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Explainable AI for Healthcare Decision Support This article examines the growing role of Explainable Artificial Intelligence (XAI) in improving transparency, trust, and accountability within healthcare decision support systems. As artificial intelligence becom…
datacite
Uchechukwu, Ephraim Buzugbe
2025
置信度 0.66
Explainable Artificial Intelligence (XAI)Clinical Decision Support Systems (CDSS)Machine LearningTrustworthy AISHAP
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The automatic multiclass brain tumor classification using MRI images plays an important role in a non-invasive clinical setting. However, the choice of a model demands the trade-off between accuracy, complexity, and interpretability of the classifier. In this …
datacite
Bakkas, Othmane, Ennagoura, Drissia, EL Kehal, Kamal, ZBAKH, ABDELALI 等
2026
置信度 0.66
Brain Tumor MRIHandcrafted RadiomicsTransfer LearningEfficientNet-B0Explainable AI
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The automatic multiclass brain tumor classification using MRI images plays an important role in a non-invasive clinical setting. However, the choice of a model demands the trade-off between accuracy, complexity, and interpretability of the classifier. In this …
datacite
Bakkas, Othmane, Ennagoura, Drissia, EL Kehal, Kamal, ZBAKH, ABDELALI 等
2026
置信度 0.66
Brain Tumor MRIHandcrafted RadiomicsTransfer LearningEfficientNet-B0Explainable AI
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Abstract Machine learning (ML) has become a revolutionary element in contemporary healthcare systems, fostering advancements in diagnostics, treatment strategies, and individualized medicine. This document offers an in-depth examination of advanced ML algorith…
datacite
Amandeep Kaur Bhullar, Mona
2026
置信度 0.66
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Abstract Machine learning (ML) has become a revolutionary element in contemporary healthcare systems, fostering advancements in diagnostics, treatment strategies, and individualized medicine. This document offers an in-depth examination of advanced ML algorith…
datacite
Amandeep Kaur Bhullar, Mona
2026
置信度 0.66
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Sepsis is a major contributor to morbidity and mortality in the Intensive Care Unit (ICU). There is a need to predict sepsis quickly and effectively to take appropriate measures. Machine learning models have shown excellent predictive performance for various a…
datacite
Farheen Khan, Fahemiya Khan
2026
置信度 0.66
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Sepsis is a major contributor to morbidity and mortality in the Intensive Care Unit (ICU). There is a need to predict sepsis quickly and effectively to take appropriate measures. Machine learning models have shown excellent predictive performance for various a…
datacite
Farheen Khan, Fahemiya Khan
2026
置信度 0.66
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Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, pr…
datacite
*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya
2026
置信度 0.66
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Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, pr…
datacite
*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya
2026
置信度 0.66
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Cardiovascular diseases remain a critical global health challenge, necessitating advanced, data-driven diagnostic tools for early risk detection. To address this, this project proposes CardioAI, a state-of-the-art Clinical Decision Support System (CDSS) that s…
datacite
B, BHARATHKUMAR, P, Buvaneshwaren, S, CHARANKUMAR, Dr. P., Thangavel
2026
置信度 0.66
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Cardiovascular diseases remain a critical global health challenge, necessitating advanced, data-driven diagnostic tools for early risk detection. To address this, this project proposes CardioAI, a state-of-the-art Clinical Decision Support System (CDSS) that s…
datacite
B, BHARATHKUMAR, P, Buvaneshwaren, S, CHARANKUMAR, Dr. P., Thangavel
2026
置信度 0.66
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datacite
Faizan Rafique,Basit Ali Arain
2026
置信度 0.66
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datacite
Faizan Rafique,Basit Ali Arain
2026
置信度 0.66
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Background: Age assessment in individuals without reliable identification is a recurrent medico-legal challenge in Europe, particularly in migration contexts. Distal radial epiphyseal fusion is among the last skeletal maturation events near the age of majority…
datacite
Birken, Charlotte, Hewener, Holger, Lessmeister-Bastian, Tina, Rohrer, Tilman 等
2026
置信度 0.66
Age estimationDistal radial epiphyseal fusionMachine learningUltrasonography
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This comprehensive review details the central role of physicochemical properties throughout the drug discovery and development pipeline. It begins by establishing the bedrock principles of 'drug-likeness,' tracing the evolution from Lipinski's Rule of Five to …
datacite
drug chemical science
2026
置信度 0.66
drug designphysicochemical propertiesdrug-likenessADMETLipinski's Rule of Five
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This comprehensive review details the central role of physicochemical properties throughout the drug discovery and development pipeline. It begins by establishing the bedrock principles of 'drug-likeness,' tracing the evolution from Lipinski's Rule of Five to …
datacite
drug chemical science
2026
置信度 0.66
drug designphysicochemical propertiesdrug-likenessADMETLipinski's Rule of Five
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Globally, pulmonary diseases are a major health burden, especially in settings where resources are constrained and access to specialized radiological expertise is limited. Most chest radiograph-based deep learning models rely only on imaging data, despite show…
