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Keywords: Chicago; State:Illinois; United States; North and Central America; Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning EN Chicago State:Illinois United States North and Central America Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning 380 380 1 05/22/23 20230526 NES 230526 2023 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Fresh data on Machine Learning are presented in a new report. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. [Extracted from the article]

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

Algorithms, Diagnostics and Screening, Emerging Technologies, Fibromyalgia, Health and Medicine, Linear Discriminant Analysis, Machine Learning, Muscular Diseases and Conditions, Musculoskeletal Diseases and Conditions, Neuromuscular Diseases and Conditions, Rheumatic Diseases and Conditions Keywords: Algorithms; Diagnostics and Screening; Emerging Technologies; Fibromyalgia; Health and Medicine; Linear Discriminant Analysis; Machine Learning; Muscular Diseases and Conditions; Musculoskeletal Diseases and Conditions; Neuromuscular Diseases and Conditions; Rheumatic Diseases and Conditions EN Algorithms Diagnostics and Screening Emerging Technologies Fibromyalgia Health and Medicine Linear Discriminant Analysis Machine Learning Muscular Diseases and Conditions Musculoskeletal Diseases and Conditions Neuromuscular Diseases and Conditions Rheumatic Diseases and Conditions 158 158 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Hematology Week -- Research findings on fibromyalgia are discussed in a new report. [Extracted from the article]

A preprint abstract discusses the use of machine learning algorithms and heuristic stopping criteria to improve the efficiency of systematic reviews in education and educational psychology. The study conducted a retrospective screening simulation using 27 systematic reviews and found that active learning algorithms reduced screening workload by an average of 50% and saved an estimated 1.64 days of time. The Random Forests algorithm with Sentence Bidirectional Encoder Representations from Transformers performed the best. Additionally, the study found that heuristic stopping rules could retrieve 95% of relevant abstracts, with the most significant gains achieved by stopping the screening process after classifying 7% of irrelevant papers. The performance of the heuristic stopping criteria depended on the active learning algorithm used, the length, and the proportion of relevant papers in the database. This research provides empirical evidence on the effectiveness of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology. [Extracted from the article]

A new report from the University of Florence in Italy provides an extensive review of monitoring methods for electrical power lines, with a focus on high-voltage and medium-voltage systems. The objective of these techniques is to prevent catastrophic failures by detecting partial damage or deterioration of components and allowing for organized maintenance operations. The paper discusses the coordination of protection devices and the implementation of artificial intelligence algorithms to improve the reliability of the network. It also highlights diagnostic techniques, protection devices, and prognostic methods, emphasizing the role of artificial intelligence and offering guidelines for choosing between different approaches. [Extracted from the article]

A recent study conducted by researchers at Hamad Bin Khalifa University in Doha, Qatar, focuses on the use of machine learning and digital signal processing to improve the analysis of electroencephalogram (EEG) signals for medical diagnosis. EEG is a safe and common test that records electrical signals from the brain and has various applications in diagnosing conditions such as epilepsy, Alzheimer's, brain tumors, sleep disorders, and stroke. The study provides a comprehensive review of recent approaches for EEG preprocessing, feature extraction, and diagnosis of brain disorders, aiming to identify reliable algorithms for improving the accuracy of automatic diagnostic systems. The research concludes that the paper can be a valuable resource for researchers and practitioners in the field, offering insights into recent developments and future research directions. [Extracted from the article]

This article discusses the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cancer research. It highlights how these technologies are transforming the diagnosis, treatment, and prevention of cancer by providing sophisticated analytical capabilities. The paper explores the mathematical foundations and practical implementations of AI and ML techniques in various aspects of cancer research, including detection, diagnosis, prognosis, and personalized treatment strategies. It also reviews influential algorithms such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and clustering techniques, and their roles in improving diagnostic accuracy and treatment efficacy. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. Keywords: Algorithms; Blood Diseases and Conditions; Bloodstream Infection; Cyborgs; Diagnostics and Screening; Emerging Technologies; Genetics; Health and Medicine; Machine Learning; Sepsis; Septicemia EN Algorithms Blood Diseases and Conditions Bloodstream Infection Cyborgs Diagnostics and Screening Emerging Technologies Genetics Health and Medicine Machine Learning Sepsis Septicemia 399 399 1 10/16/23 20231019 NES 231019 2023 OCT 19 (NewsRx) -- By a News Reporter-Staff News Editor at Blood Weekly -- Data detailed on sepsis have been presented. [Extracted from the article]

