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• Gender-Specific Osteoporosis Risk Prediction Using Longitudinal Clinical Data and Machine Learning
  Abstract Osteoporosis is a silent yet debilitating disease that often remains undetected until fractures occur. While early prediction is crucial, most studies combine male and female datasets to train a single model, introducing bias since osteoporosis risk a…
  crossref Shyama P. Tripathy, Lohitha Saripalli, Katherine Berry, Ambalangodage C. Jayasuriya 等 2026-02-17T17:50:15Z 置信度 0.70
• Non-contrast CT radiology-clinical machine learning modeling to predict chronic hydrocephalus after aneurysmal subarachnoid hemorrhage
  Abstract Objective To establish a machine learning model based on radiomics of non-contrast CT and clinical features to predict the occurrence of chronic hydrocephalus after aneurysmal subarachnoid hemorrhage. Methods A retrospective analysis of 150 patients w…
  europepmc Haiyun Yu^, Muyun Luo, Hanlong Guo, Zecun Huang 等 2026 置信度 0.80
• Continuous Knowledge Learning in Chatbots
  crossref Zhiyuan Chen, Bing Liu 2022-06-09T04:32:53Z 置信度 0.70
• Machine Learning, Deep Learning and IoT in Smart Healthcare for Retinal Diseases Management
  The convergence of cutting-edge technologies, specifically ML and DL integrated into the IoT, is revolutionizing healthcare, particularly in the field of ophthalmology. The impact of ML and DL-based Smart IoT Healthcare Systems on retinal disease early detecti…
  crossref Saurabh Ranjan, Dilip Kumar Choubey 2024-10-02T15:32:01Z 置信度 0.70
• On Average Versus Discounted Reward Temporal-Difference Learning
  crossref John N. Tsitsiklis, Benjamin Van Roy 2002-12-30T09:36:44Z 置信度 0.70
• Clinical Machine Learning Modeling Studies: Methodology and Data Reporting
  Departments of Neurology (OMD) and Ophthalmology (OMD), Mayo Clinic, Scottsdale, Arizona; Departments of Neurology (JC) and Ophthalmology (JC), Mayo Clinic, Rochester, Minnesota; and School of Computing and Augmented Intelligence (YW), Arizona State University…
  europepmc Oana M. Dumitrascu, Yalin Wang, John J. Chen 2022 置信度 0.80
• Machine learning for predictive analytics
  crossref Sehj Kashyap, Kristin M. Corey, Aman Kansal, Mark Sendak 2021-02-10T22:43:34Z 置信度 0.70
• Average Daily Processing Prediction of Artificial Hydropower Station Based on Machine Learning
  More than 20 years ago, China's hydropower undertakings rapid development, hydropower installed capacity has been a breakthrough.With the continuous improvement of hydropower installed capacity, the optimal dispatching of hydropower system is facing great chal…
  crossref 2023-02-06T01:57:18Z 置信度 0.70
• Prediction of Water's Safety for Consumption by Machine Learning
  Water quality is the most important topic in the world. Most researchers study water using machine learning techniques to determine its potability. This paper applies similar approaches to predict the water potability and discusses why the accuracy differs. Wa…
  crossref Jingyi Li 2024-06-19T10:27:50Z 置信度 0.70
• Sociolinguistic Perspectives on Machine Learning with Text Data
  Abstract Digitized text has become a popular form of data for sociologists. But text is also the product of many different social and linguistic processes, a topic traditionally examined by sociolinguists in the case of spoken language. This chapter presents a…
  crossref AJ Alvero 2023-12-19T03:20:41Z 置信度 0.70
• Introduction to Machine Learning for Farm Animal Behavior
  Animal behavior is the study of interactions, survival strategies, and communication within the animal kingdom. It encompasses how animals respond to various elements of their environment, including both the surroundings and other organisms, such as their kin,…
  crossref Natasa Kleanthous, Abir Hussain 2024-12-18T14:35:28Z 置信度 0.70
• A Bayesian Based Machine Learning Application to Task Analysis
  Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manua…
  crossref Shu-Chiang Lin 2011-10-04T09:46:18Z 置信度 0.70
• Introduction to MAchine Learning & Knowledge Extraction (MAKE)
  The grand goal of Machine Learning is to develop software which can learn from previous experience—similar to how we humans do. Ultimately, to reach a level of usable intelligence, we need (1) to learn from prior data, (2) to extract knowledge, (3) to generali…
  crossref Andreas Holzinger 2017-07-03T10:27:31Z 置信度 0.70
• Approximation of Daily AMS‐02 Spectra With Machine Learning Methods
  Abstract Almost three thousand daily AMS‐02 proton spectra from 2011 to 2019 offer the most precise and extensive data set of cosmic ray spectra covering a wide energy range. As such, they offer a unique opportunity to test machine learning algorithms for appr…
