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• Freeze the motion: Machine learning for motion correction
  crossref Haikun Qi, Chen Qin 2025-12-22T14:06:00Z 置信度 0.70
• MATLAB Machine Learning Recipes
  Harness the power of MATLAB to resolve a wide range of machine learning challenges. This new and updated third edition provides examples of technologies critical to machine learning. Each example solves a real-world problem, and all code provided is executable…
  crossref Michael Paluszek, Stephanie Thomas 2024-03-01T13:02:27Z 置信度 0.70
• AI-Powered Ecommerce
  This book takes you from decoding ecommerce business models to optimizing efficiency in AI-powered ecommerce, with strategies for success.
  crossref Ramgopal Prajapat 2024-12-13T13:17:40Z 置信度 0.70
• Machine learning for bone deformation detection in real-world applications
  crossref Chandrakant Mahobiya, Sailesh Suryanarayan Iyer 2024-11-22T05:37:30Z 置信度 0.70
• The Importance of Feature Engineering in Machine Learning Model Development
  Feature engineering is a fundamental step in machine learning (ML) model development that involves transforming raw data into meaningful features to enhance model performance. This process can greatly influence the accuracy, interpretability, and efficiency of…
  crossref Dr. Carlos Fernández 2026-03-31T07:25:34Z 置信度 0.70
• Evolutionary Approaches to Explainable Machine Learning
  crossref Ryan Zhou, Ting Hu 2023-11-01T05:06:35Z 置信度 0.70
• Title Page i
  crossref 2024-05-10T17:22:05Z 置信度 0.70
• Author biography
  crossref 2024-01-19T09:16:27Z 置信度 0.70
• Automating Data Engineering Workflows with AI and Machine Learning
  Data engineering is a critical component of modern data-driven organizations, encompassing the extraction, transformation, and loading (ETL) of data, as well as the management and optimization of data pipelines. The increasing volume, velocity, and variety of …
  crossref Aditya Banerjee 2025-06-25T09:25:21Z 置信度 0.70
• Traditional and Machine-Learning Methods for Efficacy Analysis
  crossref Ton J. Cleophas, Aeilko H. Zwinderman 2019-09-03T12:33:35Z 置信度 0.70
• Smart Health: Advancements in Machine Learning and the Internet of Things Solutions
  This chapter examines the transformative intersection of healthcare analytics, Machine Learning (ML), and the Internet of Things (IoT), exploring how these state-of-the-art technologies reshape healthcare delivery. This chapter focuses on the benefits brought …
  crossref Narasimha Rao Vajjhala, Philip Eappen 2024-10-02T15:32:01Z 置信度 0.70
• Real Time Sign Language Translator Using Machine Learning
  In today's interconnected world, effective communication is fundamental. For the deaf and mute community, communicating with those who don't understand sign language is challenging. To bridge this gap, we propose a web app translating sign language into spoken…
  crossref Ms. Pradnya Repal 2024-06-10T10:11:01Z 置信度 0.70
• Human Machine Interface Through Electromyography
  This study aimed to compare different continuous electromyography (EMG)--based interface styles for forecasting wrist and hand motions. The interfaces are moving towards continuous, simultaneous control with many degrees of freedom, utilizing data-driven strat…
  crossref Dumpeti Moses Manohar, Tirumani Prakash, Gonnuri Prudhvi, S. Teena Mrudhula 2025-05-23T17:02:44Z 置信度 0.70
• Front Matter
  crossref 2024-05-03T02:27:52Z 置信度 0.70
• Machine Learning
  crossref Arshad Khan 2024-07-29T08:02:54Z 置信度 0.70
• Machine learning
  crossref Guohui Li 2024-04-05T04:23:28Z 置信度 0.70
• Vision Bridge: Empowering Blind Individuals Using Machine Learning
  crossref 2024-11-28T05:48:53Z 置信度 0.70
• The AI Playbook
  In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it. “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book an…
  crossref Eric Siegel 2024-02-06T19:44:47Z 置信度 0.70
• Beyond Single Biomarkers: Multimodal Machine Learning for Precision Medicine Across Clinical Specialties
  This article is a preprint and has not yet been peer-reviewed. Not for clinical use. Precision medicine has traditionally relied on individual biomarkers to guide diagnosis, risk stratification, and treatment selection. However, most clinical phenotypes arise …
  crossref Simbarashe Magwenzi 2026-06-09T09:09:31Z 置信度 0.70
• Optimizing Machine Learning for ResourceConstrained Devices: A Comparative Analysis of Preprocessing Techniques and Machine Learning Algorithms
  One of the challenges in the rapid development of Internet of Things (IoT) and edge computing is deploying machine learning (ML) models on resource constrained devices (RCD). Preprocessing methods are important for improving ML model performance, especially wh…
  crossref Abdul Haseeb, Ian Cleland, Chris Nugent, James McLaughlin 2024-07-29T19:00:45Z 置信度 0.70
• Advancing Clinical Decision-Making: Utilizing Machine Learning to Predict Postoperative Stroke Following Craniotomy
  Abstract Objection: Postoperative stroke (PS) represents a significant and grave complication, which often remains challenging to detect until clear clinical symptoms emerge. The early identification of populations at high risk for perioperative stroke is esse…
  crossref Tianyou Lu, Qin Huang, Tongsen Luo, Yaxin Lu 等 2024-04-09T12:57:53Z 置信度 0.70
• Chapter 7 Neural Networks and Deep Learning
  crossref 2024-08-22T18:47:27Z 置信度 0.70
• Index
  crossref 2024-08-15T17:18:59Z 置信度 0.70
• LOGIC OF DECEPTION IN MACHINE LEARNING
  crossref 2024-04-01T13:25:24Z 置信度 0.70
• Risk Assessment Framework for Data Leakage Prevention Using Machine Learning Techniques
  Data leakage remains a critical concern for organizations handling sensitive information, requiring effective risk assessment methods to identify potential vulnerabilities. This paper proposes a machine learning-based framework for assessing data leakage risks…
  crossref Xiaolan Wu, Juan Li, Wenkun Ren 2026-01-07T18:32:21Z 置信度 0.70
• Artificial Neural Network Application on Weather Forecasting using Machine Learning Approaches
  crossref 2024-03-27T05:19:10Z 置信度 0.70
• Front Matter
  The front matter includes the Title page, Series page, Copyright page, TOC, Preface, and List of Figures.