A report from the University of Oslo discusses the use of machine learning algorithms and heuristic stopping criteria in systematic reviews in education and educational psychology. The study found that using machine learning algorithms for abstract screening reduced the workload of irrelevant records by an average of 58% and saved an estimated 1.66 days of time. The random forests algorithm with sentence bidirectional encoder representations from transformers performed the best. The study also found that heuristic stopping rules could retrieve 95% of relevant abstracts, with the best results achieved by stopping the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers. The research provides empirical evidence on the performance of machine learning screening algorithms in this field. [Extracted from the article]

The article focuses on a review from the University of Applied Sciences and Arts Western Switzerland about machine learning techniques for diagnosing Alzheimer's disease. Topics include the application of machine learning for early diagnosis of Alzheimer's and mild cognitive impairment, the use of magnetic resonance imaging in these techniques, and a comparative analysis of various algorithms' effectiveness in classification performance.

A report from Kerman University of Medical Sciences discusses the use of natural language processing (NLP) to extract cancer concepts from clinical notes automatically. The study systematically reviewed papers that used NLP methods to identify cancer concepts from clinical notes. The researchers found that rule-based algorithms were the most commonly used for concept extraction, and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies. The study concluded that the use of NLP for extracting cancer concepts has increased in recent years, and suggested that future studies use rule-based algorithms for extracting concepts of other diseases as well. [Extracted from the article]

Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. Keywords: Algorithms; American Chemical Society; Antiinfectives; Antimalarial Agents; Antiprotozoal Agents; Asia; Bone Research; Chemistry; China; Drugs and Therapies; Genetics; Health and Medicine; Metabolic Bone Diseases and Conditions; Mosquito-Borne Diseases; Musculoskeletal Diseases and Conditions; Osteoporosis EN Algorithms American Chemical Society Antiinfectives Antimalarial Agents Antiprotozoal Agents Asia Bone Research Chemistry China Drugs and Therapies Genetics Health and Medicine Metabolic Bone Diseases and Conditions Mosquito-Borne Diseases Musculoskeletal Diseases and Conditions Osteoporosis 357 357 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Zika & Mosquito Week -- Correction (): The paper's title has been corrected to "Deep Learning-Predicted Dihydroartemisinin Rescues Osteoporosis by Maintaining Mesenchymal Stem Cell Stemness through Activating Histone 3 Lys 9 Acetylation Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. [Extracted from the article]

Jammu Kashmir, India, Asia, Algorithms, Cancer, Cervical Cancer, Diagnostics and Screening, Health and Medicine, Oncology, Women's Health Keywords: Jammu Kashmir; India; Asia; Algorithms; Cancer; Cervical Cancer; Diagnostics and Screening; Health and Medicine; Oncology; Women's Health EN Jammu Kashmir India Asia Algorithms Cancer Cervical Cancer Diagnostics and Screening Health and Medicine Oncology Women's Health 1014 1014 1 10/03/23 20231006 NES 231006 2023 OCT 3 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Cervical Cancer is now available. [Extracted from the article]

This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity. Keywords: Algorithms; Artificial Intelligence; Cancer; Emerging Technologies; Gastric Cancer; Gastroenterology; Health and Medicine; Machine Learning; Oncology EN Algorithms Artificial Intelligence Cancer Emerging Technologies Gastric Cancer Gastroenterology Health and Medicine Machine Learning Oncology 427 427 1 03/23/23 20230321 NES 230321 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- A new study on artificial intelligence is now available. [Extracted from the article]

