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Utility of machine learning algorithms in Predicting Preoperative Lymph Node Metastasis in Patients With Rectal Cancer Based on Three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data

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JOURNAL OF ULTRASOUND IN MEDICINE 2023年第11期42卷 2615-2627页

作者： Huang, Weiqin Lin, Ruoxuan Ke, Xiaohui Ni, Shixiong Zhang, Zhen Tang, Lina Fujian Med Univ Fujian Canc Hosp Clin Oncol Sch Dept Ultrasonog 420 Fuma Rd Fuzhou 350014 Peoples R China China Med Univ Affiliated Hosp 1 Dept Ultrasound 155 Nanjing St Shenyang Peoples R China

Background-We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer. Methods-Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model. Results-Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis;the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively. Conclusions-Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.

关键词： endorectal ultrasound extreme gradient boosting algorithm lymph node metastasis machine learning algorithm rectal cancer

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Quantification of Celiac Disease Severity Using Video Capsule Endoscopy: A Comparison of Human Experts and machine learning algorithms

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CURRENT MEDICAL IMAGING 2023年第12期19卷 1455-1462页

作者： Zammit, Stefania Chetcuti McAlindon, Mark E. Greenblatt, Elliot Maker, Michael Siegelman, Jenifer Leffler, Daniel A. Yardibi, Ozlem Raunig, David Brown, Terry Sidhu, Reena Sheffield Teaching Hosp NHS Hosp Trust Acad Unit Gastroenterol & Hepatol Sheffield S10 2JF England Kon Minolta Co Invicro Boston MA USA Takeda Pharmaceut Inc Co Cambridge MA USA Univ Sheffield Dept Infect Immun & Cardiovasc Dis Sheffield S10 2RX England

Background Video capsule endoscopy (VCE) is an attractive method for diagnosing and objectively monitoring disease activity in celiac disease (CeD). Its use, facilitated by artificial intelligence-based tools, may allow computer-assisted interpretation of VCE studies, transforming a subjective test into a quantitative and reproducible measurement tool. Objective To evaluate and compare objective CeD severity assessment as determined with VCE by expert human readers and a machine learning algorithm (MLA). Methods Patients & GE;18 years with histologically proven CeD underwent VCE. Examination frames were scored by three readers from one center and the MLA, using a 4-point ordinal scale for assessing the severity of CeD enteropathy. After scoring, curves representing CeD severity across the entire small intestine (SI) and individual tertiles (proximal, mid, and distal) were fitted for each reader and the MLA. All comparisons used Krippendorff's alpha;values > 0.8 represent excellent to 'almost perfect' inter-reader agreement. Results VCEs from 63 patients were scored. Readers demonstrated strong inter-reader agreement on celiac villous damage (alpha=0.924), and mean value reader curves showed similarly excellent agreement with MLA curves (alpha=0.935). Average reader and MLA curves were comparable for mean and maximum values for the first SI tertile (alphas=0.932 and 0.867, respectively) and the mean value over the entire SI (alpha=0.945). Conclusion A novel MLA demonstrated excellent agreement on whole SI imaging with three expert gastroenterologists. An ordinal scale permitted high inter-reader agreement, accurately and reliably replicated by the MLA. Interpreting VCEs using MLAs may allow automated diagnosis and disease burden assessment in CeD.

关键词： Celiac disease machine learning algorithm quantitative analysis small bowel imaging video capsule endoscopy MPEG

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Discrimination of ginseng origin by using laser-induced breakdown spectrum and machine learning algorithms

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2023年第5期65卷 1248-1254页

作者： Zheng, Kexin Li, Xiaolin Song, Shaozhong Gao, Xun Changchun Univ Sci & Technol Sch Phys Changchun Peoples R China Jilin Normal Univ Coll Tourism & Geog Sci Siping Peoples R China Jilin Engn Normal Univ Sch Data Sci & Artificial Intelligence Changchun Peoples R China Jilin Prov Key Lab Ultrafast & Extreme Ultraviole Changchun Peoples R China

