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machine learning can predict surgical indication: new clustering model from a large adult spine deformity database

EUROPEAN SPINE JOURNAL 2025年 1-11页

作者： Baroncini, Alice Larrieu, Daniel Bourghli, Anouar Pizones, Javier Pellise, Ferran Kleinstueck, Frank S. Alanay, Ahmet Boissiere, Louis Obeid, Ibrahim IRCCS Osped Galeazzi St Ambrogio Milan Italy Elsan Polyclin Jean Villar Brugge France King Faisal Specialist Hosp & Res Ctr Spine Surg Dept Riyadh Saudi Arabia Hosp Univ La Paz Spine Surg Unit Madrid Spain Vall D Hebron Hosp Spine Surg Unit Barcelona Spain Schulthess Klin Zurich Switzerland Acibadem Univ Spine Ctr Sch Med Istanbul Turkiye Bordeaux Univ Pellegrin Hosp Bordeaux France

PurposeThe choice of the best management for Adult Spine Deformity (ASD) is challenging. Health-related quality of life (HRQoL), comorbidities, symptoms and spine geometry, along with surgical risk and potential residual disability play a role, and a definite algorithm for patient management is lacking. machine learning allows to analyse complex settings more efficiently than other available statistical tools. Aim of this study was to develop a machine-learning algorithm that, based on baseline data, would be able to predict whether an ASD patient would undergo surgery or *** evaluation of prospectively collected data. Demographic data, HRQoL and radiographic parameters were collected. Two clustering methods were performed to differentiate groups of patients with similar characteristics. Three models were then used to identify the most relevant variables for management *** from 1319 patients were available. Three clusters were identified: older subjects with sagittal imbalance and high PI, younger patients with greater coronal deformity and no sagittal imbalance, older patients with moderate sagittal imbalance and lower PI. The group of younger patients showed the highest error rate for the prediction (37%), which was lower for the other two groups (20-27%). For all groups, quality of life parameters such as the ODI and the SRS 22 and the Cobb angle of the major curve were the strongest predictors of surgical indication, albeit with different odds ratios in each *** clusters could be identified along with the variables that, in each, are most likely to drive the choice of management.

关键词： Adult spine deformity Spine deformity surgery machine learning algorithm

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A machine learning-Sentiment Analysis on Monkeypox Outbreak: An Extensive Dataset to Show the Polarity of Public Opinion From Twitter Tweets

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IEEE ACCESS 2023年 11卷 11811-11826页

作者： Bengesi, Staphord Oladunni, Timothy Olusegun, Ruth Audu, Halima Bowie State Univ Dept Comp Sci Bowie MD 20715 USA Morgan State Univ Dept Comp Sci Baltimore MD 21251 USA

Research on sentiment analysis has proven to be very useful in public health, particularly in analyzing infectious diseases. As the world recovers from the onslaught of the COVID-19 pandemic, concerns are rising that another pandemic, known as monkeypox, might hit the world again. Monkeypox is an infectious disease reported in over 73 countries across the globe. This sudden outbreak has become a major concern for many individuals and health authorities. Different social media channels have presented discussions, views, opinions, and emotions about the monkeypox outbreak. Social media sentiments often result in panic, misinformation, and stigmatization of some minority groups. Therefore, accurate information, guidelines, and health protocols related to this virus are critical. We aim to analyze public sentiments on the recent monkeypox outbreak, with the purpose of helping decision-makers gain a better understanding of the public perceptions of the disease. We hope that government and health authorities will find the work useful in crafting health policies and mitigating strategies to control the spread of the disease, and guide against its misrepresentations. Our study was conducted in two stages. In the first stage, we collected over 500,000 multilingual tweets related to the monkeypox post on Twitter and then performed sentiment analysis on them using VADER and TextBlob, to annotate the extracted tweets into positive, negative, and neutral sentiments. The second stage of our study involved the design, development, and evaluation of 56 classification models. Stemming and lemmatization techniques were used for vocabulary normalization. Vectorization was based on CountVectorizer and TF-IDF methodologies. K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest, Logistic Regression, Multilayer Perceptron (MLP), Naive Bayes, and XGBoost were deployed as learning algorithms. Performance evaluation was based on accuracy, F1 Score, Precision, and Recall. Our