  crossref Martin Nguyen, Pavol Bobík, Ján Genči 2025-11-19T11:14:58Z 置信度 0.70
• Machine Learning in Predicting the Physicochemical Properties of Drug Candidates
  Physicochemical properties strongly influence the pharmacokinetic and pharmacodynamic behaviour of drugs and drug-like molecules. Experimental measurement of these properties is resource-intensive, requiring significant time, cost, and specialized equipment. A…
  crossref Anila Nuthi, Vaibhav A. Dixit 2026-07-22T08:41:26Z 置信度 0.70
• Continual Learning and Catastrophic Forgetting
  crossref Zhiyuan Chen, Bing Liu 2022-06-09T01:25:55Z 置信度 0.70
• Acquisition of Brain Signals and Preprocessing for Machine Learning
  Brain signals are critical to the functioning of the body. They help to perform all day-to-day activities and provide important insights into brain function. They are pivotal for health care applications, especially in neuroscience and Brain– Computer Interfac…
  crossref S. Viveka 2026-08-14T21:23:06Z 置信度 0.70
• Supervised learning (machine learning)
  crossref Andrew Murphy, Matt Adams, Jarrel Seah 2021-10-25T01:53:55Z 置信度 0.70
• Probability and Learning
  One criticism that is often made of neural networks-especially the MLP-is that it is not clear exactly what it is doing: while we can go and have a look at the activations of the neurons and the weights, they don’t tell us much. We’ve already seen some methods…
  crossref 2020-12-22T22:34:49Z 置信度 0.70
• Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
  crossref Yongwon Lee, Bruce G. Buchanan, John M. Aronis 2002-12-22T04:48:21Z 置信度 0.70
• Assertion Detection in Clinical Natural Language Processing: A Knowledge-Poor Machine Learning Approach
  Natural language processing (NLP) have been recently used to extract clinical information from free text in Electronic Health Record (EHR). In clinical NLP one challenge is that the meaning of clinical entities is heavily affected by assertion modifiers such a…
  crossref Long Chen 2019-05-13T23:04:09Z 置信度 0.70
• Early Detection of Neurological Disorders Using Machine Learning Systems
  "This book examines the role of machine learning systems in the detection of neurological disorders such as Alzheimer disease, Parkinson's disease, schizophrenia, and depression"--Provided by publisher
  crossref 2018-11-20T00:20:24Z 置信度 0.70
• Introduction to Heart
  This chapter provides an introduction to the heart and the importance of detecting heart problems based on heart signals. It explains details about electrocardiogram signal and 4 common heart disorders including supraventricular tachycardia, bundle branch bloc…
  crossref 2018-04-25T13:33:28Z 置信度 0.70
• Artificial Intelligence and Machine Learning in Clinical Medicine
  crossref 2023-06-21T21:01:06Z 置信度 0.70
• Summary of Volume 2
  crossref 2024-10-08T16:48:48Z 置信度 0.70
• Strategies in Combined Learning via Logic Programs
  crossref Evelina Lamma, Fabrizio Riguzzi, Luís Moniz Pereira 2002-12-22T05:54:50Z 置信度 0.70
• Quantum Machine Learning and Deep Learning: Fundamentals, Algorithms, Techniques, and Real-World Applications
  Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow ex…
  crossref Maria Revythi, Georgia Koukiou 2025-08-06T07:45:11Z 置信度 0.70
• Relational Learning with Statistical Predicate Invention: Better Models for Hypertext
  crossref Mark Craven, Seán Slattery 2002-12-22T05:54:50Z 置信度 0.70
• Shallow Learning vs. Deep Learning
  crossref 2024-10-12T18:01:28Z 置信度 0.70
• Introduction to Machine Learning
  Machine learning (ML) is a subfield of artificial intelligence, the science and engineering of making intelligent machines. 2 One of the pioneers of artificial intelligence, Arthur Samuel, defined machine learning as a “field of study that gives computers the …
  crossref Butch Quinto 2020-02-22T04:03:03Z 置信度 0.70
• Learning and Updating of Uncertainty in Dirichlet Models
  crossref Enrique Castillo, Ali S. Hadi, Cristina Solares 2002-12-22T04:48:21Z 置信度 0.70
• Introduction to the Special Issue on Meta-Learning
  crossref Christophe Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil 2004-02-13T00:21:28Z 置信度 0.70
• AI and Machine Learning
  In the most recent decade, an enormous number of learning strategies have been presented in the field of the AI. Supervised learning has emerged as a major area of research in machine learning. Large numbers of the supervised learning methods have discovered a…
  crossref Manisha Verma 2022-10-11T11:13:16Z 置信度 0.70
• ADVANCING PREDICTIVE INSIGHTS IN CARDIOLOGY: A COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR EARLY DETECTION AND CLINICAL IMPLICATIONS OF HEART DISEASE.
