Journal of Medical Signals & Sensors最新文献

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Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm. 利用遗传算法增强微阵列图像的对比度并划分网格
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_65_22
Nayyer Mostaghim Bakhshayesh, Mousa Shamsi, Faegheh Golabi
{"title":"Microarray Images Contrast Enhancement and Gridding Using Genetic Algorithm.","authors":"Nayyer Mostaghim Bakhshayesh, Mousa Shamsi, Faegheh Golabi","doi":"10.4103/jmss.jmss_65_22","DOIUrl":"10.4103/jmss.jmss_65_22","url":null,"abstract":"<p><strong>Background: </strong>Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error-prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes.</p><p><strong>Methods: </strong>In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs.</p><p><strong>Results: </strong>The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi-decomposition histogram equalization (M-DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M-DHE methods, respectively.</p><p><strong>Conclusions: </strong>The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"6"},"PeriodicalIF":1.3,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11111130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591598","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
The Optimal Model for Copy-Move Forgery Detection in Medical Images. 医学影像中复制移动伪造检测的最佳模型。
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_35_22
Ehsan Amiri, Ahmad Mosallanejad, Amir Sheikhahmadi
{"title":"The Optimal Model for Copy-Move Forgery Detection in Medical Images.","authors":"Ehsan Amiri, Ahmad Mosallanejad, Amir Sheikhahmadi","doi":"10.4103/jmss.jmss_35_22","DOIUrl":"10.4103/jmss.jmss_35_22","url":null,"abstract":"<p><strong>Background: </strong>Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery.</p><p><strong>Methods: </strong>The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated.</p><p><strong>Results: </strong>The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images.</p><p><strong>Conclusions: </strong>It concluded that our method for CMFD in the medical images was more accurate.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"5"},"PeriodicalIF":1.3,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11111128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592616","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
Wireless Patient Monitoring System Based on Smart Wristbands and Central user Interface Software. 基于智能腕带和中央用户界面软件的无线病人监护系统。
Journal of Medical Signals & Sensors Pub Date : 2024-02-14 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_47_22
Mohammad Hossein Vafaie, Ebrahim Ahmadi Beni
{"title":"Wireless Patient Monitoring System Based on Smart Wristbands and Central user Interface Software.","authors":"Mohammad Hossein Vafaie, Ebrahim Ahmadi Beni","doi":"10.4103/jmss.jmss_47_22","DOIUrl":"10.4103/jmss.jmss_47_22","url":null,"abstract":"<p><p>In this article, a patient monitoring system is proposed that is able to obtain heart rate and oxygen saturation (SpO<sub>2</sub>) levels of patients, identify abnormal conditions, and inform emergency status to the nurses. The proposed monitoring system consists of smart patient wristbands, smart nurse wristbands, central monitoring user interface (UI) software, and a wireless communication network. In the proposed monitoring system, a unique smart wristband is dedicated to each of the patients and nurses. To measure heart rate and SpO<sub>2</sub> level, a pulse oximeter sensor is used in the patient wristbands. The output of this sensor is transferred to the wristband's microcontroller where heart rate and SpO<sub>2</sub> are calculated through advanced signal processing algorithms. Then, the calculated values are transmitted to central UI software through a wireless network. In the UI software, received values are compared with their normal values and a predefined message is sent to the nurses' wristband if an abnormal condition is identified. Whenever this message is received by a nurse's wristband, an acoustic alarm with vibration is generated to inform an emergency status to the nurse. By doing so, health services are delivered to the patients more quickly and as a result, the probability of the patient recovery is increased effectively.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140178706","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
Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays. 利用预训练卷积神经网络进行迁移学习,实现前列腺癌组织芯片格雷欣分级自动化
Journal of Medical Signals & Sensors Pub Date : 2024-02-14 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_42_22
Parisa Gifani, Ahmad Shalbaf
{"title":"Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays.","authors":"Parisa Gifani, Ahmad Shalbaf","doi":"10.4103/jmss.jmss_42_22","DOIUrl":"10.4103/jmss.jmss_42_22","url":null,"abstract":"<p><strong>Background: </strong>The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer's aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA).</p><p><strong>Methods: </strong>Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label.</p><p><strong>Results: </strong>Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98.</p><p><strong>Conclusion: </strong>Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176925","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
Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling. 利用统计建模实现视网膜光学相干断层扫描图像的超分辨率
Journal of Medical Signals & Sensors Pub Date : 2024-02-14 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_58_22
Sahar Jorjandi, Zahra Amini, Hossein Rabbani
