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Using a Hybrid of AI and template-based method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG

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JMIR Formative Research 2025年 9卷 e65726页

作者： Kıyak, Yavuz Selim Kononowicz, Andrzej A. Department of Medical Education and Informatics Faculty of Medicine Gazi University Ankara Turkey Department of Bioinformatics and Telemedicine Jagiellonian University Medical College Kraków Poland

Background: template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. Medical education, a field that relies heavily on both formative and summative assessments with multiple choice questions, is in dire need of AI-based support for the efficient automatic generation of items. Objective: We aimed to propose a hybrid AIG to demonstrate whether it is possible to generate item templates using AI in the field of medical education. methods: This is a mixed-methods methodological study with proof-of-concept elements. We propose the hybrid AIG method as a structured series of interactions between a human subject matter expert and AI, designed as a collaborative authoring effort. The method leverages AI to generate item models (templates) and cognitive models to combine the advantages of the two AIG approaches. To demonstrate how to create item models using hybrid AIG, we used 2 medical multiple-choice questions: one on respiratory infections in adults and another on acute allergic reactions in the pediatric population. Results: The hybrid AIG method we propose consists of 7 steps. The first 5 steps are performed by an expert in a customized AI environment. These involve providing a parent item, identifying elements for manipulation, selecting options and assigning values to elements, and generating the cognitive model. After a final expert review (Step 6), the content in the template can be used for item generation through a traditional (non-AI) software (Step 7). We showed that AI is capable of generating item templates for AIG under the control of a human expert in only 10 minutes. Leveraging AI in template development made it less challenging. Conclusions: The hybrid AIG method transcends the traditional template-based approach by marrying the “art” t

关键词： AI algorithm artificial intelligence automatic item generation ChatGPT expert human-AI human-AI collaboration hybrid hybrid AIG large language models medical education medical education mixed-method multiple-choice multiple-choice question template-based method

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TM-generation model: a template-based method for automatically solving mathematical word problems

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JOURNAL OF SUPERCOMPUTING 2021年第12期77卷 14583-14599页

作者： Lee, Donggeon Ki, Kyung Seo Kim, Bugeun Gweon, Gahgene Seoul Natl Univ Seoul South Korea

In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word problem-solving task. In automatic mathematical word problem-solving task, a machine learning model should deduce an answer to a given problem by acquiring implied numeric information. To build a robust model that can sufficiently utilize numeric information to solve various mathematical word problems, such a model should address two challenges: (1) filling in missing world knowledge required to solve the given mathematical word problem, and (2) understanding the implied relationship between numbers and variables. To address these two challenges, we propose template-based multitask generation (TM-generation). To address challenge (1), we utilize the state-of-the-art language models called ELECTRA. To address challenge (2), we propose an operator identification layer that models the relationship between numbers and variables. Our experimental results show that using the MAWPS and Math23k datasets, state-of-the-art performance was achieved: 85.2% in MAWPS and 85.3% in Math23k.

关键词： Natural language understanding Mathematical word problem solving Multi-task learning template-based method

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Controlling morphology and particle size of hollow poly(styrene-divinylbenzene) microspheres fabricated by template-based method

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JOURNAL OF SAUDI CHEMICAL SOCIETY 2018年第6期22卷 644-653页

作者： Chen, Cheng Zhang, Liping Li, Min Tian, Anli Wang, Chunxia Fu, Shaohai Jiangnan Univ Jiangsu Engn Res Ctr Digital Text Inkjet Printing Key Lab Ecotext Minist Educ Wuxi 214122 Jiangsu Peoples R China

Hollow poly(styrene-divinylbenzene) (P(S-DVB)) microspheres were fabricated via template-based method including synthesis of silica particles by sol-gel method, preparation of silica/P(S-DVB) particles by dispersion polymerization and chemical etching of silica cores by NaOH solution. TEM, FTIR and TG analyses confirmed that the hollow P(S-DVB) microspheres were successfully obtained. The morphology of hollow P(S-DVB) microspheres could be controlled by adjusting the amounts of DVB, AIBN and VTES, and the round-ball-like hollow P(S-DVB) microspheres were fabricated when the amount of DVB, AIBN and VTES was 30.0 wt%, 5.0 wt % and 30.0 vol% respectively. Both the size of silica particles and amount of monomers were regarded as the two key factors to control the particle size of the round-ball-like hollow P(SDVB) microspheres. (C) 2017 King Saud University. Production and hosting by Elsevier B.V.