  This research uses data to find patterns that may point to a higher risk of cardiovascular diseases and to use machine learning (ML) models to forecast heart disease, a significant cause of death worldwide (World Health Organization, 2023). Based on patient he…
  crossref Faith Tobore Edafetanure-Ibeh 2024-04-04T14:49:01Z 置信度 0.70
• Clinical Report Classification Using Natural Language Processing and Topic Modeling
  Large amount of electronic clinical data encompasses important information in free text format. To be able to help guide medical decision-making, text needs to be efficiently processed and coded. In this research, we investigate techniques to improve classific…
  crossref Efsun Sarioglu, Hyeong-Ah Choi, Kabir Yadav 2013-01-17T20:36:00Z 置信度 0.70
• A Hybrid Explainable Machine Learning Framework for Early Heart Disease Prediction Using Clinical Data
  Heart disease is a significant type of cardiovascular disease and one of the primary causes of death worldwide, which is why precise and timely diagnostic assistance mechanisms should be a major priority. This paper is a proposal of a hybrid explainable machin…
  crossref Shubham Gupta 2026-07-03T09:05:42Z 置信度 0.70
• Machine Learning-Enabled POCT Readers: From Lateral-Flow Interpretation to Quantitative, Connected Diagnostics
  Machine learning (ML)-enabled point-of-care testing (POCT) readers are transforming rapid diagnostics by reducing subjectivity in visual interpretation, enabling quantification where appropriate, and extending results into connected clinical pathways. This fra…
  crossref Paulo Pereira 2026-06-03T13:27:32Z 置信度 0.70
• Inferring clinical depression from speech and spoken utterances
  In this paper, we investigate the problem of detecting depression from recordings of subjects' speech using speech processing and machine learning. There has been considerable interest in this problem in recent years due to the potential for developing objecti…
  crossref Meysam Asgari, Izhak Shafran, Lisa B. Sheeber 2014-11-26T15:31:11Z 置信度 0.70
• Machine Learning Prediction of Postoperative Pain Improvement Following Coronary Artery Bypass Grafting Using Preoperative Clinical, Psychosocial, Fatigue, and Functional Variables
  Abstract Purpose : Postoperative pain following Coronary Artery Bypass Grafting (CABG) can delay recovery and reduce quality of life. Early identification of patients at risk of poor pain recovery may support personalized rehabilitation planning. This study ai…
  europepmc Raed Mara'Beh, Saed Mara'Beh, Osama Sawalha 2026 置信度 0.80
• Machine Learning-Based Digital Manufacturing and Predictive Asset Management for Industry 5.0
  The rapid transformation of industrial ecosystems under Industry 4.0 has accelerated the adoption of intelligent, data-driven approaches for enhancing manufacturing efficiency and asset reliability. Digital Manufacturing integrates cyber-physical systems, Indu…
  crossref Kuldeep Agnihotri, Ismatha Begum 2026-05-13T04:03:28Z 置信度 0.70
• Shallow Learning vs Deep Learning in Smart Grid Applications
  crossref Musa Yilmaz, Josep M. Guerrero 2024-10-12T18:01:28Z 置信度 0.70
• Machine Learning in Natural Product-based Drug Discovery
  Natural products (NPs) have been used as therapeutic agents for centuries due to their unique structure and medicinal properties. However, they also come with numerous challenges, including a complex structure, limited availability, and high-throughput screeni…
  crossref Gargee Mahajan, Neha Kadam, Prashant S. Kharkar 2026-07-22T08:41:26Z 置信度 0.70
• Application of machine learning and deep learning models in developmental toxicity
  crossref Qunshan Jia, George Daston 2025-09-23T11:38:32Z 置信度 0.70
• Optimization for Machine Learning: Gradient Descent
  Gradient descent is an optimization algorithm that is used to minimize the cost function of a machine learning algorithm. Gradient descent is called an iterative optimization algorithm because, in a stepwise looping fashion, it tries to find an approximate sol…
  crossref Ekaba Bisong 2019-09-27T15:06:10Z 置信度 0.70
• Deep Learning for Audio-Based Respiratory Disease Classification Using Clinical Sound Data