Keywords: Algorithms; Asia; Beijing Institute of Technology Press Co. Ltd; Business; China; Electronics; Emerging Technologies; Machine Learning; Nano-robot; Optoelectronics; Robotics; Technology EN Algorithms Asia Beijing Institute of Technology Press Co. Ltd Business China Electronics Emerging Technologies Machine Learning Nano-robot Optoelectronics Robotics Technology 1108 1108 1 03/23/23 20230310 NES 230310 2023 MAR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- A research paper by scientists at the Beijing Institute of Technology proposed an automated manipulation method for transporting microparticles actuated by optoelectronic tweezers (OETS). "Among various robotic micro-manipulation techniques, OETs has received widespread attention due to its superiority in terms of massively parallel manipulation." stated study author Huaping Wang, a professor at the Beijing Institute of Technology. [Extracted from the article]

Researchers from the Cleveland Clinic and IBM have published a paper in the Journal of Chemical Theory and Computation, exploring the application of quantum computing methods to protein structure prediction. Protein structures play a crucial role in understanding human health and disease, and accurately predicting these structures can aid in the development of effective therapies. The team used a combination of quantum and classical computing methods to overcome the limitations of classical computers in simulating protein folding. The initial results of their quantum-classical hybrid framework outperformed classical physics-based methods and demonstrated the ability to create accurate models without relying heavily on training data. The researchers plan to continue developing quantum algorithms to predict the structure of larger and more complex proteins. [Extracted from the article]

A recent study from Tianjin University in China discusses the progress made in developing methods for predicting epileptic seizures based on EEG data. The researchers highlight the importance of early prediction and intervention in improving the quality of life for epilepsy patients. The study reviews commonly used databases, methods for pre-processing and analyzing EEG data, and limitations of current algorithms. The researchers also compare deep learning algorithms with traditional machine learning methods and suggest that these new technologies may lead to significant breakthroughs in seizure prediction. This research has been peer-reviewed and provides valuable insights for further study in this field. [Extracted from the article]

A research paper from the Dr. Sivanthi Aditanar College of Engineering in Tiruchendur, India, explores the analysis of Bipolar Disorder (BD) using structural magnetic resonance imaging (MRI) and a supervised learning framework. The study aims to identify specific abnormalities in brain cells that indicate BD. The researchers employed a 3D texture analysis approach, extracting texture features from the MRI data and using a supervised learning algorithm called Support Vector Machine (SVM) to classify samples as healthy or abnormal. The results showed promising outcomes, with the SVM model achieving an accuracy of 90.42%. This research provides valuable insights into the application of supervised learning and MRI data analysis for understanding BD. [Extracted from the article]

To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. Keywords: Algorithms; Cancer; Diagnostics and Screening; Health and Medicine; Machine Learning; Oncology; Prostate Cancer; Prostatic Neoplasms; Support Vector Machines EN Algorithms Cancer Diagnostics and Screening Health and Medicine Machine Learning Oncology Prostate Cancer Prostatic Neoplasms Support Vector Machines 1492 1492 1 09/11/23 20230912 NES 230912 2023 SEP 12 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators publish new report on prostate cancer. [Extracted from the article]

A recent study conducted by researchers at the ECE Department in Vijayawada, India, examined the effectiveness of machine learning techniques for the early detection of type 2 diabetes. The researchers analyzed various papers and methodologies, assessing the accuracy of these methods. They proposed a comprehensive framework that utilizes current machine learning algorithms and incorporates novel technologies to enhance the accuracy and reliability of diabetes prediction. The study emphasizes the socio-economic impact of early diabetes detection and highlights the importance of timely interventions in reducing healthcare expenses and improving patient outcomes. The research contributes to global efforts to combat diabetes and improve public health. [Extracted from the article]

Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. Keywords: Algorithms; Breast Cancer; Cancer; Cyborgs; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Women's Health EN Algorithms Breast Cancer Cancer Cyborgs Diagnostics and Screening Emerging Technologies Health and Medicine Machine Learning Oncology Women's Health 471 471 1 03/23/23 20230324 NES 230324 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- Current study results on breast cancer have been published. [Extracted from the article]

Nutritional and Metabolic Diseases and Conditions - Type 2 Diabetes Investigators publish new report on type 2 diabetes. In this paper,gradient boosting classifier, AdaBoost classifier, decision treeclassifier, and extra trees classifier machine learning models have beenutilized for identifying chronic diabetes disease. [Extracted from the article]