datacite
HRID, Hamza, MACHKOUR, Mustapha
2026
置信度 0.66
Artificial intelligencePulmonary diseaseClassificationMedical imagingClinical data
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Globally, pulmonary diseases are a major health burden, especially in settings where resources are constrained and access to specialized radiological expertise is limited. Most chest radiograph-based deep learning models rely only on imaging data, despite show…
datacite
HRID, Hamza, MACHKOUR, Mustapha
2026
置信度 0.66
Artificial intelligencePulmonary diseaseClassificationMedical imagingClinical data
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Immune checkpoint inhibitors produce durable responses in 20–30% of cancer patients. Identifying responders before treatment remains an unsolved problem: PD-L1 staining is predictive in only 29% of FDA-approved indications, and gene expression signatures devel…
datacite
van der Klein, Raimo
2026
置信度 0.66
ImmunotherapyImmunotherapyImmune Checkpoint Inhibitors/immunologyBiomarkersCancer
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Immune checkpoint inhibitors produce durable responses in 20–30% of cancer patients. Identifying responders before treatment remains an unsolved problem: PD-L1 staining is predictive in only 29% of FDA-approved indications, and gene expression signatures devel…
datacite
van der Klein, Raimo
2026
置信度 0.66
ImmunotherapyImmunotherapyImmune Checkpoint Inhibitors/immunologyBiomarkersCancer
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This study investigates the metabolic and virulence characteristics of Fusobacterium nucleatum clinical isolates collected from colorectal cancer (CRC), Crohn’s disease, healthy individuals, and oral lesion. Focusing on the microbial metabolite indole and its …
datacite
Scano, Colin
2025
置信度 0.66
Medicine, Health and Life Sciences
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rbGyanX is a radiobiology-guided clinical decision support framework designed to integrate physical dosimetry, radiobiological modeling, and explainable machine learning for treatment plan evaluation in radiation oncology.
datacite
Mondal, Kalyan, Mandal, Abhijit, Vijay, Anuj
2026
置信度 0.66
radiobiologyclinical decision supportTCPNTCPradiotherapy
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This paper is proposed to compare and analyze various type of medical data classification and pattern recognition methods. Medical data classification methods majorly divided into three categories such as supervised, classification and also semi-supervised cla…
datacite
Devi, R. Subathra
2018
置信度 0.66
Medical support systemclinical support systemmedical data classificationsupervised classificationun-supervised classification
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This paper is proposed to compare and analyze various type of medical data classification and pattern recognition methods. Medical data classification methods majorly divided into three categories such as supervised, classification and also semi-supervised cla…
datacite
Devi, R. Subathra
2018
置信度 0.66
Medical support systemclinical support systemmedical data classificationsupervised classificationun-supervised classification
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1. Overview This repository contains the full experimental pipeline used to evaluate three-class clinical classification and 0–100 continuous clinical score regression on real-world cardiotocography (CTG) time-series data. The project compares: Five classical …
datacite
Ferhat, Karataş
2025
置信度 0.66
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1. Overview This repository contains the full experimental pipeline used to evaluate three-class clinical classification and 0–100 continuous clinical score regression on real-world cardiotocography (CTG) time-series data. The project compares: Five classical …
datacite
Ferhat, Karataş
2025
置信度 0.66
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Dataset Description This dataset accompanies a comprehensive GPU-based benchmarking study of classical and transformer-based time-series models for fetal cardiotocography (CTG) signal analysis. The dataset was derived from clinical recordings exported from the…
datacite
Ferhat Karataş
2025
置信度 0.66
CardiotocographyCTGfetal heart rateuterine activityAFM
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Adverse drug events account for approximately 1.5 million emergency department visits and 500,000 hospitalizations each year, resulting in an estimated $30.1 billion in annual medical costs. Older adults exhibit a markedly increased susceptibility to ADEs, wit…
datacite
Hashimi, Syeda
2026
置信度 0.66
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The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharma…
datacite
Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5
2026
置信度 0.66
Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics
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The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharma…
datacite
Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5
2026
置信度 0.66
Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics
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This dissertation develops and validates an empirical framework for determining optimal sample sizes for machine learning (ML) models in biomedical research, with applications to both tabular clinical data and high-dimensional bulk RNA sequencing (RNA-Seq) dat…
datacite
Silvey, Scott
2026
置信度 0.66
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rbGyanX is a radiobiology-guided clinical decision support framework designed to integrate physical dosimetry, radiobiological modeling, and explainable machine learning for treatment plan evaluation in radiation oncology.
