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Large language models in orthopedics: An exploratory research trend analysis and machine learning classification

JOURNAL OF ORTHOPAEDICS 2025年 66卷 110-118页

作者： Garcia, Ausberto Velasquez Minami, Masataka Mejia-Rodriguez, Manuel Ortiz-Morales, Jorge Rolando Radice, Fernando Mayo Clin Dept Orthoped Surg Rochester MN USA Clin Univ los Andes Dept Orthoped Surg Ave Plaza 2501 Santiago 7620157 Chile Kyoto Prefectural Univ Med Grad Sch Med Sci Dept Orthopaed Kyoto Japan Univ Valle Sch Mech Engn Cali Colombia Univ Nacl Colombia Dept Cirugia Ortoped Bogota Colombia Univ los Andes Sch Med Santiago Chile

Background: Large Language Models (LLMs) are set to transform orthopedic practice with promising applications and a growing body of research. This exploratory study analyzed research trends in orthopedic LLMs and validated a machine learning classifier for categorizing publications into predefined domains. We hypothesized that LLM-related research would exhibit distinct thematic trends, and that a machine learning classifier would be able to accurately categorize research domains. Methods: A bibliometric analysis of 140 Scopus-indexed publications (2019-2024) was performed using keyword co-occurrence and thematic clustering. Articles were categorized into five areas: Patient Education, Research and Ethics, Surgeon Education, Clinical Support, and Diagnostics and Radiology Interpretation. machine learning classifiers were trained on TF-IDF vectorized text and evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Exploratory projections using linear regression assessed the volume and growth trends within the five research areas. Results: The exploratory analysis revealed a substantial increase in LLM publications increased significantly from 28 in 2023 to 108 articles in 2024. The support vector machine (SVM) model outperformed others, achieving 82 % accuracy (AUC-ROC: 0.97), with high precision for categorizing research in Clinical Assistance Tools and strong recall for Diagnosis and Radiology Interpretation. Subgroup analysis showed that Patient Education achieved balanced performance (precision: 88 %, recall: 78 %, F1-score: 82 %), but overlapping terminology caused misclassifications between research and education domains. Temporal analysis predicted continued growth in these research domains, with Patient Education (+26 %) and Research and Ethics (+57 %) leading the way through 2027. Conclusion: LLMs are exploring advancements in patient engagement, surgeon training, and orthopedic research, but challen

关键词： Large language models (LLMs) Orthopedic research machine learning classification Thematic clustering Chat GPT

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Private machine learning classification Based on Fully Homomorphic Encryption

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2020年第2期8卷 352-364页

作者： Sun, Xiaoqiang Zhang, Peng Liu, Joseph K. Yu, Jianping Xie, Weixin Shenzhen Univ ATR Key Lab Natl Def Technol Coll Informat Engn Shenzhen 518060 Peoples R China Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

machine learning classification is an useful tool for trend prediction by analyzing big data. As supporting homomorphic operations over encrypted data without decryption, fully homomorphic encryption (FHE) contributes to machine learning classification without leaking user privacy, especially in the outsouring scenario. In this paper, we propose an improved FHE scheme based on HElib, which is a FHE library implemented based on Brakerski's FHE scheme. Our improvement focuses on two aspects. On the one hand, we first use the relinearization technique to reduce the ciphertext size, and then the modulus switching technique is used to reduce the modulus and decryption noise. On the other hand, we need no relinearization and modulus switching if there is additive homomorphic or no homomorphic operation in the multiplicative ciphertext's next homomorphic operation. Homomorphic comparison protocol, private hyperplane decision-based classification and private Naive Bayes classification are implemented by additive homomorphic and multiplicative homomorphic first. In our homomorphic comparison protocol, the number of interactions is reduced from 3 to 1. We choose the proposed FHE scheme to implement private decision tree classification. Simulation results show that the efficiency of our FHE scheme and implementation of private decision tree classification are more efficient than other two schemes.