A recent study conducted at the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

A researcher from Renmin Hospital of Wuhan University in China has published new data on the use of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of internet hospitals. The study aimed to integrate internet technology into the medical field and improve the accuracy of medical diagnoses. The research found that the remote mobile medical diagnosis system based on machine learning achieved an accuracy of 80% or higher compared to traditional systems. The study suggests that this system has a broad application prospect and can accommodate more than 10,000 people simultaneously. [Extracted from the article]

A recent study conducted by researchers at the First Affiliated Hospital of Wenzhou Medical University in China explores the use of deep learning algorithms in medical image analysis. The study highlights the importance of employing advanced deep learning techniques in this field, as they have shown impressive results in various areas of healthcare. The researchers categorized and assessed different deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), based on their principles, advantages, limitations, methodologies, and datasets. The study also addresses the challenges that hinder the widespread implementation of deep learning in medical image analysis. The findings of this research provide valuable insights for future studies and advancements in image analysis in medical healthcare research. [Extracted from the article]

Keywords: Algorithms; Cyborgs; Emerging Technologies; Epidemiology; Health and Medicine; Hemic and Immune Systems; Immunology; Lymph Nodes; Lymphoid Tissue; Machine Learning; Neoplasms; Risk and Prevention EN Algorithms Cyborgs Emerging Technologies Epidemiology Health and Medicine Hemic and Immune Systems Immunology Lymph Nodes Lymphoid Tissue Machine Learning Neoplasms Risk and Prevention 4761 4761 1 11/06/23 20231110 NES 231110 2023 NOV 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- A new study on neoplasms is now available. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform.". [Extracted from the article]

This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. Keywords: Algorithms; Cyborgs; Diabetic Angiopathies; Diabetic Retinopathy; Emerging Technologies; Eye Diseases and Conditions; Health and Medicine; Machine Learning; Networks; Neural Networks; Nutritional and Metabolic Diseases and Conditions; Ophthalmology; Retinal Diseases and Conditions; Retinopathy; Vascular Diseases and Conditions EN Algorithms Cyborgs Diabetic Angiopathies Diabetic Retinopathy Emerging Technologies Eye Diseases and Conditions Health and Medicine Machine Learning Networks Neural Networks Nutritional and Metabolic Diseases and Conditions Ophthalmology Retinal Diseases and Conditions Retinopathy Vascular Diseases and Conditions 107 107 1 10/30/23 20231030 NES 231030 2023 OCT 30 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- New study results on diabetic retinopathy have been published. [Extracted from the article]

Researchers at Jiangnan University in Wuxi, China have developed a heart failure prediction algorithm based on a reinforcement learning framework. The algorithm combines reinforcement learning and swarm intelligence optimization algorithms to improve the global optimization capability. The researchers found that the improved algorithm framework was significantly better than previous frameworks and accurately predicted heart failure. This research has important implications for reducing the occurrence of heart failure and improving patient outcomes. [Extracted from the article]

The article explores the use of active learning techniques combined with machine learning algorithms to streamline systematic reviews in depression research. Topics discussed include the performance and stability of these algorithms, the development and evaluation of a convolutional neural network, and the efficacy of switching classification models compared to traditional methods.

This article discusses the use of survey questionnaires in psychology and social sciences to measure latent traits of study subjects. Traditional causal inference methods assume knowledge of the true causal structure, which is often unknown in these fields. The article proposes the use of a Bayesian network that can learn the causal structure objectively from data, without prior knowledge. The authors develop a learning algorithm that is able to identify a unique causal structure and quantify associated uncertainty. The algorithm is demonstrated using a dataset from a psychological study on the relationship between symptoms of obsessive-compulsive disorder and depression. The article also mentions the availability of a user-friendly open-source R package for implementing the proposed algorithm. [Extracted from the article]

A research report from Soochow University in Suzhou, China, introduces a new algorithm called Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MASDSC) for brain tumor clustering in medical imaging. The algorithm addresses the limitations of traditional unsupervised deep subspace clustering algorithms by incorporating a semi-supervised learning framework and a multi-scale feature extraction mechanism. It also employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters. The MAS-DSC algorithm achieves excellent clustering results on standard datasets and demonstrates robustness and enhanced clustering performance on a brain tumor dataset. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