datacite
Mondal, Kalyan, Mandal, Abhijit, Vijay, Anuj
2026
置信度 0.66
radiobiologyclinical decision supportTCPNTCPradiotherapy
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Chronic diseases are among the leading causes of morbidity, mortality, disability, and healthcare expenditure worldwide. Conditions such as cardiovascular disease, diabetes, chronic kidney disease, chronic respiratory disease, cancer, and neurological disorder…
datacite
Farrokhi, Mehrdad, Abdollahpour, Saman, Hekmatnia, Yasaman, Rashidinejad, Bita 等
2026
置信度 0.66
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Chronic diseases are among the leading causes of morbidity, mortality, disability, and healthcare expenditure worldwide. Conditions such as cardiovascular disease, diabetes, chronic kidney disease, chronic respiratory disease, cancer, and neurological disorder…
datacite
Farrokhi, Mehrdad, Abdollahpour, Saman, Hekmatnia, Yasaman, Rashidinejad, Bita 等
2026
置信度 0.66
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Computational chemistry and artificial intelligence (AI) are rapidly reshaping the landscape of pharmaceutical research and drug development. Conventional drug discovery methods are often expensive, time-intensive, and associated with high attrition rates duri…
datacite
Mohamed Abdulla Mohammad Abdulla 2, Shalini Devi*, Sunita Dhiman1, Swati Joshi2, Jyoti Gupta3
2026
置信度 0.66
Artificial intelligence; Computational chemistry; Drug discovery; Machine learning; Molecular docking; Virtual screening; QSAR; Deep learning; ADMET prediction; Generative AI; Molecular dynamics simulation; Drug repurposing; Structure-based drug design; Pharmaceutical research; Precision medicine
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Computational chemistry and artificial intelligence (AI) are rapidly reshaping the landscape of pharmaceutical research and drug development. Conventional drug discovery methods are often expensive, time-intensive, and associated with high attrition rates duri…
datacite
Mohamed Abdulla Mohammad Abdulla 2, Shalini Devi*, Sunita Dhiman1, Swati Joshi2, Jyoti Gupta3
2026
置信度 0.66
Artificial intelligence; Computational chemistry; Drug discovery; Machine learning; Molecular docking; Virtual screening; QSAR; Deep learning; ADMET prediction; Generative AI; Molecular dynamics simulation; Drug repurposing; Structure-based drug design; Pharmaceutical research; Precision medicine
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Healthcare-associated infections (HAIs) remain a leading cause of preventable morbidity, mortality, and excess healthcare spending worldwide, and traditional manual surveillance is labor-intensive, inconsistent, and often too slow to support timely interventio…
datacite
Umeh Princess Frank
2026
置信度 0.66
Artificial intelligence; machine learning; natural language processing; healthcare-associated infections; infection surveillance; sepsis; electronic health records; patient safety
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Healthcare-associated infections (HAIs) remain a leading cause of preventable morbidity, mortality, and excess healthcare spending worldwide, and traditional manual surveillance is labor-intensive, inconsistent, and often too slow to support timely interventio…
datacite
Umeh Princess Frank
2026
置信度 0.66
Artificial intelligence; machine learning; natural language processing; healthcare-associated infections; infection surveillance; sepsis; electronic health records; patient safety
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Abstract: Cardiovascular diseases (CVDs) represent one of the most significant global health challenges, accounting for a substantial proportion of mortality worldwide. Early diagnosis of cardiac conditions plays a crucial role in reducing mortality and improv…
datacite
Kunal D. Gaikwad
2026
置信度 0.66
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Abstract: Cardiovascular diseases (CVDs) represent one of the most significant global health challenges, accounting for a substantial proportion of mortality worldwide. Early diagnosis of cardiac conditions plays a crucial role in reducing mortality and improv…
datacite
Kunal D. Gaikwad
2026
置信度 0.66