关键词： Social networking (online) Sentiment analysis Blogs Classification algorithms Computational modeling machine learning Count vectorizer machine learning algorithm monkeypox sentiment analysis twitter TF-IDF TextBlob Vader

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Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2023年第12期415卷 2291-2305页

作者： Cao, Jie Wei, Xue Liu, Ming-Feng An, Guo-Shuai Li, Jian Du, Qiu-Xiang Sun, Jun-Hong Shanxi Med Univ Sch Forens Med Jinzhong 030604 Shanxi Peoples R China

The determination of sudden cardiac death (SCD) is one of the difficult tasks in the forensic practice, especially in the absence of specific morphological changes in the autopsies and histological investigations. In this study, we combined the metabolic characteristics from corpse specimens of cardiac blood and cardiac muscle to predict SCD. Firstly, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS)-based untargeted metabolomics was applied to obtain the metabolomic profiles of the specimens, and 18 and 16 differential metabolites were identified in the cardiac blood and cardiac muscle from the corpses of those who died of SCD, respectively. Several possible metabolic pathways were proposed to explain these metabolic alterations, including the metabolism of energy, amino acids, and lipids. Then, we validated the capability of these combinations of differential metabolites to distinguish between SCD and non-SCD through multiple machine learning algorithms. The results showed that stacking model integrated differential metabolites featured from the specimens showed the best performance with 92.31% accuracy, 93.08% precision, 92.31% recall, 91.96% F1 score, and 0.92 AUC. Our results revealed that the SCD metabolic signature identified by metabolomics and ensemble learning in cardiac blood and cardiac muscle has potential in SCD post-mortem diagnosis and metabolic mechanism investigations.

关键词： Sudden cardiac death Untargeted metabolomics UPLC-HRMS machine learning algorithm Stacking learning

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Soft Electronics for Health Monitoring Assisted by machine learning

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Nano-Micro Letters 2023年第5期15卷 83-168页

作者： Yancong Qiao Jinan Luo Tianrui Cui Haidong Liu Hao Tang Yingfen Zeng Chang Liu Yuanfang Li Jinming Jian Jingzhi Wu He Tian Yi Yang Tian-Ling Ren Jianhua Zhou School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen UniversityNo.66Gongchang RoadGuangming DistrictShenzhen 518107People’s Republic of China School of Integrated Circuits and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing 100084People’s Republic of China Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province School of Biomedical EngineeringSun Yat-sen UniversityGuangzhou 510275People’s Republic of China

Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft *** soft electronics have huge potential in the physical sign monitoring and health *** of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal ***,it is easy to build the specific dataset,which is important to improve the performance of machine learning *** the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and *** soft electronics and machining learning algorithms complement each other very *** is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near ***,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning *** soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and ***,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network ***,the soft device assisted by neural network will be introduced ***,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.

关键词： Soft electronics machine learning algorithm Physiological signal monitoring Soft materials

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machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer

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DISCOVER ONCOLOGY 2025年第1期16卷 1-16页

作者： Li, Chang-Lei Yao, Zhi-Yuan Qu, Chao Shao, Guan-Ming Liu, Yu-Kun Pei, Xiang-Yu Cao, Jing-Yu Wang, Zu-Sen Qingdao Univ Affiliated Hosp Dept Hepatobiliary & Pancreat Surg Qingdao Peoples R China Qingdao Univ Affiliated Hosp Dept Thorac Surg Qingdao Peoples R China Qingdao Univ Coll Med Qingdao Peoples R China

BackgroundHistamine, a critical inflammatory mediator, is generated by both mast cells and specific tumor cells, and it plays a fundamental role in inflammatory and immune responses. In the current scientific landscape, histamine-related genes (HRGs) and their associated pathways have been validated to be implicated in the development and advancement of cancer. However, the precise role of HRGs in gauging the risk and predicting the prognosis of pancreatic adenocarcinoma (PAAD) remains *** carried out an elaborate data collection endeavor. Transcriptome data along with pertinent clinical information were obtained from the GSE28735, GSE62452, and TCGA-PAAD cohorts. GWAS data were retrieved from the FinnGen Release 11 and eQTLGen databases. For the drug-target Mendelian randomization (MR) analysis, the "TwoSampleMR" (version 0.5.6) R package was employed. The random survival forest (RSF) model was analyzed using the "randomForestSRC (rfsrc)" R package and further elucidated with the help of the "mlr3" package. Somatic mutation analysis and immune infiltration investigations were conducted by means of the "maftools" (v. 2.12.0) R package and "pRRophetic" R software package, respectively. Targeted drug sensitivity analysis was executed using the "oncopredict" and "parallel" *** a meticulous drug-targeted MR analysis and an exhaustive exploration of transcriptome databases (including 2 GSE combat and TCGA cohort), 20 upregulated differentially expressed genes (DEGs) were identified. The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777. In the training and validation cohorts, the model exhibited remarkable predictive prowess, with 1-, 2-, and 3-year prediction accuracies of 0.898, 0.932, and 0.922 in the training set, and 0.909, 0.974, and 0.962 in the validation set, respectively. A higher HRG score was found to correlate with adverse ev