关键词： Decision trees Encryption Additives Switches Protocols Privacy machine learning classification privacy preserving fully homomorphic encryption hyperplane decision-based Naive Bayes decision tree

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Characterization and machine learning classification of AI and PC Workloads

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IEEE ACCESS 2024年 12卷 83858-83875页

作者： Sibai, Fadi N. Asaduzzaman, Abu El-Moursy, Ali Gulf Univ Sci & Technol Dept Elect & Comp Engn Mubarak Al Abdullah 32093 Kuwait Wichita State Univ Elect & Comp Engn Dept Wichita KS USA Univ Sharjah Elect & Comp Engn Dept Sharjah U Arab Emirates

To better design AI processors, it is critical to characterize artificial intelligence (AI) workloads and contrast them to normal personal computer (PC) workloads. In this work, we profiled the AIBench and PassMark PerformanceTest benchmarks with the Intel oneAPI VTune Profiler on a multi-core computer. We captured and contrasted the various CPU and platform metrics and event counts for these two distinct benchmarks. Using the Orange 3.0 data mining tool, and based on the captured profile metrics and event counts, we then trained and tested 9 machine learning (ML) models to classify the CPIs and elapsed times of the various tests of these two benchmarks, including inference and training tests in AIBench, and CPU, memory, graphics, and disk tests in PassMark. The linear regression machine learning model emerged as the best clocks per instruction (CPI) classifier, while the neural network model with 4 hidden layers was the best elapsed time classifier. This machine learning classification can help in predicting the CPI and elapsed time and distinguish between AI and standard PC workloads based on the profiled application(s) and captured profile metrics and event counts. The stressed computer units identified by this detailed profiling work and exercised by the benchmark tests can also guide future AI processor design improvements.

关键词： Benchmark testing Artificial intelligence Computational modeling machine learning Training Graphics processing units Program processors AI workloads Tensorflow PassMark PerformanceTest AIBench workload characterization event counts benchmark profiling machine learning classification VTune

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machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 261卷 120054-120054页

作者： Jiao, Changwei Xu, Zhanpeng Bian, Qiuwan Forsberg, Erik Tan, Qin Peng, Xin He, Sailing Zhejiang Univ Ctr Opt & Electromagnet Res Natl Engn Res Ctr Opt Instruments Zhejiang Prov Key Lab Sensing Technol Hangzhou 310058 Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Peoples R China

A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 lm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Tetrastigma hemsleyanum Dual-mode microscopic hyperspectral imager machine learning classification Geographical origin Medicinal variety

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Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

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FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 2022年第59期16卷 172-187页

作者： Nguyen, Tran-Hieu Vu, Anh-Tuan Hanoi Univ Civil Engn Hanoi Vietnam

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a novel method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 40% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses.

关键词： Structural Optimization machine learning classification Differential Evolution Transmission Tower

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A machine learning Approach for classification of Sentence Polarity 3

A Machine Learning Approach for Classification of Sentence P...

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3rd International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Akkarapatty, Neethu Raj, Nisha S. SCMS Sch Engn & Technol Dept Comp Sci Informat Syst Emakulam India

ISBN: (纸本)9781467391979

Opinion Mining is the process used to determine the attitude/opinion/emotion expressed by a person about a particular topic. Analyzing opinions is an integral part for making decisions. In the era of web, if a person wants to buy a product, he will look into the reviews and comments given by the experienced users in web. But it seems to be a tedious task to read the entire reviews available in the web. Hence people are interested in checking whether the review recommends to buy a product or not. If lot of reviews recommends to buy the product, user reach at a conclusion to buy the product, otherwise not to buy the product. In this study, machine learning approach is applied to the TripAdvisor dataset in order to develop an efficient review classification. For this work to be carried out, style markers are applied to each of the reviews. In the next stage, significant style markers are recognized with the help of some suitable feature selection method. Thus the reviews can be identified by developing a classifier using the style markers that help to characterize nature of reviews as positive or negative.

关键词： Opinion Mining machine learning classification PoS tags Aspects ensemble model Sentiment classification

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Comparison and analysis of accuracy of various machine learning algorithms in the classification of patients with Parkinson's disease 4

Comparison and analysis of accuracy of various machine learn...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Chen, Wenpei Liu, Qiwei Gao, Xuyan Geng, Yingbao Kan, Hongxing Anhui Univ Tradit Chinese Med Sch Pharmaceut Informat Engn Hefei Peoples R China

ISBN: (纸本)9798400708138

Parkinson's disease is a neurological disorder characterized by muscle stiffness, tremors, and slow movement. The early diagnosis of Parkinson's disease is of great significance for treatment and intervention, and the machine learning classification method based on biomedical characteristics can provide a new idea and means for the early diagnosis of Parkinson's disease. We selected Parkinson's Disease classification as a dataset, which is a dataset used for classification tasks and contains biomedical characteristic data for diagnosis of Parkinson's Disease. The dataset contained a total of 195 samples, of which 48 were patients with Parkinson's disease and 147 were healthy people. The data was preprocessed and the data was cooled based on correlation analysis, and the data set was divided into training set and test set according to the ratio of 7:3. 10 kinds of machine learning algorithms were used to predict the classification of Parkinson's disease patients, and the calculation accuracy, accuracy, recall rate, F1 score and other parameters were used to compare and evaluate the classification results of each machine learning. For further discussion and research.