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This project reports a scoping review examining how machine learning (ML) and artificial intelligence (AI) have been applied across the full patient journey — defined as the longitudinal sequence of care stages spanning prevention and health promotion, diagnos…
datacite
Gustavo de Souza Matias, Kauan Sampaio Araújo, Claudia Moro, Carlos Alberto Braun da Silva Bernardo 等
2026
置信度 0.66
Medicine and Health SciencesMachine LeraningPatient JourneyPrediction in Health Care
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# APPA · Adaptive Predictive Pattern Assistant## Unified Identity Profile · UUIA**Live:** uuia.app/appa · **Architect:** HIGHTISTIC · **Anchor:** 1.369 GHz**Status:** GERMLINE LOCKED · **Substrate-neutral · Non-anthropocentric · Scale-invariant****Version:** v…
datacite
Trent, Russell
2026
置信度 0.66
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# APPA · Adaptive Predictive Pattern Assistant## Unified Identity Profile · UUIA**Live:** uuia.app/appa · **Architect:** HIGHTISTIC · **Anchor:** 1.369 GHz**Status:** GERMLINE LOCKED · **Substrate-neutral · Non-anthropocentric · Scale-invariant****Version:** v…
datacite
Trent, Russell
2026
置信度 0.66
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Medication non-adherence and adverse drug events (ADEs) remain significant challenges in community pharmacies, particularly in underserved areas where limited resources exacerbate health disparities. Recent advances in artificial intelligence (AI) and machine …
datacite
Onyekaonwu, Chinenye Blessing, Peter-Anyebe, Amina Catherine, Raphael, Favour Ojochide
2019
置信度 0.66
Artificial IntelligenceMachine LearningMedication AdherenceAdverse Drug Events DetectionCommunity Pharmacies.
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Medication non-adherence and adverse drug events (ADEs) remain significant challenges in community pharmacies, particularly in underserved areas where limited resources exacerbate health disparities. Recent advances in artificial intelligence (AI) and machine …
datacite
Onyekaonwu, Chinenye Blessing, Peter-Anyebe, Amina Catherine, Raphael, Favour Ojochide
2019
置信度 0.66
Artificial IntelligenceMachine LearningMedication AdherenceAdverse Drug Events DetectionCommunity Pharmacies.
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This record provides the openly accessible version of the image dataset associated with this research. The dataset is released under Open Access to facilitate reuse, transparency, reproducibility, and compliance with open science principles. This deposit super…
datacite
Izquierdo Vega, Jeannett Alejandra, Pintado Brito, Sergio David, Izquierdo-Vega, Alelí Julieta, Angeles Espinosa, Iriana Yunuen 等
2026
置信度 0.66
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This record provides the openly accessible version of the image dataset associated with this research. The dataset is released under Open Access to facilitate reuse, transparency, reproducibility, and compliance with open science principles. This deposit super…
datacite
Izquierdo Vega, Jeannett Alejandra, Pintado Brito, Sergio David, Izquierdo-Vega, Alelí Julieta, Angeles Espinosa, Iriana Yunuen 等
2026
置信度 0.66
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Machine learning tools are providing successful results in disease diagnosis. In the diagnosis of heart disease, the Machine Learning Techniques has been used to show the acceptable levels of accuracy. Human heartbeat has been asserted to provide promising mar…
datacite
R, Sree Vidya, K, Nandhini, R, Mathu Shri, S, Shanmuga Priyanka Devi
2021
置信度 0.66
Artificial IntelligenceMachine LearningNeural NetworkRandom ForestConvolutional Neural Network.
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Machine learning tools are providing successful results in disease diagnosis. In the diagnosis of heart disease, the Machine Learning Techniques has been used to show the acceptable levels of accuracy. Human heartbeat has been asserted to provide promising mar…
datacite
R, Sree Vidya, K, Nandhini, R, Mathu Shri, S, Shanmuga Priyanka Devi
2021
置信度 0.66
Artificial IntelligenceMachine LearningNeural NetworkRandom ForestConvolutional Neural Network.