关键词： Hollow poly(styrene-divinyl benzene)microspheres template-based method Sol-gel method Dispersion polymerization Chemical etching

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Dynamic integration of feature-and template-based methods improves the prediction of conformational B cell epitopes

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STRUCTURE 2025年第4期33卷 798-807.e4页

作者： Shen, Yueyue Jiang, Zheng Liu, Rong Huazhong Agr Univ Coll Informat Hubei Key Lab Agr Bioinformat Wuhan 430070 Peoples R China

The accurate prediction of conformational epitopes promotes our understanding of antigen-antibody interactions. All existing algorithms depend on a feature-based strategy, which limits their performance. A template-based strategy can provide complementary information, and the interplay between these two strategies could improve the prediction of epitopes. Here, we present DynaBCE, a dynamic ensemble algorithm to effectively identify conformational B cell epitopes (BCEs). Using novel handcrafted structural descriptors and embeddings from protein language models, we developed machine learning and deep learning modules based on boosting algorithms and geometric graph neural networks, respectively. Furthermore, we built a template module by leveraging known structural template information and transformer-based algorithms to capture binding signatures. Finally, we integrated the three modules using a dynamic weighting approach to maximize the strength of each module for different samples. DynaBCE achieved promising results for both native and predicted structures and outperformed previous methods as demonstrated in various evaluation scenarios.

关键词： antigen-antibody interactions B cell epitopes structural descriptors deep learning template-based method dynamic integration predicted structure

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Robust Classification method of Tumor Subtype by Using Correlation Filters

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第2期9卷 580-591页

作者： Wang, Shu-Lin Zhu, Yi-Hai Jia, Wei Huang, De-Shuang Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Chinese Acad Sci Hefei Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Anhui Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available data sets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter-based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only a few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.

关键词： Correlation filters gene expression profiles tumor classification template-based method model-based method

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Preparation of SiC hollow spheres by template method and its performance of purification and regeneration

Preparation of SiC hollow spheres by template method and its...

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International Conference on Biotechnology, Chemical and Materials Engineering (CBCME 2011)

作者： Fan, Zimin Xian Univ Sci & Technol Dept Mat Sci & Engn Xian 710054 Peoples R China

ISBN: (纸本)9783037853078

SiC hollow spheres were prepared by template mothod with polyethylene ball. The phase composition, morphology, momicrostructure, porosity and resistivity of the SiC hollow spheres were characterized by X-ray diffration, scanning electron microscopy, porosity test and voltammetry. The mechanism for formation of porous hollow pheres were discussed. The purification efficiency and performance of regeneration under ohmic heating were investigated using the prepared SiC hollow spheres for adsorbing particles in exhaust gas of diesel engine. The results show that the SiC hollow spheres comprise inner hollow and outer shell with much more micropores, and the thickness of the shells with microporosity having 80-120 nm is obout 300 mu m, The SiC hollow spheres consists mainly alpha-SiC, beta-SiC and with a small quantity C, SiO2 and free Si. The purification efficiency could reach to 94 % or more for adsorbing particles in exhaust gas of diesel engine exhaust gas in 90 min because of its especial pore structure. Within ohmic heating, the SiC hollow spheres could work in a constant state of purification and and can its efficiency of purification could reach to 96% or more of efficiency in 180 min because of its excellent electro-heating performance.

关键词： Silicon Carbide Hollow Pheres Purification Regeneration template-based method

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Face recognition: A study in information fusion using fuzzy integral

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PATTERN RECOGNITION LETTERS 2005年第6期26卷 719-733页

作者： Kwak, KC Pedrycz, W Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada Chungbuk Natl Univ Dept Elect & Elect Engn Cheongju 361763 South Korea Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

We study two approaches to fuzzy information fusion for face recognition involving aggregation of local and global face information and a wavelet decomposition approach. The former is concerned with a fuzzy information fusion involving both feature-based (e.g., eye, nose, and mouth) and template-based (global face) approaches to face recognition. The latter is associated with fuzzy information fusion based on four subimages (that is approximation, horizontal, vertical, and diagonal detailed images) using wavelet decomposition method. Making use of these two approaches, we design classifiers based on the well-known fisherface method. We demonstrate that the proposed method comes with better performance when compared with other template-based techniques and shows substantial insensitivity to large variation in light direction and facial expression. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. The experimental results produced for FERET face database with 600 frontal face images corresponding to 200 subjects quantifies the performance of the classifier and contrast it with some other approaches existing in the literature. (c) 2004 Elsevier B.V. All rights reserved.