In this paper, the ginsengs from five ginseng origins are discriminated by using laser- induced breakdown spectrum (LIBS) combined with random forest- support vector machine (RF- SVM) and random forest- multilayer perception (RF- MLP) machine learning algorithms. The raw LIBS of ginseng is pretreated by using the wavelet threshold method, denoise the background information and normalazation to improve the signal- to- background ratio and the experimental reliability. The RF algorithm is used to select 10 characteristic spectral lines as the input vectors of the MLP and the SVM models to identify the ginseng orgin. The experimental results show that the discrimination accuracy rates of RF- MLP and RF- SVM models are 99.75% and 99.5%, respectively. The disrimination accuracy of ginseng origin used in the RF-MLP machine algorithm model is slightly higher than that of the RF- SVM model, and then calculated the speed of the RF- MLP model is faster than the RF- SVM model. The results show that LIBS combined with machine algorithms are both promising rapid discrimination methods for ginseng origin.

关键词： ginseng origin laser-induced breakdown spectroscopy machine learning algorithm random forest

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Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses

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JOURNAL OF EVIDENCE BASED MEDICINE 2023年第2期16卷 166-177页

作者： Min, Kaiyuan Cheng, Zhenshun Liu, Jiangfeng Fang, Yanhong Wang, Weichen Yang, Yehong Geldsetzer, Pascal Baernighausen, Till Yang, Juntao Liu, Depei Chen, Simiao Wang, Chen Chinese Acad Med Sci Peking Union Med Coll Sch Basic Med State Key Lab Med Mol BiolInst Basic Med Sci Beijing Peoples R China Wuhan Univ Dept Resp Med Zhongnan Hosp Wuhan Peoples R China Chinese Acad Med Sci Wuhan Res Ctr Infect Dis & Canc Wuhan Peoples R China Univ Hong Kong Fac Business & Econ Innovat & Informat Management Hong Kong Peoples R China Heidelberg Univ Heidelberg Inst Global Hlth HIGH Fac Med Heidelberg Germany Univ Hosp Heidelberg Univ Heidelberg Germany Stanford Univ Dept Med Div Primary Care & Populat Hlth Stanford CA USA Chinese Acad Med Sci & Peking Union Med Coll Beijing Peoples R China Natl Clin Res Ctr Resp Dis Beijing Peoples R China China Japan Friendship Hosp Ctr Resp Med Dept Pulm & Crit Care Med Beijing Peoples R China

ObjectiveTo determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables. MethodsMedical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods-machine learning algorithms and competing risk survival analyses-were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19. ResultsMore than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age >= 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. ConclusionsEarly-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.

关键词： competing risk survival analysis COVID-19 machine learning algorithm progression SARS-CoV-2

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Using machine learning algorithms for predicting real estate values in tourism centers

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SOFT COMPUTING 2023年第5期27卷 2601-2613页

作者： Alkan, Tansu Dokuz, Yesim Ecemis, Alper Bozdas, Asli Durduran, S. Savas Necmettin Erbakan Univ Engn Fac Geomat Engn Konya Turkey Nigde Omer Halisdemir Univ Engn Fac Comp Engn Nigde Turkey Nigde Omer Halisdemir Univ Engn Fac Geomat Engn Nigde Turkey

Along with the development of technology in recent years, artificial intelligence (machine learning) techniques that perform operations, such as learning, classification, association, optimization, and prediction, have started to be used on data on real estate according to the criteria affecting the value. Using artificial intelligence (machine learning) techniques, valuation processes are performed objectively and scientifically. In this study, machine learning techniques were employed to balance the real estate market, affected by the tourism sector in Alanya district of Antalya province, Turkey, and examine changes in value objectively and scientifically. First, the criteria affecting the real estate value were determined as structural and spatial, and data on real estate were obtained from the online real estate website. Then, the values of the real estate in the selected application area were predicted using machine learning algorithms (k-nearest neighbors, random forest, and support vector machines). Unlike studies in the literature, algorithm-based valuation using machine learning algorithms was performed instead of mathematical modeling. When analyzed for performance metrics, the best result was achieved with the support vector machines algorithm (0.73). Objective methods should be used to balance the exorbitant differences between real estate values, to regulate market conditions and to carry out a real estate valuation process free from speculative effects in coastal areas where tourism factor is effective. This study indicated the applicability of algorithm-based machine learning techniques in real estate valuation.