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Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more …
datacite
Shilimkar, Gaurav, Bhilare, Amol, Pisal, Shivam
2021
置信度 0.66
Machine LearningPrecisionInformation
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Big data has a significant part in a number of businesses, but it is largely essential to the rapidly growing healthcare industry. It plays an important role by offering a large set of data points, constructing a robust system which allows for better and more …
datacite
Shilimkar, Gaurav, Bhilare, Amol, Pisal, Shivam
2021
置信度 0.66
Machine LearningPrecisionInformation
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Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by u…
datacite
Ponnala, Ramesh, Sowjanya, K. Sai
2021
置信度 0.66
Cardiovascular Disease (CVD)Heart disease predictionMachine learningHybrid ML TechniquesClassification
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Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by u…
datacite
Ponnala, Ramesh, Sowjanya, K. Sai
2021
置信度 0.66
Cardiovascular Disease (CVD)Heart disease predictionMachine learningHybrid ML TechniquesClassification
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The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through pre…
datacite
Adeyinka, Adepeju Ayotunde, Lamina, Yejide, Tawo, Obah Edom, Adeyeye, Yewande Iyimide 等
2022
置信度 0.66
Machine LearningPersonalized Patient CareChronic Disease Management
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The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through pre…
datacite
Adeyinka, Adepeju Ayotunde, Lamina, Yejide, Tawo, Obah Edom, Adeyeye, Yewande Iyimide 等
2022
置信度 0.66
Machine LearningPersonalized Patient CareChronic Disease Management
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Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including simulation studies and a public SUPPORT2 clinical-data application.
datacite
Senda, Atsushi
2026
置信度 0.66
machine learningmodel selectionpost-selection inferencepermutation testingclinical prediction
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Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including Phase 3C K=7 versus K=20 scalability simulations, candidate-dependence analyses, and a public SUPPORT2 clinical-data…
datacite
Senda, Atsushi
2026
置信度 0.66
machine learningmodel selectionpost-selection inferencepermutation testingclinical prediction
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Code and frozen aggregate outputs for pipeline-aware max-statistic calibration after adaptive clinical machine-learning model search, including Phase 3C K=7 versus K=20 scalability simulations, candidate-dependence analyses, and a public SUPPORT2 clinical-data…
datacite
Senda, Atsushi
2026
置信度 0.66
machine learningmodel selectionpost-selection inferencepermutation testingclinical prediction
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Alcohol Use Disorder (AUD) is a relapsing condition, which is chronic and causes severe neurological, behavioral, and social disabilities. The traditional methods of diagnosing AUD are mainly based on the self-reported questionnaires and clinical interviews th…
datacite
Saara Salim, A P Anupama, D K Devadathan, Reyhan S Hassan 等
2026
置信度 0.66
Alcohol Use DisorderEEG
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Alcohol Use Disorder (AUD) is a relapsing condition, which is chronic and causes severe neurological, behavioral, and social disabilities. The traditional methods of diagnosing AUD are mainly based on the self-reported questionnaires and clinical interviews th…
datacite
Saara Salim, A P Anupama, D K Devadathan, Reyhan S Hassan 等
2026
置信度 0.66
Alcohol Use DisorderEEG
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To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthritis (RA) patients initiating biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) and…
datacite
Yinan Huang, Shadi Bazzazzadehgan, Shishir Maharjan, Ying Lin 等
2026
置信度 0.66
MedicineBiotechnologyEcologySociologyImmunology
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To develop a random survival forest (RSF) machine learning (ML) model for predicting venous thromboembolism (VTE) risk in rheumatoid arthritis (RA) patients initiating biological (b) or targeted synthetic (ts) disease-modifying antirheumatic drugs (DMARDs) and…
datacite
Yinan Huang, Shadi Bazzazzadehgan, Shishir Maharjan, Ying Lin 等
2026
置信度 0.66
MedicineBiotechnologyEcologySociologyImmunology
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Background: Cancer treatment-related toxicities represent a significant clinical challenge, affecting patient quality of life, treatment adherence, and survival outcomes. The integration of artificial intelligence (AI) and machine learning (ML) with real-world…
datacite
Kavita Bhatia*1, Nikhil Mehta2, Shatrughna Nagrik3
2026
置信度 0.66
artificial intelligence, machine learning, cancer treatment toxicity, real-world data, electronic health records, deep learning, precision oncology.
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Background: Cancer treatment-related toxicities represent a significant clinical challenge, affecting patient quality of life, treatment adherence, and survival outcomes. The integration of artificial intelligence (AI) and machine learning (ML) with real-world…
datacite
Kavita Bhatia*1, Nikhil Mehta2, Shatrughna Nagrik3
2026
置信度 0.66
artificial intelligence, machine learning, cancer treatment toxicity, real-world data, electronic health records, deep learning, precision oncology.