关键词： Pancreatic adenocarcinoma machine learning algorithm Drug-targeted Mendelian randomization Transcriptome

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Development of machine learning based classifier for the pressure test result prediction of type IV composite overwrapped pressure vessels

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 58卷 380-388页

作者： Jiang, Weili Liang, Moxi Schiebel, Martin Zaremba, Swen Drechsler, Klaus Tech Univ Munich Chair Carbon Composites D-85748 Garching Germany Voith Composites SE & Co KG Daimlerstr 27 D-85748 Garching Germany

The stringent safety regulations of type IV composite overwrapped pressure vessels (COPVs) for commercial vehicles mandate a certification process involving pressurization up to 1050 bar, with the critical requirement of withstanding burst pressures of 1570 bar. Analyzing proof test data is crucial to enhance and ensure tank safety regarding burst pressure. In this study, we developed various machine learning classifiers for structure health monitoring and damage prediction of COPVs. The classifiers were trained using a substantial amount of acoustic emission data collected during burst and pressure cycling tests. The test results were employed as label inputs during the training process. Statistical features were extracted per time unit and trained using Naive Bayes, Logistic Regression, Decision Tree, XGBoost, and TabNet models. Upon training the data collected from the burst pressure test, TabNet, Decision Tree, and XGBoost achieved classification accuracies above 0.94. Notably, TabNet demonstrated also the best performance for the pressure cycling test with an accuracy of 0.98. Furthermore, TabNet provided visualizations of feature sensitivity in relation to classification results. This study marks the first development of a machine learning classifier for predicting the damage state of COPV tanks in commercial applications pertaining to required safety tests.

关键词： Hydrogen pressure vessel Prediction of pressure test results machine learning algorithm ML -based classifier

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A comparative study of traditional machine learning and hybrid fuzzy inference system machine learning models for air quality index forecasting

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年 1-22页

作者： Ordenshiya, K. M. Revathi, Gk Vellore Inst Technol Chennai Sch Adv Sci Dept Math Chennai Campus Chennai 600127 Tamil Nadu India

Air pollution from urban activities poses significant health risks, underscoring the need for effective monitoring of the air quality index (AQI). This paper presents a novel approach for AQI prediction by integrating a Takagi-Sugeno fuzzy inference system (TS-FIS) with machine learning (ML). Traditional ML techniques often encounter difficulties in converting regression datasets into classification formats, particularly when conventional labelling methods are inadequate. The TS-FIS model, developed using MATLAB, integrates both pollutant and meteorological data, simplifies input grouping and rule management, and converts regression data into classification levels such as healthy, moderate, and unhealthy using IF-THEN rules. The regression outputs are validated with metrics including RMSE (0.48), MSE (0.23), MAE (0.45), and MAPE (1.77). A random forest classifier (RFC) trained on the TS-FIS outputs achieves maximum 99.85% accuracy, with F1 score, precision, and recall, surpassing traditional methods, which achieve maximum 99.63% accuracy. The study also includes a comparative analysis of ML-based FIS models for AQI prediction with different parameters and membership functions, alongside a traditional ML model for AQI classification using RFC. The key novelty of this work lies in the fine-tuning of membership functions and parameters, which significantly enhances the performance of the ML-based FIS model, demonstrating its superiority. These results underscore the model's potential for practical applications in environmental monitoring and management.