This article discusses the use of machine learning algorithms to predict the Big Five personality traits of synthetic participants based on their recent music listening activity. The study utilizes AI-generated participants and analyzes their motivations for listening to music using the Spotify API. The findings suggest that AI struggles to accurately mimic the complexity of human personality psychology and music taste. This study serves as a pilot for a larger investigation comparing synthetic participants to real college students. [Extracted from the article]

This article discusses the use of survey questionnaires in psychology and social science research to measure latent traits in study subjects. While structural equation models have traditionally been used for causal analysis, their reliance on knowledge of true causal structure limits their reliability. The article proposes the use of a Bayesian network, which can learn causal structure objectively from data without prior knowledge. The authors develop a learning algorithm based on hill-climbing and bootstrapping that can identify a unique causal structure and quantify associated uncertainty. The algorithm is demonstrated using a dataset from a psychological study on the relationship between symptoms of obsessive-compulsive disorder and depression. The article also mentions the availability of a user-friendly open-source R package for implementing the proposed algorithm. [Extracted from the article]

A study conducted by researchers at Jamia Millia Islamia in New Delhi, India, explores the use of hybrid machine learning algorithms to improve the accuracy of classifying malaria-infected red blood cells (RBCs). The study introduces a novel hybrid algorithm that combines supervised algorithms, such as stochastic gradient descent, logistic regression, decision tree, and Xgboost, to achieve higher accuracy rates in classifying malaria data. The proposed hybrid algorithm achieved an accuracy rate of 95.64% on the dataset, surpassing the individual algorithms. The research suggests that this approach can aid medical practitioners in diagnosing malaria more accurately. [Extracted from the article]

This article discusses the use of active learning-aided systematic reviewing to reduce the screening load in systematic reviews and meta-analyses. The study evaluates the performance of machine learning algorithms in this process and explores different ways to apply them. The article presents four studies that assess the performance and stability of the active learning pipeline, develop a convolutional neural network to improve existing algorithms, test and compare different algorithm combinations, and build algorithm-switching models for increased performance. The study suggests that switching models can outperform currently used models. However, it is important to note that this preprint has not been peer-reviewed. [Extracted from the article]

A study conducted by researchers at Beijing Jiaotong University in China has developed a new algorithm called EnergySaving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) to optimize the energy consumption of urban rail transit systems. The algorithm incorporates Lagrange multipliers and maximum policy entropy to minimize train traction energy consumption while balancing ride comfort, punctuality, and safety. Numerical experiments conducted on the Beijing Metro's Yizhuang Line showed a minimum 20% increase in energy efficiency compared to manual driving. The algorithm has the potential to guide energy-efficient train operations at the planning level. [Extracted from the article]

For more information on this research see: Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Keywords: Algorithms; Artificial Intelligence; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Pathology; Prostate Cancer; Prostatic Neoplasms; Technology EN Algorithms Artificial Intelligence Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Pathology Prostate Cancer Prostatic Neoplasms Technology 575 575 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators discuss new findings in artificial intelligence. [Extracted from the article]

Algorithms, Chronic Disease, Cyborgs, Disease Attributes, Emerging Technologies, Health and Medicine, Machine Learning Keywords: Algorithms; Chronic Disease; Cyborgs; Disease Attributes; Emerging Technologies; Health and Medicine; Machine Learning EN Algorithms Chronic Disease Cyborgs Disease Attributes Emerging Technologies Health and Medicine Machine Learning 4807 4807 1 08/21/23 20230825 NES 230825 2023 AUG 25 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- A new study on chronic disease is now available. [Extracted from the article]