关键词： Real estate valuation machine learning algorithm k-nearest neighbors Random forest Support vector machines Geographic information systems

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machine learning algorithms for predicting occupants' behaviour in the manual control of windows for cross-ventilation in homes

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INDOOR AND BUILT ENVIRONMENT 2021年第8期30卷 1106-1123页

作者： Park, Junseok Jeong, Bongchan Chae, Young-Tae Jeong, Jae-Weon Hanyang Univ Dept Architectural Engn Seoul 04763 South Korea Cheongju Univ Dept Architectural Engn Cheongju South Korea

The manual control of windows is one of the common adaptive behaviours for occupants to adjust their indoor environment in homes. The cross-ventilation by the window opening provides a useful tool to control the thermal comfort and indoor air quality in homes. The objective of this study was to develop a modelling methodology for predicting individual occupant's behaviour relating to the manual control of windows by using machine learning algorithms. The proposed six machine learning algorithms were trained by the field monitoring data of 23 sample homes. The predictive performance of the machine learning algorithms was analysed. The algorithms predicted the occupant's behaviour more precisely compared with the logistic model. Among the algorithms, K-Nearest Neighbours (KNN) shows the best fitness with the monitored data set. The driving parameters of the manual control of windows in each sample home can be clearly drawn by the algorithms. The proposed machine learning algorithms can help to understand the influence of the occupant's behaviour on the indoor environment in buildings.

关键词： Occupant behaviour Window opening Cross-ventilation Indoor air quality Building simulation machine learning algorithm

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Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models

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CANCER CELL INTERNATIONAL 2024年第1期24卷 1-4页

作者： Xu, Fangshi Li, Zongyu Guan, Hao Ma, Jiancang Xi An Jiao Tong Univ Affiliated Hosp 2 Dept Vasc Surg 157 West Five Rd Xian 710004 Shaanxi Peoples R China

Bioinformatics models greatly contribute to individualized assessments of cancer patients. However, considerable research neglected some critical technological points, including comparisons of multiple modeling algorithms, evaluating gain effects of constructed model, comprehensive bioinformatics analyses and validation of clinical cohort. These issues are worthy of emphasizing, which will better serve future cancer research.

关键词： machine learning algorithm Gain effect Bioinformatics Prognostic model Clinical validation

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Design and Python Simulation of Cross-border E-commerce Logistics Pathways Based on machine learning algorithms 24

Design and Python Simulation of Cross-border E-commerce Logi...

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1st International Conference on Image Processing machine learning and Pattern Recognition

作者： Liu, Jian Lei, Fei Liu, Qi Yuan, Huan Guangdong Univ Sci & Technol Sch Finance & Econ Dongguan Peoples R China Guangdong Univ Sci & Technol Sch Management Dongguan Peoples R China

ISBN: (纸本)9798400707032

In the era of globalization, cross-border e-commerce has emerged as a significant driver of international trade, necessitating efficient logistics pathways to enhance delivery speed and reduce costs. This study presents a comprehensive approach to designing and simulating cross-border e-commerce logistics pathways using machine learning algorithms. We first identify the key factors influencing logistics efficiency, including geographical, regulatory, and demand variability aspects. Utilizing a dataset comprising historical shipping data, we apply various machine learning techniques, such as decision trees, random forests, and neural networks, to model and predict optimal logistics routes. The proposed model integrates real-time data processing and predictive analytics to dynamically adapt to changing conditions and optimize the logistics pathways in real-time. A Python-based simulation framework is developed to visualize and test the effectiveness of the proposed logistics pathways under different scenarios. The results demonstrate significant improvements in delivery times and cost reductions when compared to traditional logistics strategies. This research not only contributes to the field of logistics and supply chain management but also provides practical insights for e-commerce businesses seeking to enhance their operational efficiency in the competitive cross-border market. Future work will focus on refining the model with additional variables and exploring the integration of blockchain technology to further enhance transparency and traceability in cross-border logistics operations.