关键词： Air quality index Artificial intelligence Fuzzy inference system machine learning algorithm Simulink

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Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children

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ITALIAN JOURNAL OF PEDIATRICS 2025年第1期51卷 1-11页

作者： Setegn, Gizachew Mulu Dejene, Belayneh Endalamaw Debark Univ Dept Comp Sci Debark 90 Ethiopia Univ Gondar Gondar 196 Ethiopia

BackgroundChild nutrition in Ethiopia is a significant concern, particularly for preschool-aged children. Children must have a varied diet to ensure they receive all the essential nutrients for good health. Unfortunately, many children in Ethiopia lack access to a range of foods, which can lead to malnutrition and other health issues. While machine learning (ML) has the potential to analyse extensive datasets, the lack of transparency in these models can impede their effectiveness in real-world applications, especially in public health. This research aims to enhance machine learning models by integrating Explainable AI (XAI) methods to more accurately predict the level of dietary diversity in Ethiopian preschool *** Improve the ML Model for Predicting the Level of Dietary Diversity among Ethiopian Preschool Children. We employed an ensemble ML approach with XAI. The Ethiopian demographic health survey collected a dataset consisting of dietary information and relevant socioeconomic variables. The data were preprocessed to obtain quality data that are suitable for the ensemble ML algorithms to develop a model. We applied filter (chi-square and mutual information) and wrapper (sequential backwards) feature selection methods to identify the most influential factors for dietary diversity (DD). Ethiopia demographic health survey (from 2011 to 2019). Datasets were used. We developed a predictive model using a decision tree, random forest, gradient boosting, light gradient boosting, CatBoost, and XGBClassifier. We evaluated it using accuracy, precision, recall, F1_score, and receiver operating characteristic (ROC)-based evaluation *** ensemble ML models exhibited robust predictive performance, and light gradient boosting outperformed the other ensemble ML algorithms by 95.3%. The explainability of the Light Gradient Boosting Ensemble Model was determined using Eli5 and LIME. The child's age, household wealth index, household region, source o

关键词： Dietary diversity Ethiopia Explanatory AI Food group Predicting level Preschool children machine learning algorithm

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machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer

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HELIYON 2024年第17期10卷 e37256页

作者： Li, Qinyu Wang, Yanan Chen, Junjie Zeng, Kai Wang, Chengwei Guo, Xiangdong Hu, Zhiquan Hu, Jia Liu, Bo Xiao, Jun Zhou, Peng Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Oncol Wuhan 430030 Hubei Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Urol Wuhan 430030 Hubei Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Thyroid & Breast Surg Wuhan 430030 Hubei Peoples R China Chongqing Med Univ Affiliated Hosp 1 Dept Urol Chongqing 400016 Peoples R China

Background: It has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities. Methods: In this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo. Results: ARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARS high group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow low patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARS high patients. Conclusions: Collectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

关键词： Prostate cancer Androgen receptor regulatory genes machine learning algorithm Clinical translation Precision therapy

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machine learning and financial big data control using IoT

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第4期18卷 2657-2670页

作者： Xiao, Jian Zhejiang Fash Inst Technol Sch Business Ningbo Zhejiang Peoples R China

machine learning algorithms have been widely used in risk prediction management systems for financial data. Early warning and control of financial risks are important areas of corporate investment decision-making, which can effectively reduce investment risks and ensure companies' stable development. With the development of the Internet of Things, enterprises' financial information is obtained through various intelligent devices in the enterprise financial system. Big data provides high-quality services for the economy and society in the high-tech era of information. However, the amount of financial data is large, complex and variable, so the analysis of financial data has huge difficulties, and with the in-depth application of machine learning algorithms, its shortcomings are gradually exposed. To this end, this paper collects the financial data of a listed group from 2005 to 2020, and conducts data preprocessing and Feature selection, including removing missing values, Outlier and unrelated items. Next, these data are divided into a training set and a testing set, where the training set data is used for model training and the testing set data is used to evaluate the performance of the model. Three methods are used to build and compare data control models, which are based on machine learning algorithm, based on deep learning network and the model based on artificial intelligence and Big data technology proposed in this paper. In terms of risk event prediction comparison, this paper selects two indicators to measure the performance of the model: accuracy and Mean squared error (MSE). Accuracy reflects the predictive ability of the model, which is the proportion of all correctly predicted samples to the total sample size. Mean squared error is used to evaluate the accuracy and error of the model, that is, the square of the Average absolute deviation between the predicted value and the true value. In this paper, the prediction results of the three methods are compared

关键词： machine learning algorithm financial risk control big data control cyborg sensation Internet of Things

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