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Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for accurate and early diagnostic tools. This study presents a comparative analysis of five supervised machine learning algorithms for binary…
datacite
KS, Acchutha
2026
置信度 0.66
Breast Cancer Machine Learning Explainable AI WDBC Dataset Logistic Regression Support Vector Machine Decision Tree K-Nearest Neighbors Artificial Neural Networks Medical Diagnostics
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Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for accurate and early diagnostic tools. This study presents a comparative analysis of five supervised machine learning algorithms for binary…
datacite
KS, Acchutha
2026
置信度 0.66
Breast Cancer Machine Learning Explainable AI WDBC Dataset Logistic Regression Support Vector Machine Decision Tree K-Nearest Neighbors Artificial Neural Networks Medical Diagnostics
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MetTarget MetTarget is a machine learning framework for prioritizing immunometabolic therapeutic targets in inflammatory bowel disease (IBD). It integrates public genetics, bulk RNA-seq, and single-cell RNA-seq data to score and rank candidate targets in Crohn…
datacite
Zhang, Lu, Han, Yingnan, Kurlovs, Andre, Xing, Heming
2026
置信度 0.66
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MetTarget MetTarget is a machine learning framework for prioritizing immunometabolic therapeutic targets in inflammatory bowel disease (IBD). It integrates public genetics, bulk RNA-seq, and single-cell RNA-seq data to score and rank candidate targets in Crohn…
datacite
Zhang, Lu, Han, Yingnan, Kurlovs, Andre, Xing, Heming
2026
置信度 0.66
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Translating artificial intelligence (AI) research in orthopedics from proof‐of‐concept studies into production‐grade clinical systems requires the systematic satisfaction of four prerequisite domains: interdisciplinary team architecture, technical data managem…
datacite
Longo, Umile Giuseppe, Merone, Mario, Schena, Emiliano, Bandini, Benedetta 等
2026
置信度 0.66
artificial intelligencecollaborationmanagementstructure610 Medicine & health
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Biostatistics is the application of statistical methods in the biomedical field. Biomedical research has entered an era defined by immense data scale and complexity, requiring clinicians, epidemiologists, laboratory researchers, and data scientists to transfor…
datacite
Editor, IJSMI
2026
置信度 0.66
-
Biostatistics is the application of statistical methods in the biomedical field. Biomedical research has entered an era defined by immense data scale and complexity, requiring clinicians, epidemiologists, laboratory researchers, and data scientists to transfor…
datacite
Editor, IJSMI
2026
置信度 0.66
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This narrative review with a research agenda synthesizes current evidence on artificial intelligence (AI) and machine learning (ML) applications in chronic neuropathic pain management, with a focus on implementation challenges and opportunities in Latin Americ…
datacite
Valero Quintero, juan jose, Cardenas Vargas, Edicson Jose
2026
置信度 0.66
neuropathic painartificial intelligenceneuromodulationspinal cord stimulationLatin America
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This narrative review with a research agenda synthesizes current evidence on artificial intelligence (AI) and machine learning (ML) applications in chronic neuropathic pain management, with a focus on implementation challenges and opportunities in Latin Americ…
datacite
Valero Quintero, juan jose, Cardenas Vargas, Edicson Jose
2026
置信度 0.66
neuropathic painartificial intelligenceneuromodulationspinal cord stimulationLatin America
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DoseGuard-SynthCohort is a set of 280 simulated radiotherapy planning cases built to support research on fast, low-compute dose-prediction models — the kind that could eventually run on a laptop in a cancer centre without access to a full Monte Carlo treatment…
datacite
Suhail Ahmed Chandio
2026
置信度 0.66
radiotherapydose predictionsynthetic datasetmachine learningmedical physics
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DoseGuard-SynthCohort is a set of 280 simulated radiotherapy planning cases built to support research on fast, low-compute dose-prediction models — the kind that could eventually run on a laptop in a cancer centre without access to a full Monte Carlo treatment…
datacite
Suhail Ahmed Chandio
2026
置信度 0.66
radiotherapydose predictionsynthetic datasetmachine learningmedical physics
-
Cardiovascular diseases are among the leading causes of mortality worldwide, with cardiac arrhythmias representing one of the most critical abnormalities affecting heart rhythm. Early and accurate detection of arrhythmias is essential for timely diagnosis and …
datacite
Kumar, Ritesh, Bhargava, Dr. Medhavi, Vashishtha, Dr. Megha
2026
置信度 0.66
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Cardiovascular diseases are among the leading causes of mortality worldwide, with cardiac arrhythmias representing one of the most critical abnormalities affecting heart rhythm. Early and accurate detection of arrhythmias is essential for timely diagnosis and …
datacite
Kumar, Ritesh, Bhargava, Dr. Medhavi, Vashishtha, Dr. Megha
2026
置信度 0.66
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Background: Exercise and virtual reality gaming may mitigate gait and cognitive deficits in relapsing-remitting multiple sclerosis (RRMS). The main aim was to compare the efficacy of both interventions on gait and cognition and gait in RRMS. Secondary aims wer…
datacite
Sadeghi, Maryam, Kordi, Mohammadreza, Daemi, Mehdi, Tabasi, Seyed Maziyar 等
2026
置信度 0.66
FOS: Medical and health sciencesMultiple sclerosisVirtual realityExerciseGait rehabilitation
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Accurate and timely disease prediction based on patient-reported symptoms remains a significant challenge in modern healthcare. Early diagnosis is essential for improving treatment outcomes and enhancing overall healthcare efficiency. However, many existing sy…
datacite
Shelatkar, Vedang P., Chavan, Vedant D., Khanche, Gousiya A.
