Machine learning and knowledge extraction最新文献

Dynamic Graph Analysis: A Hybrid Structural–Spatial Approach for Brain Shape Correspondence 动态图分析：脑形状对应的结构-空间混合方法

Machine learning and knowledge extraction Pub Date : 2025-09-10 DOI: 10.3390/make7030099

Javier A. Garcia, Hernán Felipe García Arias, Andrés Escobar Mejía, David Cárdenas‐Peña, Álvaro A. Orozco

{"title":"Dynamic Graph Analysis: A Hybrid Structural–Spatial Approach for Brain Shape Correspondence","authors":"Javier A. Garcia, Hernán Felipe García Arias, Andrés Escobar Mejía, David Cárdenas‐Peña, Álvaro A. Orozco","doi":"10.3390/make7030099","DOIUrl":"https://doi.org/10.3390/make7030099","url":null,"abstract":"Accurate correspondence of complex neuroanatomical surfaces under non-rigid deformations remains a formidable challenge in computational neuroimaging, owing to inter-subject topological variability, partial occlusions, and non-isometric distortions. Here, we introduce the Dynamic Graph Analyzer (DGA), a unified hybrid framework that integrates simplified structural descriptors with spatial constraints and formulates matching as a global linear assignment. Structurally, the DGA computes node-level metrics, degree weighted by betweenness centrality and local clustering coefficients, to capture essential topological patterns at a low computational cost. Spatially, it employs a two-stage scheme that combines global maximum distances and local rescaling of adjacent node separations to preserve geometric fidelity. By embedding these complementary measures into a single cost matrix solved via the Kuhn–Munkres algorithm followed by a refinement of weak correspondences, the DGA ensures a globally optimal correspondence. In benchmark evaluations on the FAUST dataset, the DGA achieved a significant reduction in the mean geodetic reconstruction error compared to spectral graph convolutional netwworks (GCNs)—which learn optimized spectral descriptors akin to classical approaches like heat/wave kernel signatures (HKS/WKS)—and traditional spectral methods. Additional experiments demonstrate robust performance on partial matches in TOSCA and cross-species alignments in SHREC-20, validating resilience to morphological variation and symmetry ambiguities. These results establish the DGA as a scalable and accurate approach for brain shape correspondence, with promising applications in biomarker mapping, developmental studies, and clinical morphometry.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"7 3","pages":"99-99"},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.mdpi.com/2504-4990/7/3/99/pdf?version=1757495281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381957","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

Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes. 来自EHR数据的机器学习代码比人工分配的ICD代码更能预测硬结果。

IF 4

Machine learning and knowledge extraction Pub Date : 2025-06-01 Epub Date: 2025-04-17 DOI: 10.3390/make7020036

Ying Yin, Yijun Shao, Phillip Ma, Qing Zeng-Treitler, Stuart J Nelson

{"title":"Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes.","authors":"Ying Yin, Yijun Shao, Phillip Ma, Qing Zeng-Treitler, Stuart J Nelson","doi":"10.3390/make7020036","DOIUrl":"10.3390/make7020036","url":null,"abstract":"We used machine learning (ML) to characterize 894,154 medical records of outpatient visits from the Veterans Administration Central Data Warehouse (VA CDW) by the likelihood of assignment of 200 International Classification of Diseases (ICD) code blocks. Using four different predictive models, we found the ML-derived predictions for the code blocks were consistently more effective in predicting death or 90-day rehospitalization than the assigned code block in the record. We reviewed records of ICD chapter assignments. The review revealed that the ML-predicted chapter assignments were consistently better than those humanly assigned. Impact factor analysis, a method of explanation of AI findings that was developed in our group, demonstrated little effect on any one assigned ICD code block but a marked impact on the ML-derived code blocks of kidney disease as well as several other morbidities. In this study, machine learning was much better than human code assignment at predicting the relatively rare outcomes of death or rehospitalization. Future work will address generalizability using other datasets, as well as addressing coding that is more nuanced than that of the categorization provided by code blocks.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"7 2","pages":"36"},"PeriodicalIF":4.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129704","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

Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies For Literature Review Automation. 加速疾病模型参数提取：一种基于llm的文献综述自动化初始研究选择排序方法。

IF 6

Machine learning and knowledge extraction Pub Date : 2025-03-26 DOI: 10.3390/make7020028

Masood Sujau, Masako Wada, Emilie Vallée, Natalie Hillis, Teo Sušnjak

{"title":"Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies For Literature Review Automation.","authors":"Masood Sujau, Masako Wada, Emilie Vallée, Natalie Hillis, Teo Sušnjak","doi":"10.3390/make7020028","DOIUrl":"10.3390/make7020028","url":null,"abstract":"As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question-answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS@95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"7 2","pages":"28"},"PeriodicalIF":6.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7618976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624700","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

Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis 从新闻文本中提取商业互动知识图谱，用于商业网络分析

Machine learning and knowledge extraction Pub Date : 2024-01-07 DOI: 10.3390/make6010007

Didier Gohourou, Kazuhiro Kuwabara

引用次数: 0

A Data Mining Approach for Health Transport Demand 健康运输需求的数据挖掘方法

Machine learning and knowledge extraction Pub Date : 2024-01-04 DOI: 10.3390/make6010005

Jorge Blanco Prieto, Marina Ferreras González, S. Van Vaerenbergh, Oscar Jesús Cosido Cobos

{"title":"A Data Mining Approach for Health Transport Demand","authors":"Jorge Blanco Prieto, Marina Ferreras González, S. Van Vaerenbergh, Oscar Jesús Cosido Cobos","doi":"10.3390/make6010005","DOIUrl":"https://doi.org/10.3390/make6010005","url":null,"abstract":"Efficient planning and management of health transport services are crucial for improving accessibility and enhancing the quality of healthcare. This study focuses on the choice of determinant variables in the prediction of health transport demand using data mining and analysis techniques. Specifically, health transport services data from Asturias, spanning a seven-year period, are analyzed with the aim of developing accurate predictive models. The problem at hand requires the handling of large volumes of data and multiple predictor variables, leading to challenges in computational cost and interpretation of the results. Therefore, data mining techniques are applied to identify the most relevant variables in the design of predictive models. This approach allows for reducing the computational cost without sacrificing prediction accuracy. The findings of this study underscore that the selection of significant variables is essential for optimizing medical transport resources and improving the planning of emergency services. With the most relevant variables identified, a balance between prediction accuracy and computational efficiency is achieved. As a result, improved service management is observed to lead to increased accessibility to health services and better resource planning.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386625","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

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data 增强信用风险分析的机器学习：整合心理健康数据的贷款审批预测模型比较研究

Machine learning and knowledge extraction Pub Date : 2024-01-04 DOI: 10.3390/make6010004

Adnan Alagic, Natasa Zivic, E. Kadusic, Dženan Hamzić, Narcisa Hadzajlic, Mejra Dizdarević, Elmedin Selmanovic

{"title":"Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data","authors":"Adnan Alagic, Natasa Zivic, E. Kadusic, Dženan Hamzić, Narcisa Hadzajlic, Mejra Dizdarević, Elmedin Selmanovic","doi":"10.3390/make6010004","DOIUrl":"https://doi.org/10.3390/make6010004","url":null,"abstract":"The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"15 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386505","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

Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics 预测城市地区的风舒适度：一般风玫瑰图统计回归与分类-CNN 的比较

Machine learning and knowledge extraction Pub Date : 2024-01-04 DOI: 10.3390/make6010006

Jennifer Werner, Dimitri Nowak, Franziska Hunger, Tomas Johnson, A. Mark, Alexander Gösta, F. Edelvik