  Respiratory diseases, including asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia, are common global health concerns that contribute to morbidity and mortality worldwide. These conditions often present with an initial symptom such as a cough.…
  crossref Cherylene Callista Reksohartono, Crysantha Monica Lim, Alexander Agung Santoso Gunawan, Jeffrey Junior Tedjasulaksana 2025-07-22T18:00:49Z 置信度 0.70
• Rethinking MedSAM: Performance Discrepancies in Clinical Applications
  Segment Anything Model (SAM) has gained significant attention for its versatility and effectiveness in solving various image segmentation problems. Medical image segmentation (MIS) is a complicated problem compared to natural image segmentation, considering th…
  crossref Muhammad Nouman, Ghada Khoriba, Essam A. Rashed 2025-02-17T13:27:08Z 置信度 0.70
• Using machine learning to predict hypertension from a clinical dataset
  Hypertension is an illness that often leads to severe and life threatening diseases such as heart failure, thickening of the heart muscle, coronary artery disease, and other severe conditions if left untreated. An artificial neural network is a powerful machin…
  crossref Daniel LaFreniere, Farhana Zulkernine, David Barber, Ken Martin 2017-02-16T22:24:33Z 置信度 0.70
• Stratum-eval: A Normative-First Evaluation Framework for Clinical Machine Learning
  Abstract Evaluation of clinical machine learning systems has focused predominantly on discriminative performance while deferring normative questions which errors matter, to whom, and under what conditions to deployment. We think this ordering is backwards. str…
  europepmc 2026 置信度 0.80
• Methodological considerations in a radiomics-clinical machine-learning model for predicting cytoreduction completeness.
  All data generated or analysed during this study are included in this published article.
  europepmc 2026 置信度 0.80
• Ultrasound-Clinical Machine Learning Models for Differentiating Early Cervical Cancer from Myoma: A Retrospective Exploratory Study.
  Objective: To develop machine learning models by integrating transvaginal ultrasound (TVUS) with clinical indicators, conduct visual analysis of the models, and systematically assess their diagnostic efficacy in differentiating early cervical neoplastic lesion…
  europepmc 2026 置信度 0.80
• Continuous Evaluation Frameworks for Retrospective Evaluation of Clinical Machine Learning Models.
  The performance of clinical machine learning (ML) models that continuously predict the risk of a future event is typically evaluated using metrics at a single time point. While these metrics are widely accepted to evaluate the performance of ML models, they ar…
  europepmc 2026 置信度 0.80
• Multimodal AI approach combining deep learning imaging and clinical machine learning for pancreatic cancer detection.
  Pancreatic ductal adenocarcinoma (PDAC) has poor prognosis due to late diagnosis, limitations of computed tomography (CT) imaging, and low accuracy of clinical biomarkers. This study aimed to develop and validate a multimodal artificial intelligence (AI) appro…
  europepmc 2026 置信度 0.80
• A 13-year cohort study using clinical machine learning to differentiate bacterial and viral infections in young infants in a dengue hyperendemic region.
  Differentiating bacterial from viral infections in febrile young infants is challenging, particularly in dengue-hyperendemic regions. We developed and internally validated a clinical machine-learning model to enhance diagnostic accuracy in this risk population…
  pubmed Cortés-Guzmán LJ, Salgado DM, Narváez CF 2026 置信度 0.82
• Predicting Antegrade Success in Femoropopliteal Occlusions Using Radiological and Clinical Machine Learning Models.
  Purpose To develop and validate a machine learning model integrating imaging, demographic, and laboratory features to predict the technical success of antegrade endovascular approaches for femoropopliteal artery occlusion. Materials and methods The retrospecti…
  europepmc 2026 置信度 0.80
• Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy.