关键词： machine learning classification XGBoost Prediction accuracy

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Tracking antibiotic resistance gene pollution from different sources using machine-learning classification

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MICROBIOME 2018年第1期6卷 93-93页

作者： Li, Li-Guan Yin, Xiaole Zhang, Tong Univ Hong Kong Dept Civil Engn Environm Biotechnol Lab Pokfulam Rd Hong Kong 999077 Hong Kong Peoples R China

Background: Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non-point sources, as well as endogenous and exogenous cross-reactivity under complicated environmental conditions. Because of insufficient capability in identifying source-sink relationship within a quantitative framework, traditional antibiotic resistance gene (ARG) signatures-based source-tracking methods would hardly be a practical solution. Results: By combining broad-spectrum ARG profiling with machine-learning classification SourceTracker, here we present a novel way to address the question in the era of high-throughput sequencing. Its potential in extensive application was firstly validated by 656 global-scale samples covering diverse environmental types (e.g., human/animal gut, wastewater, soil, ocean) and broad geographical regions (e.g., China, USA, Europe, Peru). Its potential and limitations in source prediction as well as effect of parameter adjustment were then rigorously evaluated by artificial configurations with representative source proportions. When applying SourceTracker in region-specific analysis, excellent performance was achieved by ARG profiles in two sample types with obvious different source compositions, i.e., influent and effluent of wastewater treatment plant. Two environmental metagenomic datasets of anthropogenic interference gradient further supported its potential in practical application. To complement general-profile-based source tracking in distinguishing continuous gradient pollution, a few generalist and specialist indicator ARGs across ecotypes were identified in this study. Conclusion: We demonstrated for the first time that the developed source-tracking platform when coupling with proper experiment design and efficient metagenomic analysis tools will have significant implications f

关键词： Antibiotic resistance gene Source tracking machine learning classification Metagenomics

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Enhanced Binary Kepler Optimization Algorithm for effective feature selection of supervised learning classification

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JOURNAL OF BIG DATA 2025年第1期12卷 1-67页

作者： Abd El-Mageed, Amr A. Abohany, Amr A. Hosny, Khalid M. Sohag Univ Dept Informat Syst Sohag 82511 Egypt Damanhour Univ Fac Comp & Informat Damanhour Egypt Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt

This study proposes an Enhanced Binary Kepler Optimization Algorithm (BKOA-MUT) improves feature selection (FS) by integrating Kepler's planetary motion laws with DE/rand and DE/best Mutation Approach. BKOA-MUT balances exploration and exploitation, effectively guiding search for optimal feature subsets. BKOA-MUT was evaluated using k-Nearest Neighbors (k-NN) and Support Vector machine (SVM) on 25 UCI benchmarks, including three large-scale ones. It outperformed recent Meta-heuristic Algorithms (MHAs) in accuracy, feature reduction, and computational efficiency. The algorithm showed rapid convergence, minimal feature selection, and scalability, making it a robust and adaptable tool for enhancing FS in machine learning, validated through the Wilcoxon rank-sum test.

关键词： Feature selection Meta-heuristic Algorithms Kepler Optimization Algorithm DE/rand and DE/best Mutation Approach machine learning classification

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classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

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MATERIALS RESEARCH EXPRESS 2024年第11期11卷 116507页

作者： Saxena, Pragya Bongale, Arunkumar Symbiosis Skills & Profess Univ SSPU Pune 412101 MH India Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 MH India

Aluminium alloy-based surface composites with hard reinforcement particles have a wide scope in the aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing ( FSP ) with varying parameters are investigated for the faults that occurred during the fabrication process. In this study, Al6061 alloy surface composites reinforced with copper and graphene are fabricated using Friction Stir Processing ( FSP ) on a CNC milling machine. The process involves heat generation through friction, leading to a uniform distribution of reinforcement particles in the matrix. Various sensors ( vibration, current, and dynamometer) are used to monitor specific parameters during the FSP experimentation. The Taguchi L27 orthogonal array is employed to design experiments for analyzing sensor data from vibration sensors, current sensors, and dynamometers during composite fabrication. Data is collected in the time domain and processed using Python libraries. Also, it explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano- powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data ( vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories ( e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Then, various types of feature selection techniques, viz fi lter, wrapper, and embedded methods are implemented to select the most relevant features. The selected subset of features from all selection methods used are applied to different machine learning and ensemble learning classifiers, and their performances are evaluated. The optimal combinations of the type of fea

关键词： composites defects feature selection sensor data machine learning classification

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