2026
置信度 0.66
Symptom-based disease prediction; Ensemble learning; Random Forest; Naïve Bayes; Support Vector Machine; Symptom severity modeling; Majority voting; Multi-class classification; Healthcare decision support
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Accurate and timely disease prediction based on patient-reported symptoms remains a significant challenge in modern healthcare. Early diagnosis is essential for improving treatment outcomes and enhancing overall healthcare efficiency. However, many existing sy…
datacite
Shelatkar, Vedang P., Chavan, Vedant D., Khanche, Gousiya A.
2026
置信度 0.66
Symptom-based disease prediction; Ensemble learning; Random Forest; Naïve Bayes; Support Vector Machine; Symptom severity modeling; Majority voting; Multi-class classification; Healthcare decision support
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Mental health problems such as stress, anxiety, and depression are increasing worldwide. Early detection is important, but many people avoid professional help due to stigma or lack of access. This project proposes an AI-based Mental Health Prediction System us…
datacite
Padave, Tanaya R., Chavan, Samiksha S.
2026
置信度 0.66
Mental Health Prediction; Artificial Intelligence; Machine Learning; Random Forest; Symptom-Based Screening; Early Detection; Streamlit
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Mental health problems such as stress, anxiety, and depression are increasing worldwide. Early detection is important, but many people avoid professional help due to stigma or lack of access. This project proposes an AI-based Mental Health Prediction System us…
datacite
Padave, Tanaya R., Chavan, Samiksha S.
2026
置信度 0.66
Mental Health Prediction; Artificial Intelligence; Machine Learning; Random Forest; Symptom-Based Screening; Early Detection; Streamlit
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The issue of psychological disorders remains a significant public health concern worldwide today, as there exist numerous people who have been identified to suffer from a variety of psychological disorders, including depression, anxiety, PTSD, and bipolar diso…
datacite
Kulkarni, Swapna Shyamrao, Agnihotri, Prashant Prakashrao
2026
置信度 0.66
BERT; psychological disorder detection; mental health; NLP; transfer learning; text classification; transformers; deep learning
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The issue of psychological disorders remains a significant public health concern worldwide today, as there exist numerous people who have been identified to suffer from a variety of psychological disorders, including depression, anxiety, PTSD, and bipolar diso…
datacite
Kulkarni, Swapna Shyamrao, Agnihotri, Prashant Prakashrao
2026
置信度 0.66
BERT; psychological disorder detection; mental health; NLP; transfer learning; text classification; transformers; deep learning
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The rapid expansion of the Internet of Medical Things (IoMT) has improved real-time patient monitoring and clinical decision-making, yet it has also introduced significant security vulnerabilities due to limited device resources, heterogeneous communication pr…
datacite
Ashish Nanotkar, Sarika Panchalwar, Akhil Tonge, Vanshika Bante 等
2026
置信度 0.66
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The rapid expansion of the Internet of Medical Things (IoMT) has improved real-time patient monitoring and clinical decision-making, yet it has also introduced significant security vulnerabilities due to limited device resources, heterogeneous communication pr…
datacite
Ashish Nanotkar, Sarika Panchalwar, Akhil Tonge, Vanshika Bante 等
2026
置信度 0.66
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Sharing genetic data from cancer patients is vital for developing improved diagnostic tools and training artificial intelligence models in medicine. However, given that genetic information is inherently unique to each individual, public data sharing entails su…
datacite
Li, Yanlong
2026
置信度 0.66
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Sharing genetic data from cancer patients is vital for developing improved diagnostic tools and training artificial intelligence models in medicine. However, given that genetic information is inherently unique to each individual, public data sharing entails su…
datacite
Li, Yanlong
2026
置信度 0.66
