keyword
https://read.qxmd.com/read/38540086/machine-learning-and-texture-analysis-of-18-f-fdg-pet-ct-images-for-the-prediction-of-distant-metastases-in-non-small-cell-lung-cancer-patients
#21
JOURNAL ARTICLE
Armin Hakkak Moghadam Torbati, Sara Pellegrino, Rosa Fonti, Rocco Morra, Sabino De Placido, Silvana Del Vecchio
The aim of our study was to predict the occurrence of distant metastases in non-small-cell lung cancer (NSCLC) patients using machine learning methods and texture analysis of 18 F-labeled 2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography {[18 F]FDG PET/CT} images. In this retrospective and single-center study, we evaluated 79 patients with advanced NSCLC who had undergone [18 F]FDG PET/CT scan at diagnosis before any therapy. Patients were divided into two independent training ( n = 44) and final testing ( n = 35) cohorts...
February 20, 2024: Biomedicines
https://read.qxmd.com/read/38539773/cnn-ht-a-two-stage-algorithm-selection-framework
#22
JOURNAL ARTICLE
Siyi Xu, Wenwen Liu, Chengpei Wu, Junli Li
The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories...
March 14, 2024: Entropy
https://read.qxmd.com/read/38539752/ensemble-classifier-based-on-interval-modeling-for-microarray-datasets
#23
JOURNAL ARTICLE
Urszula Bentkowska, Wojciech Gałka, Marcin Mrukowicz, Aleksander Wojtowicz
The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier...
March 8, 2024: Entropy
https://read.qxmd.com/read/38539600/the-optimization-of-a-natural-language-processing-approach-for-the-automatic-detection-of-alzheimer-s-disease-using-gpt-embeddings
#24
JOURNAL ARTICLE
Benjamin S Runde, Ajit Alapati, Nicolas G Bazan
The development of noninvasive and cost-effective methods of detecting Alzheimer's disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-evaluating their performance against traditional manual transcription methods...
February 25, 2024: Brain Sciences
https://read.qxmd.com/read/38539533/machine-learning-analysis-of-post-operative-tumour-progression-in-non-functioning-pituitary-neuroendocrine-tumours-a-pilot-study
#25
JOURNAL ARTICLE
Ziad Hussein, Robert W Slack, Stephanie E Baldeweg, Evangelos B Mazomenos, Hani J Marcus
Post-operative tumour progression in patients with non-functioning pituitary neuroendocrine tumours is variable. The aim of this study was to use machine learning (ML) models to improve the prediction of post-operative outcomes in patients with NF PitNET. We studied data from 383 patients who underwent surgery with or without radiotherapy, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree, showed superior performance in predicting tumour progression when compared with parametric statistical modelling using logistic regression, with SVM achieving the highest performance...
March 19, 2024: Cancers
https://read.qxmd.com/read/38539493/radiomics-based-classification-of-tumor-and-healthy-liver-on-computed-tomography-images
#26
JOURNAL ARTICLE
Vincent-Béni Sèna Zossou, Freddy Houéhanou Rodrigue Gnangnon, Olivier Biaou, Florent de Vathaire, Rodrigue S Allodji, Eugène C Ezin
Liver malignancies, particularly hepatocellular carcinoma and metastasis, stand as prominent contributors to cancer mortality. Much of the data from abdominal computed tomography images remain underused by radiologists. This study explores the application of machine learning in differentiating tumor tissue from healthy liver tissue using radiomics features. Preoperative contrast-enhanced images of 94 patients were used. A total of 1686 features classified as first-order, second-order, higher-order, and shape statistics were extracted from the regions of interest of each patient's imaging data...
March 14, 2024: Cancers
https://read.qxmd.com/read/38539201/a-prediction-model-based-on-artificial-intelligence-techniques-for-disintegration-time-and-hardness-of-fast-disintegrating-tablets-in-pre-formulation-tests
#27
JOURNAL ARTICLE
Mehri Momeni, Marziyeh Afkanpour, Saleh Rakhshani, Amin Mehrabian, Hamed Tabesh
BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The choice of excipients in tablet formulations plays a critical role in ensuring product quality, highlighting its importance in tablet creation. The traditional trial-and-error approach to this process is both expensive and time-intensive. To tackle these obstacles, we introduce a fresh approach leveraging machine learning and deep learning methods to automate and enhance pre-formulation drug design...