{"title":"Super-resolution of Retinal Optical Coherence Tomography Images Using Statistical Modeling.","authors":"Sahar Jorjandi, Zahra Amini, Hossein Rabbani","doi":"10.4103/jmss.jmss_58_22","DOIUrl":"10.4103/jmss.jmss_58_22","url":null,"abstract":"<p><strong>Background: </strong>Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view.</p><p><strong>Methods: </strong>In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator.</p><p><strong>Results: </strong>The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors.</p><p><strong>Conclusion: </strong>The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950312/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176923","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
Tensor Ring Based Image Enhancement. 基于张量环的图像增强技术
Journal of Medical Signals & Sensors Pub Date : 2024-02-14 eCollection Date: 2024-01-01 DOI: 10.4103/jmss.jmss_32_23
Farnaz Sedighin
{"title":"Tensor Ring Based Image Enhancement.","authors":"Farnaz Sedighin","doi":"10.4103/jmss.jmss_32_23","DOIUrl":"10.4103/jmss.jmss_32_23","url":null,"abstract":"<p><strong>Background: </strong>Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement.</p><p><strong>Method: </strong>In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm.</p><p><strong>Result: </strong>This enables the method to do the super-resolution and de-noising simultaneously.</p><p><strong>Conclusion: </strong>Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"14 ","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140176924","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
Erratum: Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network. 勘误:使用有效连接网络对自闭症患者大脑电信号的研究。
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-01-23 eCollection Date: 2025-01-01 DOI: 10.4103/jmss.jmss_12_25
{"title":"Erratum: Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network.","authors":"","doi":"10.4103/jmss.jmss_12_25","DOIUrl":"https://doi.org/10.4103/jmss.jmss_12_25","url":null,"abstract":"<p><p>[This corrects the article 24 in vol. 14, PMID: 39234588.].</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"4"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543754","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
Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition. 2023年伊斯法罕人工智能赛事:黄斑病理检测大赛。
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-01-23 eCollection Date: 2025-01-01 DOI: 10.4103/jmss.jmss_47_24
Farnaz Sedighin, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Reza Mokhtari, Maryam Mohammadi, Mohadese Ramezani, Mahnoosh Tajmirriahi, Hossein Rabbani
{"title":"Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition.","authors":"Farnaz Sedighin, Maryam Monemian, Zahra Zojaji, Ahmadreza Montazerolghaem, Mohammad Amin Asadinia, Seyed Mojtaba Mirghaderi, Seyed Amin Naji Esfahani, Mohammad Kazemi, Reza Mokhtari, Maryam Mohammadi, Mohadese Ramezani, Mahnoosh Tajmirriahi, Hossein Rabbani","doi":"10.4103/jmss.jmss_47_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_47_24","url":null,"abstract":"<p><strong>Background: </strong>Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison.</p><p><strong>Methods: </strong>Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms.</p><p><strong>Results: </strong>A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results.</p><p><strong>Conclusions: </strong>The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"3"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143542876","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
Isfahan Artificial Intelligent 2023 Competitions. 2023年伊斯法罕人工智能大赛。
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-01-23 eCollection Date: 2025-01-01 DOI: 10.4103/jmss.jmss_78_24
Samaneh Ghasemi, Zahra Baharlouei, Hossein Rabbani
{"title":"Isfahan Artificial Intelligent 2023 Competitions.","authors":"Samaneh Ghasemi, Zahra Baharlouei, Hossein Rabbani","doi":"10.4103/jmss.jmss_78_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_78_24","url":null,"abstract":"","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"1"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543138","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
Isfahan Artificial Intelligence Event 2023: Drug Demand Forecasting. 2023年伊斯法罕人工智能事件:药物需求预测。
IF 1.3
Journal of Medical Signals & Sensors Pub Date : 2024-01-23 eCollection Date: 2025-01-01 DOI: 10.4103/jmss.jmss_59_24
Meysam Jahani, Zahra Zojaji, AhmadReza Montazerolghaem, Maziar Palhang, Reza Ramezani, Ahmadreza Golkarnoor, Alireza Akhavan Safaei, Hossein Bahak, Pegah Saboori, Behnam Soufi Halaj, Ahmad R Naghsh-Nilchi, Fatemeh Mohamadpoor, Saeid Jafarizadeh
{"title":"Isfahan Artificial Intelligence Event 2023: Drug Demand Forecasting.","authors":"Meysam Jahani, Zahra Zojaji, AhmadReza Montazerolghaem, Maziar Palhang, Reza Ramezani, Ahmadreza Golkarnoor, Alireza Akhavan Safaei, Hossein Bahak, Pegah Saboori, Behnam Soufi Halaj, Ahmad R Naghsh-Nilchi, Fatemeh Mohamadpoor, Saeid Jafarizadeh","doi":"10.4103/jmss.jmss_59_24","DOIUrl":"https://doi.org/10.4103/jmss.jmss_59_24","url":null,"abstract":"<p><strong>Background: </strong>The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time.</p><p><strong>Methods: </strong>Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions-2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results.</p><p><strong>Results: </strong>A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics.</p><p><strong>Conclusions: </strong>In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"15 ","pages":"2"},"PeriodicalIF":1.3,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543771","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
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