IISE Transactions工业与系统工程学会汇刊
IISE Transactions(英文缩写 IISE TRANS),ISSN 2472-5854,eISSN 2472-5862,中文译名:工业与系统工程学会汇刊 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 | 3.425 | Q2 |
| 2022 | 2.600 | Q2 |
| 2023 | 2.000 | Q2 |
| 2024 | 2.300 | Q2 |
| 2025 | 2.600 | Q2 |
IISE Transactions 最新收录文献
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1. Multi-modal mixed-type structural equation modeling with structured sparsity for subgroup discovery from heterogeneous health data.
PMID:日期:2025-02-11The increasing availability of health data from resources such as large biobanks, electronic healthcare records, medical tests, and wearable sensors, has set the stage for the development of novel machine learning (ML) models for multi-modal mixed-type data to capture the complexity of human health and disease. Clustering is a type of ML model that aims to identify homogenous subgroups from heterogeneous data, providing a data-driven solution to targeted, subgroup-specific study and intervention. While such data contain diverse and complementary information to facilitate decision making and improve population health, clustering of high-dimensional multi-modal mixed-type data poses major challenges to existing ML and statistical models. We propose a novel Multi-modal Mixed-type Structural Equation Model (M2-SEM) with structured sparsity to cluster heterogeneous health data for precise subgroup discovery. To accommodate a mix of continuous and categorical data modalities, we developed a novel Gauss-Hermite-enabled Expectation-Majorization-Minimization (GH-EMM) algorithm that integrates the GH quadrature and the Majorization Maximization (MM) algorithm within the Expectation Maximization (EM) framework for efficient model estimation. The proposed M2-SEM and GH-EMM are first tested in extensive simulation studies in comparison with benchmarks, and then applied to identify subgroups of individuals with low- and high-risk of developing adverse cardiometabolic (CM) outcomes based on a full spectrum of CM risk factors such as poor nutrition and mental health, physical inactivity, and sleep deprivation. These findings shed light on the promise of using multi-modal mixed-type health data for early identification and targeted intervention of at-risk individuals for health promotion at the population level.
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2. Ranking and Combining Latent Structured Predictive Scores without Labeled Data.
PMID:日期:2024-12-04Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predictors to achieve better performance is challenging. Conventional ensemble learning methods assess the accuracy of predictors based on extensive labeled data. In practical applications, however, the acquisition of such labeled data can prove to be an arduous task. Furthermore, the predictors under consideration may exhibit high degrees of correlation, particularly when similar data sources or machine learning algorithms were employed during their model training. In response to these challenges, this paper introduces a novel structured unsupervised ensemble learning model (SUEL) to exploit the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. Two novel correlation-based decomposition algorithms are further proposed to estimate the SUEL model, constrained quadratic optimization (SUEL.CQO) and matrix-factorization-based (SUEL.MF) approaches. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery. The results compellingly demonstrate that the proposed methods can efficiently integrate the dependent predictors to an ensemble model without the need of ground truth data.
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3. Discriminant Subgraph Learning from Functional Brain Sensory Data.
PMID:日期:2021-12-06The human brain is a complex system with many functional units interacting with each other. This interacting relationship, known as the functional connectivity network (FCN), is critical for brain functions. To learn the FCN, machine learning algorithms can be built based on brain signals captured by sensing technologies such as EEG and fMRI. In neurological diseases, past research has revealed that the FCN is altered. Also, focusing on a specific disease, some part of the FCN, i.e., a sub-network, can be more susceptible than other parts. However, the current knowledge about disease-specific sub-networks is limited. We propose a novel Discriminant Subgraph Learner (DSL) to identify a functional sub-network that best differentiates patients with a specific disease from healthy controls based on brain sensory data. We develop an integrated optimization framework for DSL to simultaneously learn the FCN of each class and identify the discriminant sub-network. Further, we develop tractable and converging algorithms to solve the optimization. We apply DSL to identify a functional sub-network that best differentiates patients with episodic migraine (EM) from healthy controls based on a fMRI dataset. DSL achieved the best accuracy compared to five state-of-the-art competing algorithms.
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4. A Novel Transfer Learning Model for Predictive Analytics using Incomplete Multimodality Data.
PMID:日期:2020-09-17Multimodality datasets are becoming increasingly common in various domains to provide complementary information for predictive analytics. One significant challenge in fusing multimodality data is that the multiple modalities are not universally available for all samples due to cost and accessibility constraints. This results in a unique data structure called Incomplete Multimodality Dataset (IMD). We propose a novel Incomplete-Multimodality Transfer Learning (IMTL) model that builds a predictive model for each sub-cohort of samples with the same missing modality pattern, and meanwhile couples the model estimation processes for different sub-cohorts to allow for transfer learning. We develop an Expectation-Maximization (EM) algorithm to estimate the parameters of IMTL and further extend it to a collaborative learning paradigm that is specifically valuable for patient privacy preservation in health care applications. We prove two advantageous properties of IMTL: the ability for out-of-sample prediction and a theoretical guarantee for a larger Fisher information compared with models without transfer learning. IMTL is applied to diagnosis and prognosis of the Alzheimer's Disease (AD) at an early stage called Mild Cognitive Impairment (MCI) using incomplete multimodality imaging data. IMTL achieves higher accuracy than competing methods without transfer learning. Supplementary materials are available for this article on the publisher's website.
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5. Access Planning and Resource Coordination for Clinical Research Operations.
PMID:日期:2019-12-13This research creates an operations engineering and management methodology to optimize a complex operational planning and coordination challenge faced by sites that perform clinical research trials. The time-sensitive and resource-specific treatment sequences for each of the many trial protocols conducted at a site make it very difficult to capture the dynamics of this unusually complex system. Existing approaches for site planning and participant scheduling exhibit both excessively long and highly variable Time to First Available Visit (TFAV) waiting times and high staff overtime costs. We have created a new method, termed CApacity Planning Tool And INformatics (CAPTAIN) that provides decision support to identify the most valuable set of research trials to conduct within available resources and a plan for how to book their participants. Constraints include (i) the staff overtime costs, and/or (ii) the TFAV by trial. To estimate the site's metrics via a Mixed Integer Program, CAPTAIN combines a participant trajectory forecasting with an efficient visit booking reservation plan to allocate the date for the first visit of every participant's treatment sequence. It also plans a daily nursing staff schedule that is optimized together with the booking reservation plan to optimize each nurse's shift assignments in consideration of participants' requirements/needs.