such misinformation in social media. Despite the recent research studies that only focused on semantic features extracted by deep contextualized text representation models, we aim to show that content-based feature engineering can enhance the semantic models in a complex task like fake news detection. These features can provide valuable information from different aspects of input texts and assist our neural classifier in detecting fake and real news more accurately than using semantic features. To substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (CNN) layers extract semantic features from contextual representation vectors. Then, semantic and content-based features are fed to a fully connected layer. We evaluated our model on an English dataset about the COVID-19 pandemic and a domain-independent Persian fake news dataset (TAJ). Our experiments on the English COVID-19 dataset show 4.16% and 4.02% improvement in accuracy and f1-score, respectively, compared to the baseline model, which does not benefit from the content-based features. We also achieved 2.01% and 0.69% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art results reported by Shifath et al. (A transformer based approach for fighting covid-19 fake news, arXiv preprint arXiv:2101.12027, 2021). Our model outperformed the baseline on the TAJ dataset by improving accuracy and f1-score metrics by 1.89% and 1.74%, respectively. The model also shows 2.13% and 1.6% improvement in accuracy and f1-score, respectively, compared to the state-of-the-art model proposed by Samadi et al. (ACM Trans Asian Low-Resour Lang Inf Process, https://doi.org/10.1145/3472620, 2021).
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8. A new one-parameter discrete probability distribution with its neutrosophic extension: mathematical properties and applications.
PMID:日期:2023-02-07Count data modeling's significance and its applicability to real-world occurrences have been emphasized in a number of research studies. The purpose of this work is to introduce a new one-parameter discrete distribution for the modeling of count datasets. Some mathematical properties, such as reliability measures, characteristic function, moment-generating function, and associated measurements, such as mean, variance, skewness, kurtosis, and index of dispersion, have been derived and studied. The nature of the probability mass function and failure rate function has been studied graphically. The model parameter is estimated using renowned maximum likelihood estimation methods. A neutrosophic extension of the new model is also introduced for the modeling of interval datasets. In addition, the proposed distribution's applicability was compared to that of other discrete distributions. The study's findings show that the novel discrete distribution is a very appealing alternative to some other discrete competitive distributions.
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9. Generalization of the Lindley distribution with application to COVID-19 data.
PMID:日期:2022-11-23Creating new distributions with more desired and flexible qualities for modeling lifetime data has resulted in a concentrated effort to modify or generalize existing distributions. In this paper, we propose a new distribution called the power exponentiated Lindley (PEL) distribution by generalizing the Lindley distribution using the power exponentiated family of distributions, that can fit lifetime data. Then the main statistical properties such as survival function, hazard function, reverse hazard function, moments, quantile function, stochastic ordering, MRL, order statistics, etc., of the newly proposed distribution have been derived. The parameters of the distribution are estimated using the MLE method. Then, a Monte Carlo simulation study is used to check the consistency of the parameters of the PEL distribution in terms of MSE, RMSE, and bias. Finally, we implement the PEL distribution as a statistical lifetime model for the COVID-19 case fatality ratio (in %) in China and India, and the new cases of COVID-19 reported in Delhi. Then we check whether the new distribution fits the data sets better than existing well-known distributions. Different statistical measures such as the value of the log-likelihood function, K-S statistic, AIC, BIC, HQIC, and -value are used to assess the accuracy of the model. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set.
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10. Learning to discover medicines.
PMID:日期:2022-11-18Discovering new medicines is the hallmark of the human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper, we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature on AI for drug discovery into three relatively stable sub-areas: (a) over molecular sequences and geometric graphs; (b) where we predict molecular properties and their binding, optimize existing compounds, generate molecules, and plan the synthesis of target molecules; and (c) where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.