{"title":"Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics","authors":"Jennifer Werner, Dimitri Nowak, Franziska Hunger, Tomas Johnson, A. Mark, Alexander Gösta, F. Edelvik","doi":"10.3390/make6010006","DOIUrl":"https://doi.org/10.3390/make6010006","url":null,"abstract":"Wind comfort is an important factor when new buildings in existing urban areas are planned. It is common practice to use computational fluid dynamics (CFD) simulations to model wind comfort. These simulations are usually time-consuming, making it impossible to explore a high number of different design choices for a new urban development with wind simulations. Data-driven approaches based on simulations have shown great promise, and have recently been used to predict wind comfort in urban areas. These surrogate models could be used in generative design software and would enable the planner to explore a large number of options for a new design. In this paper, we propose a novel machine learning workflow (MLW) for direct wind comfort prediction. The MLW incorporates a regression and a classification U-Net, trained based on CFD simulations. Furthermore, we present an augmentation strategy focusing on generating more training data independent of the underlying wind statistics needed to calculate the wind comfort criterion. We train the models based on different sets of training data and compare the results. All trained models (regression and classification) yield an F1-score greater than 80% and can be combined with any wind rose statistic.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"28 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386972","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

An Evaluative Baseline for Sentence-Level Semantic Division 句子级语义划分的评估基线

Machine learning and knowledge extraction Pub Date : 2024-01-02 DOI: 10.3390/make6010003

Kuangsheng Cai, Zugang Chen, Hengliang Guo, Shaohua Wang, Guoqing Li, Jing Li, Feng Chen, Hang Feng

引用次数: 0

Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature 在生物医学文献研究论文数据集上对带有训练规模差异和子采样的不平衡分类进行统计分析

Machine learning and knowledge extraction Pub Date : 2023-12-11 DOI: 10.3390/make5040095

Jose Dixon, M. Rahman

{"title":"Statistical Analysis of Imbalanced Classification with Training Size Variation and Subsampling on Datasets of Research Papers in Biomedical Literature","authors":"Jose Dixon, M. Rahman","doi":"10.3390/make5040095","DOIUrl":"https://doi.org/10.3390/make5040095","url":null,"abstract":"The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"36 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981076","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

Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches 利用深度学习方法通过脑电信号有效检测癫痫发作

Machine learning and knowledge extraction Pub Date : 2023-12-11 DOI: 10.3390/make5040094

S. Mekruksavanich, A. Jitpattanakul

{"title":"Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches","authors":"S. Mekruksavanich, A. Jitpattanakul","doi":"10.3390/make5040094","DOIUrl":"https://doi.org/10.3390/make5040094","url":null,"abstract":"Epileptic seizures are a prevalent neurological condition that impacts a considerable portion of the global population. Timely and precise identification can result in as many as 70% of individuals achieving freedom from seizures. To achieve this, there is a pressing need for smart, automated systems to assist medical professionals in identifying neurological disorders correctly. Previous efforts have utilized raw electroencephalography (EEG) data and machine learning techniques to classify behaviors in patients with epilepsy. However, these studies required expertise in clinical domains like radiology and clinical procedures for feature extraction. Traditional machine learning for classification relied on manual feature engineering, limiting performance. Deep learning excels at automated feature learning directly from raw data sans human effort. For example, deep neural networks now show promise in analyzing raw EEG data to detect seizures, eliminating intensive clinical or engineering needs. Though still emerging, initial studies demonstrate practical applications across medical domains. In this work, we introduce a novel deep residual model called ResNet-BiGRU-ECA, analyzing brain activity through EEG data to accurately identify epileptic seizures. To evaluate our proposed deep learning model’s efficacy, we used a publicly available benchmark dataset on epilepsy. The results of our experiments demonstrated that our suggested model surpassed both the basic model and cutting-edge deep learning models, achieving an outstanding accuracy rate of 0.998 and the top F1-score of 0.998.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"31 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981149","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