关键词： face recognition fuzzy information fusion feature-based method template-based method wavelet decomposition fisherfaces choquet fuzzy integral

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Facile preparation of fluorescent gold nanocluster via polysaccharide-templated approach and its application for Cu²⁺ sensing

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SENSORS AND ACTUATORS B-CHEMICAL 2017年 248卷 63-70页

作者： Wen, Ruizhi Li, Hongchang Chen, Bo Wang, Liping Hunan Normal Univ Key Lab Phytochem R&D Hunan Prov Changsha 410081 Hunan Peoples R China Hunan Normal Univ Key Lab Chem Biol & Tradit Chinese Med Res Minist Educ Changsha 410081 Hunan Peoples R China Cent South Univ Forestry & Technol Coll Sci Changsha 410081 Hunan Peoples R China Hunan Anal & Testing Ctr Changsha 410004 Hunan Peoples R China

In this report, we have constructed a simple, eco-friendly and one-pot synthetic strategy for the synthesis of fluorescent gold nanoclusters (GNCs) using a native polysaccharide (chondroitin sulfate) as the novel template. Moreover, we used some analytical instruments for characterizing the formation of Au clusters, containing ultraviolet-visible spectrometer, fluorescence spectrometer, high resolution transmission electron microscope (HRTEM) and X-ray photoelectron spectroscopy (XPS). The obtained data indicated that the Au nanoclusters were successfully formed. The chondroitin sulfate-capped gold nanoclusters (CS-GNCs) displayed excitation and emission bands at 450 and 520 nm, respectively. The fluorescence quantum yield was 4.78%. We further used CS-stabilized gold nanoclusters selectively and sensitively for sensing Cu2+ in real samples via non-covalent d(10)-d(10) metallophilic bond. The linear range of the method was 0.01-1.8 mu M with the limit of detection (LOD) was 8 nM (signal/noise = 3), and the recoveries of the spiked samples in lake water and tap water samples ranged from 90% to 108%, suggesting that the proposed method could be used in the practice detection of Cu2+ (C) 2017 Elsevier B.V. All rights reserved.

关键词： Polysaccharide Gold nanocluster template-based method Cu2+ sensing

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Protein Fold Recognition based on Auto-Weighted Multi-View Graph Embedding Learning Model

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第6期18卷 2682-2691页

作者： Yan, Ke Wen, Jie Xu, Yong Liu, Bin Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Guangdong Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China

Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods that combine the various features to improve predictive performance remain the challenge problems. In this study, we proposed two novel algorithms: AWMG and EMfold. AWMG used a novel predictor based on the multi-view learning framework for fold recognition. Each view was treated as the intermediate representation of the corresponding data source of proteins, including the evolutionary information and the retrieval information. AWMG calculated the auto-weight for each view respectively and constructed the latent subspace which contains the common information shared by different views. The marginalized constraint was employed to enlarge the margins between different folds, improving the predictive performance of AWMG. Furthermore, we proposed a novel ensemble method called EMfold, which combines two complementary methods AWMG and DeepSS. The later method was a template-based algorithm using the SPARKS-X and DeepFR programs. EMfold integrated the advantages of template-based assignment and machine learning classifier. Experimental results on the two widely datasets (LE and YK) showed that the proposed methods outperformed some state-of-the-art methods, indicating that AWMG and EMfold are useful tools for protein fold recognition.

关键词： Proteins Predictive models Protein sequence Feature extraction Prediction algorithms Protein fold recognition template-based method Multi-view learning method Laplacian matrix

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Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第5期18卷 2008-2016页

作者： Yan, Ke Wen, Jie Liu, Jin-Xing Xu, Yong Liu, Bin Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Shenzhen Key Lab Visual Object Detect & Recognit Guangzhou 518055 Guangdong Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Rizhao 276826 Shandong Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 10081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 10081 Peoples R China

Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment scores between query-template protein pairs and the other is the machine learning method based on the feature representation and classifier. These two approaches have their own advantages and disadvantages. Can we combine these methods to establish more accurate predictors for protein fold recognition? In this study, we made an initial attempt and proposed two novel algorithms: TSVM-fold and ESVM-fold. TSVM-fold was based on the Support Vector Machines (SVMs), which utilizes a set of pairwise sequence similarity scores generated by three complementary template-based methods, including HHblits, SPARKS-X, and DeepFR. These scores measured the global relationships between query sequences and templates. The comprehensive features of the attributes of the sequences were fed into the SVMs for the prediction. Then the TSVM-fold was further combined with the HHblits algorithm so as to improve its generalization ability. The combined method is called ESVM-fold. Experimental results in two rigorous benchmark datasets (LE and YK datasets) showed that the proposed methods outperform some state-of-the-art methods, indicating that the TSVM-fold and ESVM-fold are efficient predictors for protein fold recognition.

关键词： Proteins Support vector machines Feature extraction Machine learning Training Hidden Markov models Protein engineering Protein fold recognition SVMs template-based method Pairwise sequence similarity scores

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