March 27, 2024: BMC Medical Informatics and Decision Making
https://read.qxmd.com/read/38538920/differentiation-of-closely-related-species-within-acinetobacter-baumannii-calcoaceticus-complex-via-raman-spectroscopy-a-comparative-machine-learning-analysis
#28
JOURNAL ARTICLE
Xue-Song Xiong, Lin-Fei Yao, Yan-Fei Luo, Quan Yuan, Yu-Ting Si, Jie Chen, Xin-Ru Wen, Jia-Wei Tang, Su-Ling Liu, Liang Wang
Bacterial species within the Acinetobacter baumannii-calcoaceticus (Acb) complex are very similar and are difficult to discriminate. Misidentification of these species in human infection may lead to severe consequences in clinical settings. Therefore, it is important to accurately discriminate these pathogens within the Acb complex. Raman spectroscopy is a simple method that has been widely studied for bacterial identification with high similarities. In this study, we combined surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms for identifying species within the Acb complex...
March 28, 2024: World Journal of Microbiology & Biotechnology
https://read.qxmd.com/read/38538269/from-sleep-deprivation-to-severe-covid-19-a-comprehensive-analysis-of-shared-differentially-expressed-genes-and-potential-diagnostic-biomarkers
#29
JOURNAL ARTICLE
Jing Peng, Xiaocheng Zhu, Wuping Zhuang, Hui Luo, E Wang
BACKGROUND: This study aims to identify biomarkers through the analysis of genomic data, with the goal of understanding the potential immune mechanisms underpinning the association between sleep deprivation (SD) and the progression of COVID-19. METHODS: Datasets derived from the Gene Expression Omnibus (GEO) were employed, in conjunction with a differential gene expression analysis, and several machine learning methodologies, including models of Random Forest, Support Vector Machine, and Least Absolute Shrinkage and Selection Operator (LASSO) regression...
March 18, 2024: Frontiers in Bioscience (Landmark Edition)
https://read.qxmd.com/read/38538213/-cd6-and-ccr7-as-genetic-biomarkers-in-evaluating-intracranial-aneurysm-rupture-risk
#30
JOURNAL ARTICLE
Dan-Dan Xu, Xiao-Qiang Liu, Zhi-Sheng Wu
BACKGROUND: This study used bioinformatics combined with statistical methods to identify plasma biomarkers that can predict intracranial aneurysm (IA) rupture and provide a strong theoretical basis for the search for new IA rupture prevention methods. METHODS: We downloaded gene expression profiles in the GSE36791 and GSE122897 datasets from the Gene Expression Omnibus (GEO) database. Data were normalized using the "sva" R package and differentially expressed genes (DEGs) were identified using the "limma" R package...
March 11, 2024: Journal of Integrative Neuroscience
https://read.qxmd.com/read/38538062/machine-learning-approaches-to-identify-affected-brain-regions-in-movement-disorders-using-mri-data-a-systematic-review-and-diagnostic-meta-analysis
#31
JOURNAL ARTICLE
Sadegh Ghaderi, Mahdi Mohammadi, Fatemeh Sayehmiri, Sana Mohammadi, Arian Tavasol, Masoud Rezaei, Azadeh Ghalyanchi-Langeroudi
BACKGROUND: Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. PURPOSE/HYPOTHESIS: We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data...
March 27, 2024: Journal of Magnetic Resonance Imaging: JMRI
https://read.qxmd.com/read/38537959/machine-learning-algorithm-based-risk-prediction-and-screening-detected-prostate-cancer-in-a-benign-prostate-hyperplasia-cohort
#32
JOURNAL ARTICLE
Chia-Cheng Chang, Jiun-Kai Chiou, Cheng-Jian Lin, Kevin Lu, Jian-Ri Li, Li-Wen Chang, Sheng-Chun Hung, Chen-Li Cheng
BACKGROUND/AIM: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance. PATIENTS AND METHODS: The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled...