  Background Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality therapy) in esophageal adenocarcinoma (EAC) and gastroesophageal junction adenocarcinoma (GEJAC) may improve clin…
  europepmc 2026 置信度 0.80
• A clinical machine learning model for 1-year functional outcome prediction in acute ischemic stroke: temporal validation across evolving guidelines.
  Accurate prediction of 1-year excellent functional outcome (modified Rankin Scale [mRS] 0–1) in acute ischemic stroke (AIS) patients is vital for guiding long-term rehabilitation. However, existing tools primarily focus on short-term (3-month) outcomes and oft…
  europepmc 2026 置信度 0.80
• Early Prediction of Standing at Discharge in Moderate-to-Severe Traumatic Brain Injury: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors.
  Objective To develop and internally validate a machine learning model to predict favorable standing ability at hospital discharge in patients with moderate-to-severe traumatic brain injury, incorporating both modifiable and nonmodifiable clinical factors. Desi…
  europepmc 2026 置信度 0.80
• Diagnostic framework to validate clinical machine learning models locally on temporally stamped data.
  Background Real-world medical environments such as oncology are highly dynamic due to rapid changes in medical practice, technologies, and patient characteristics. This variability, if not addressed, can result in data shifts with potentially poor model perfor…
  europepmc 2025 置信度 0.80
• What makes clinical machine learning fair? A practical ethics framework.
  Machine learning (ML) can offer a tremendous contribution to medicine by streamlining decision-making, reducing mistakes, improving clinical accuracy and ensuring better patient outcomes. The prospects of a widespread and rapid integration of machine learning …
  europepmc 2025 置信度 0.80
• Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill children
  Objectives To streamline the development of clinical machine learning (ML) models for predicting acute neurological morbidity in critically ill children by extending our prior work to create a standardized, reproducible, and scalable workflow leveraging Fast H…
  europepmc 2025 置信度 0.80
• Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.
  Objectives To present an accurate machine-learning (ML) method and knowledge-based heuristics for automatic sequence-type identification in multi-centric multiparametric MRI (mpMRI) datasets for prostate cancer (PCa) ML. Methods Retrospective prostate mpMRI st…
  europepmc 2025 置信度 0.80
• GHOSTS: Generation of synthetic hospital time series for clinical machine learning research
  Machine learning (ML) holds great promise to support, improve, and automatize clinical decision-making in hospitals. Data protection regulations, however, hinder abundantly available routine data from being shared across sites for model training. Generative mo…
  europepmc 2024 置信度 0.80
• Clinical Machine Learning Pitfalls: Reliability of Feature Importance in Prediction of Continuous Renal Replacement Therapy in Acute Type A Aortic Dissection Assessment.
  europepmc 2025 置信度 0.80
• Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms.
  Background and objectives Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through whic…
  europepmc 2025 置信度 0.80
• An overview of clinical machine learning applications in neurology.
  Machine learning techniques for clinical applications are evolving, and the potential impact this will have on clinical neurology is important to recognize. By providing a broad overview on this growing paradigm of clinical tools, this article aims to help hea…
  europepmc 2023 置信度 0.80
• An operational guide to translational clinical machine learning in academic medical centers.
  Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at…
  europepmc 2024 置信度 0.80
• Detecting changes in the performance of a clinical machine learning tool over time.
  Background Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine lear…
  europepmc 2023 置信度 0.80
• T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.
  Background The study on predicting the differentiation grade of colorectal cancer (CRC) based on magnetic resonance imaging (MRI) has not been reported yet. Developing a non-invasive model to predict the differentiation grade of CRC is of great value. Aim To d…
  europepmc 2024 置信度 0.80
• Clinical machine learning predicting best stroke rehabilitation responders to exoskeletal robotic gait rehabilitation.
  Background Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RA…
  europepmc 2024 置信度 0.80
• An overview of the use of cutting-edge artificial intelligence (AI) modeling to produce synthetic medical data (SMD) in decentralized clinical machine learning (ML) for ovarian cancer(OC) and ovarian lymphoma(OL).
  Aim o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease. Material and methods ultilizing programmed detection…
  europepmc 2025 置信度 0.80
• Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke.
  Objective To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. Met…
  europepmc 2024 置信度 0.80
• Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning.
  As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we int…
  europepmc 2023 置信度 0.80
• A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.
  Background To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcino…
  europepmc 2024 置信度 0.80
• An adversarial training framework for mitigating algorithmic biases in clinical machine learning.
  Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framewor…
  europepmc 2023 置信度 0.80
• Clinical machine learning in parafunctional and altered functional occlusion: A systematic review.
  Statement of problem The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic…
  europepmc 2025 置信度 0.80
• Physicians' and Machine Learning Researchers' Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study.
  Background Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and…
  europepmc 2023 置信度 0.80
• Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection.
  europepmc 2023 置信度 0.80
• The ultrasound-based radiomics-clinical machine learning model to predict papillary thyroid microcarcinoma in TI-RADS 3 nodules.
  Background Conventional ultrasound (CUS) technology has proven to be successful in the identification of thyroid nodules. Moreover, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) was developed for the purpose of evalu…
  europepmc 2024 置信度 0.80
• Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models.
  This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models…
  europepmc 2023 置信度 0.80
• From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.
  Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are ap…
  europepmc 2023 置信度 0.80
• Ultrasound deep learning radiomics and clinical machine learning models to predict low nuclear grade, ER, PR, and HER2 receptor status in pure ductal carcinoma <i>in situ</i>.
  Background Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular mar…
  europepmc 2024 置信度 0.80
• Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study.
  Purpose This study assessed the impact of intraoral scanner type, operator, and data augmentation on the dimensional accuracy of in vitro dental cast digital scans. It also evaluated the validation accuracy of an unsupervised machine-learning model trained wit…
  europepmc 2023 置信度 0.80
• Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.
  Background Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high ris…
  europepmc 2024 置信度 0.80
• Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?
  McCradden et al. (2022) propose to close the “AI chasm” between algorithms and clinically meaningful application using the norms of evidence-based medicine (EBM) and clinical research, with the rat...
  europepmc 2022 置信度 0.80
• Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning
  As machine learning-based models continue to be developed for healthcare applications, greater effort is needed in ensuring that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. In this stud…
  europepmc 2022 置信度 0.80
• Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review.
  Background Racial bias is a key concern regarding the development, validation, and implementation of machine learning (ML) models in clinical settings. Despite the potential of bias to propagate health disparities, racial bias in clinical ML has yet to be thor…
  europepmc 2022 置信度 0.80
• Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.
  As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which …
  europepmc 2022 置信度 0.80
• Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.
  Objective To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs). Patients and methods We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019.…
  europepmc 2022 置信度 0.80
• Open-Source Clinical Machine Learning Models: Critical Appraisal of Feasibility, Advantages, and Challenges.
  Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market force…
  europepmc 2022 置信度 0.80
• Swarm Learning for decentralized and confidential clinical machine learning.
  Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an inc…
  europepmc 2021 置信度 0.80
• Beyond performance metrics: modeling outcomes and cost for clinical machine learning.
  Advances in medical machine learning are expected to help personalize care, improve outcomes, and reduce wasteful spending. In quantifying potential benefits, it is important to account for constraints arising from clinical workflows. Practice variation is kno…
  europepmc 2021 置信度 0.80
• A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients.
  Objective To develop an effective machine learning model to preoperatively predict the occurrence of futile recanalization (FR) of acute basilar artery occlusion (ABAO) patients with endovascular treatment (EVT). Materials and methods Data from 132 ABAO patien…
  europepmc 2023 置信度 0.80
• Physiology as a Lingua Franca for Clinical Machine Learning.
  The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a starting point for the d…
  europepmc 2020 置信度 0.80
• Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning
  Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training fra…
  europepmc 2022 置信度 0.80
• Significance of external validation in clinical machine learning: let loose too early?
  europepmc 2020 置信度 0.80
• Response to letter to the editor on "Significance of external validation in clinical machine learning: let loose too early?"
  europepmc 2020 置信度 0.80
• Why educating for clinical machine learning still requires attention to history: a rejoinder to Gauld <i>et al</i>.
  An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
  europepmc 2021 置信度 0.80
• The need to approximate the use-case in clinical machine learning.
  The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map those data into clinical predictions. As machine learning algorithms are increasingly…
  europepmc 2017 置信度 0.80
• Clinical Implementation of Pharmacogenetics-Based Machine Learning Clinical Decision Support Systems: A Scoping Review.
  Abstract BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rul…
  europepmc 2026 置信度 0.80
• The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.
  Background Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in healthcare, with significant potential to enhance nursing practice, particularly in intensive care units (ICUs). ICUs pose complex challenges, including h…
  europepmc 2026 置信度 0.80