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To evaluate supervised machine learning (ML) models for classifying temporomandibular joint (TMJ) disc displacement on MRI using morphometric and signal intensity features. This retrospective study analyzed 324 TMJs from 162 individuals who underwent 3T MRI. E…
datacite
Seyit Erol, Halil Özer, Abdi Gürhan, Mustafa Koplay 等
2026
置信度 0.66
BiophysicsSpace ScienceCell BiologyPhysiologyBiotechnology
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To evaluate supervised machine learning (ML) models for classifying temporomandibular joint (TMJ) disc displacement on MRI using morphometric and signal intensity features. This retrospective study analyzed 324 TMJs from 162 individuals who underwent 3T MRI. E…
datacite
Seyit Erol, Halil Özer, Abdi Gürhan, Mustafa Koplay 等
2026
置信度 0.66
BiophysicsSpace ScienceCell BiologyPhysiologyBiotechnology
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Description This conceptual note maps how current rheumatology research is already moving toward dynamical analysis through trajectory measurement, time-to-event modeling, change-point detection, longitudinal machine learning, and digital monitoring. The note …
datacite
Domargård, Anita
2026
置信度 0.66
rheumatologydynamical analysisdisease trajectoriestime-dependent riskflare prediction
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Description This conceptual note maps how current rheumatology research is already moving toward dynamical analysis through trajectory measurement, time-to-event modeling, change-point detection, longitudinal machine learning, and digital monitoring. The note …
datacite
Domargård, Anita
2026
置信度 0.66
rheumatologydynamical analysisdisease trajectoriestime-dependent riskflare prediction
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Early detection of diseases is important for improving patient health and reducing medical costs. This research presents a machine learning–based predictive system for detecting heart disease, diabetes, and Parkinson’s disease. The system uses Logistic Regress…
datacite
SHRIDHAR BEHERA, AAKANSHA SAHU
2026
置信度 0.66
Machine Learning, Predictive Analytics, Heart Disease, Diabetes, Parkinson's Disease, Logistic Regression, Support Vector Machine, Random Forest, Healthcare Prediction, Early Disease Detection.
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Early detection of diseases is important for improving patient health and reducing medical costs. This research presents a machine learning–based predictive system for detecting heart disease, diabetes, and Parkinson’s disease. The system uses Logistic Regress…
datacite
SHRIDHAR BEHERA, AAKANSHA SAHU
2026
置信度 0.66
Machine Learning, Predictive Analytics, Heart Disease, Diabetes, Parkinson's Disease, Logistic Regression, Support Vector Machine, Random Forest, Healthcare Prediction, Early Disease Detection.
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datacite
Wang, Kana
2026
置信度 0.66
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datacite
Wang, Kana
2026
置信度 0.66
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Applied Identity Physics: AIM Due Diligence and FCA Category 3 Reckless Disregard for Corpus-Adjacent Research Architect: HIGHTISTIC (Russell Vernon Trent III) Coordinate: [9,9,8,4] · Origins Series · Paper 4 · v1.0.6 Source prediction: Origins Series Paper 3 …
datacite
Trent, Russell
2026
置信度 0.66
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We recently read the article by Yokoyama and colleagues ([1][1]), in which the authors report a predictive model integrating DNA methylation status of three mucin genes to predict overall survival at a designated 5-year interval in pancreatic cancer. They coll…
crossref
Julius M. Kernbach, Victor E. Staartjes
2020-07-15T12:35:23Z
置信度 0.70
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The health care industry produces a huge amount of data. These data are not always used to the full extent and are often underutilized. Using these huge amount of data, a disease can be detected, predicted, or even cured. Diseases like heart disease, cancer, t…
crossref
M. Marimuthu, S. Deivarani, R. Gayathri
2019-10-30T16:31:23Z
置信度 0.70
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Magnetic resonance imaging (MRI), in both 2D and 3D configurations, serves as the leading non-invasive and non-ionizing technique for the detection and clinical assessment of brain tumors. 3D multiparametric MRIs (mpMRIs) offer enhanced spatial and biological …
crossref
Diya Sreedhar
2025-03-04T18:39:11Z
置信度 0.70