The primary intention of this systematic review is to point out and critically evaluate the Machine Learning (ML)-based PubMed, Clinical Trial.gov, and Cochrane libraries, an analysis of 400 different studies (1990 to 24 January 2021) is made in which 25 papers met the inclusion criterion. Keywords: Jeddah; Saudi Arabia; Asia; Algorithms; Cardiovascular Diseases and Conditions; Cyborgs; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Heart Disease; Heart Disorders and Diseases; Machine Learning EN Jeddah Saudi Arabia Asia Algorithms Cardiovascular Diseases and Conditions Cyborgs Diagnostics and Screening Emerging Technologies Health and Medicine Heart Disease Heart Disorders and Diseases Machine Learning 1541 1541 1 08/14/23 20230814 NES 230814 2023 AUG 14 (NewsRx) -- By a News Reporter-Staff News Editor at Heart Disease Weekly -- Current study results on Heart Disorders and Diseases - Heart Disease have been published. [Extracted from the article]

A new advanced artificial intelligence (AI) algorithm developed by researchers at Penn State College of Medicine may improve predictions and therapies for autoimmune diseases. The algorithm analyzes the genetic code underlying these conditions to accurately model gene expression and regulation, as well as identify additional genes of risk. The algorithm outperforms existing methodologies and identified 26% more novel gene and trait associations. The researchers also used the algorithm to identify potential therapeutics for autoimmune diseases, pointing to drug compounds that could reverse gene expression in specific cell types associated with these diseases. [Extracted from the article]

A study conducted by researchers at Jamia Hamdard in New Delhi, India, explores the use of machine learning algorithms for diabetes detection. The study focuses on supervised machine learning techniques and tests the efficiency of various classification algorithms using a publicly available dataset. The Random Forest algorithm was found to outperform the other algorithms, achieving an accuracy of 79.52%. The research concludes that further iterations of the algorithm resulted in correct predictions for a significant number of test data. For more information, readers can refer to the journal article "The Diabacare Cloud: Predicting Diabetes Using Machine Learning" published in Acta Scientiarum. Technology. [Extracted from the article]

A recent report from Shanghai Jiao Tong University in China discusses the use of soft robots in cardiac surgery. Soft robots are known for their ability to interact safely with organs and have lower risks of damage compared to rigid robots. The researchers propose an adaptive iterative learning controller to track the irregular motion of the beating heart during surgery. The algorithm has been proven to improve control precision and stability compared to traditional controllers. The research provides valuable insights into the potential of soft robots in surgical applications. [Extracted from the article]

A recent study conducted by researchers at the University of California San Diego has shown that AI algorithms, combined with a modified pacifier, can determine if newborns are nursing properly. The study measured the strength and regularity of babies' suckling patterns using eight independent parameters. The results provide objective data that can improve standard assessments and potentially prevent surgical interventions. The study also found that the device-generated data could improve the subjective evaluation of infants' suckling ability by clinicians. The researchers hope that their findings will support parents struggling with breastfeeding and improve long-term health outcomes. [Extracted from the article]

A report from Yangtze University in Jingzhou, China discusses the use of deep learning algorithms in food safety and authenticity detection. The researchers highlight the advantages of deep learning algorithms over traditional machine learning algorithms and chemometric algorithms. They specifically focus on convolutional neural networks (CNN), fully convolutional networks (FCN), and generative adversarial networks (GAN). The report also explores the potential for further development in this field, including the use of data augmentation and multimodal neural networks. The research concludes that deep learning algorithms have made significant progress in food safety and authenticity detection, but there is still room for improvement. [Extracted from the article]

A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

This article discusses the use of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) algorithm, in predicting vaccine targets. Traditional methods for identifying suitable antigen targets for vaccines can be time-consuming and lack scalability. The authors present an implementation of the KNN algorithm for vaccine target prediction and demonstrate its effectiveness in accurately identifying vaccine targets. The study highlights the potential of the KNN algorithm in this field and provides insights for future research. Please note that this article has not been peer-reviewed. [Extracted from the article]

A researcher from the University of Science and Technology Beijing has conducted a study on the predictive analysis of brain imaging data using deep learning algorithms. The study aimed to improve the accuracy and efficiency of brain imaging data prediction. The researcher developed a batch normalized convolutional neural network (BN-CNN) based prediction method, which showed promising results in predicting missing structural data in brain imaging. The algorithm was applied to epilepsy and Parkinson's cases, achieving an average prediction accuracy of 93.37%. The study suggests that deep learning algorithms have the potential to greatly benefit clinical diagnosis and treatment in the future. [Extracted from the article]