April 2024: Anticancer Research
https://read.qxmd.com/read/38537278/bandgap-prediction-of-non-metallic-crystals-through-machine-learning-approach
#33
JOURNAL ARTICLE
Sadhana Barman, Harkishan Dua, Utpal Sarkar
The determination of bandgap is the heart of electronic structure of any material and is a crucial factor for thermoelectric performance of it. Due to large amount to data (features) that are related to bandgap are now a days available, it is possible to make use of machine learning approach to predict the bandgap of the material. The study commences by selecting the feature through Pearson correlation study between bandgap and various thermoelectric parameters in non-metallic crystals. Among the forty two parameters available in the dataset, the seebeck coefficient and its corresponding temperatures show high correlation with the bandgap...
March 27, 2024: Journal of Physics. Condensed Matter: An Institute of Physics Journal
https://read.qxmd.com/read/38537183/a-radiomics-model-based-on-transrectal-ultrasound-for-predicting-prostate-cancer
#34
JOURNAL ARTICLE
Yanhua Huang, Hongwei Qian, Yuanyuan Zheng, Huiming Song, Xiatian Liu
AIM: Prostate cancer (PCa) is one of the most common neoplasms in men. However, the value of ultrasound-based radiomics for diagnosing PCa remains uncertain. MATERIAL AND METHODS: We retrospectively analyzed ultrasonic and clinical data from 373 patients. Patients were divided into two groups according to the pathological results. Radiomics features wereextracted from TRUS, and we screened the optimal features to construct radiomics models. Relationships between clinical characteristics and prostate lesions were identified by univariate and multivariate logistic regression analysis...
February 7, 2024: Medical Ultrasonography
https://read.qxmd.com/read/38536782/a-machine-learning-based-depression-screening-framework-using-temporal-domain-features-of-the-electroencephalography-signals
#35
JOURNAL ARTICLE
Sheharyar Khan, Sanay Muhammad Umar Saeed, Jaroslav Frnda, Aamir Arsalan, Rashid Amin, Rahma Gantassi, Sadam Hussain Noorani
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA)...
2024: PloS One
https://read.qxmd.com/read/38536559/radiomics-based-machine-learning-in-the-differentiation-of-benign-and-malignant-bowel-wall-thickening-radiomics-in-bowel-wall-thickening
#36
JOURNAL ARTICLE
Hande Melike Bülbül, Gülen Burakgazi, Uğur Kesimal, Esat Kaba
PURPOSE: To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model. METHODS: One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO)...
March 27, 2024: Japanese Journal of Radiology
https://read.qxmd.com/read/38535315/machine-learning-to-predict-enzyme-substrate-interactions-in-elucidation-of-synthesis-pathways-a-review
#37
REVIEW
Luis F Salas-Nuñez, Alvaro Barrera-Ocampo, Paola A Caicedo, Natalie Cortes, Edison H Osorio, Maria F Villegas-Torres, Andres F González Barrios
Enzyme-substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space...
March 7, 2024: Metabolites
https://read.qxmd.com/read/38535046/machine-learning-based-algorithms-for-enhanced-prediction-of-local-recurrence-and-metastasis-in-low-rectal-adenocarcinoma-using-imaging-surgical-and-pathological-data
#38
JOURNAL ARTICLE
Cristian-Constantin Volovat, Dragos-Viorel Scripcariu, Diana Boboc, Simona-Ruxandra Volovat, Ingrid-Andrada Vasilache, Corina Ursulescu-Lupascu, Liliana Gheorghe, Luiza-Maria Baean, Constantin Volovat, Viorel Scripcariu
(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023...
March 15, 2024: Diagnostics
https://read.qxmd.com/read/38534863/metaheuristic-based-feature-selection-methods-for-diagnosing-sarcopenia-with-machine-learning-algorithms
#39
JOURNAL ARTICLE
Jaehyeong Lee, Yourim Yoon, Jiyoun Kim, Yong-Hyuk Kim
This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms...
March 15, 2024: Biomimetics
https://read.qxmd.com/read/38534549/gait-recognition-and-assistance-parameter-prediction-determination-based-on-kinematic-information-measured-by-inertial-measurement-units
#40
JOURNAL ARTICLE
Qian Xiang, Jiaxin Wang, Yong Liu, Shijie Guo, Lei Liu
The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks...
March 13, 2024: Bioengineering
keyword
keyword
6123
2
3
Fetch more papers »
Fetching more papers... Fetching...
Remove bar
Read by QxMD icon Read
×

Save your favorite articles in one place with a free QxMD account.

×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"

We want to hear from doctors like you!

Take a second to answer a survey question.