关键词： machine learning algorithm Python Simulation Random Forest Cross-border e-commerce logistics path design

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Application of machine learning algorithms in electronic medical records to predict amputation-free survival after first revascularization in patients with peripheral artery disease

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INTERNATIONAL JOURNAL OF CARDIOLOGY 2023年第1期383卷 175-184页

作者： Liu, Yang Xue, Junshuai Jiang, Jianjun Shandong Univ Qilu Hosp Dept Gen Surg Vasc Surg 107 Rd Wen Hua Xi Jinan Shandong Peoples R China Shandong Univ Qilu Hosp Dept Gen Surg 107 Rd Wen Hua Xi Jinan Shandong Peoples R China

Background: This study aimed to apply eight machine learning algorithms to develop the optimal model to predict amputation-free survival (AFS) after first revascularization in patients with peripheral artery disease (PAD).Methods: Among 2130 patients from 2011 to 2020, 1260 patients who underwent revascularization were randomly assigned to training set and validation set in an 8:2 ratio. 67 clinical parameters were analyzed by lasso regression analysis. Logistic regression, gradient boosting machine, random forest, decision tree, eXtreme gradient boosting, neural network, Cox regression, and random survival forest (RSF) were applied to develop prediction models. The optimal model was compared with GermanVasc score in testing set comprising patients from 2010. Results: The postoperative 1/3/5-year AFS were 90%, 79.4%, and 74.1%. Age (HR:1.035, 95%CI: 1.015-1.056), atrial fibrillation (HR:2.257, 95%CI: 1.193-4.271), cardiac ejection fraction (HR:0.064, 95%CI: 0.009-0.413), Rutherford grade >= 5 (HR:1.899, 95%CI: 1.296-2.782), creatinine (HR:1.03, 95%CI: 1.02-1.04), surgery duration (HR:1.03, 95%CI: 1.01-1.05), and fibrinogen (HR:1.292, 95%CI: 1.098-1.521) were independent risk factors. The optimal model was developed by RSF algorithm, with 1/3/5-year AUCs in training set of 0.866 (95% CI:0.819-0.912), 0.854 (95% CI:0.811-0.896), 0.844 (95% CI:0.793-0.894), in validation set of 0.741 (95% CI:0.580-0.902), 0.768 (95% CI:0.654-0.882), 0.836 (95% CI:0.719-0.953), and in testing set of 0.821 (95%CI: 0.711-0.931), 0.802 (95%CI: 0.684-0.919), 0.798 (95%CI: 0.657-0.939). The c-index of the model outperformed GermanVasc Score (0.788 vs 0.730). A dynamic nomogram was published on shinyapp (https://wyy ***/amputation/). Conclusion: The optimal prediction model for AFS after first revascularization in patients with PAD was developed by RSF algorithm, which exhibited outstanding prediction performance.

关键词： Peripheral artery disease Amputation-free survival machine learning algorithm Prediction model Electronic medical records Random survival forest

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Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2023年第23期41卷 14285-14298页

作者： Ma, Zhang-Wen Tang, Jia-Wei Liu, Qing-Hua Mou, Jing-Yi Qiao, Rui Du, Yan Wu, Chang-Yu Tang, Dao-Quan Wang, Liang Xuzhou Med Univ Sch Pharm Dept Pharmaceut Anal Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Jiangsu Key Lab New Drug Res & Clin Pharm Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Dept Intelligent Med Engn Xuzhou Jiangsu Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Taipa Macau Peoples R China Xuzhou Med Univ Sch Clin Med 1 Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Dept Clin Pharm Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Med Imaging Dept Biomed Engn Xuzhou Jiangsu Peoples R China Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Lab Med Guangzhou Guangdong Peoples R China

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

关键词： Mulberry leaf geographic origin Raman spectrometry machine learning algorithm convolutional neural network

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