  crossref 2024-04-11T18:47:43Z 置信度 0.70
• Machine Learning Methods for Multi-Omics Data Integration
  This book covers the latest multi-omics technologies, machine learning techniques for data integration, and multi-omics databases for validations.
  crossref 2023-11-13T20:01:33Z 置信度 0.70
• A Machine Learning Approach for Deriving Atmospheric Temperatures and Typhoon Warm Cores From FY‐3E MWTS‐3 Observations
  Abstract Machine learning has gained an increasing popularity in the fields of satellite retrieval and numerical weather modeling. In this study, machine‐learning (ML) neural‐network (NN) models are utilized to retrieve atmospheric temperatures from observatio…
  crossref Zeyi Niu, Xiaolei Zou 2024-09-25T16:10:02Z 置信度 0.70
• Machine Learning in Clinical Medicine
  crossref Jingli Ren, Yiwen Tao 2026-02-12T08:17:14Z 置信度 0.70
• Quantum and machine learning applications involving matrices
  crossref Charles R. Giardina 2024-02-16T10:15:06Z 置信度 0.70
• An Enhanced Machine Learning-Based Approach for Analysis and Prediction of Student Performance in Classroom Learning
  crossref 2024-08-30T03:27:49Z 置信度 0.70
• Initial Selection and Subsequent Updating of OML Models
  crossref Thomas Bartz-Beielstein 2024-02-05T05:02:18Z 置信度 0.70
• Early Warning System of Attrition in the BPO Industry Using Machine Learning Classification Models
  Employee attrition is one of the factors affecting gross margin erosion in the BPO industry, the fastest-growing industry in the Philippines, due to hiring and training costs. The cost of employee attrition depends on the employee's role and salary/wage level.…
  crossref Sandrilito Abogada, Laurence Usona 2024-04-06T10:23:37Z 置信度 0.70
• Evolutionary Algorithms for Fair Machine Learning
  crossref Alex Freitas, James Brookhouse 2023-11-01T05:06:35Z 置信度 0.70
• Machine Learning-Based Analytics for Premature Rheumatoid Arthritis and Osteoarthritis Detection in Clinical Practices—A Review
  People experience significant health discomfort as a result of rheumatoid arthritis (RA) and osteoarthritis (OA). There are numerous other types of arthritis that have an impact on people’s comfort and efficiency. In terms of using computer-aided diagnostic so…
  crossref Kumar M. Ganesh, Agam Das Goswami 2025-09-02T12:03:00Z 置信度 0.70
• Machine-learning operations streamlined clinical workflows of DNA methylation-based CNS tumor classification
  Abstract Background The diagnosis and grading of central nervous system (CNS) tumors, which was traditionally relied on histology, has been enhanced significantly by molecular testing, including DNA methylation profiling, which has been widely adopted for tumo…
  crossref Alexander L Markowitz, Dejerianne G Ostrow, Chern-Yu Yen, Xiaowu Gai 等 2024-01-26T18:35:12Z 置信度 0.70
• GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS
  ABSTRACT The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint ™ is a proprietary streaming algorithm desi…
  crossref Guillermo Sanz-Martín, Daniela Paula Migliore, Pablo Gómez del Campo, José del Castillo-Izquierdo 等 2024-03-12T21:14:49Z 置信度 0.70
• IoT-driven machine learning mechanisms for healthcare applications
  The integration of Internet of Things (IoT) and machine learning algorithms has revolutionized biomedical applications by enabling the analysis of vast amounts of data from interconnected devices. This chapter explores the potential of IoT-enabled machine lear…
  crossref Gopalakrishnan Karuppaiah, Karthikeyan Velayuthapandian, Sridhar Raj Sankara Vadivel 2024-10-04T11:24:27Z 置信度 0.70
• Enhancing clinical decision-making in closed pelvic fractures with machine learning models
  Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithm…
  crossref Dian Wang, Yongxin Li, Li Wang 2024-11-29T11:53:48Z 置信度 0.70
• Predicting cognitive decline in Alzheimer’s disease using minimal clinical features: A machine learning approach with the NACC cohort
  Early identification of individuals at risk for Alzheimer’s disease–related cognitive decline is crucial for timely intervention and clinical trial enrollment. We developed a machine learning model using only five routinely collected clinical variables, age, s…
  crossref Maryam Tarkesh Esfahani 2026-06-02T09:48:23Z 置信度 0.70
• Advanced Predictive Modeling for Hepatitis C Diagnosis Using Machine Learning
  The hepatitis C virus (HCV) virus, which has infected nearly fifty million individuals across the world, has recently been receiving significant attention. With the advent of artificial intelligence, there is an increasing effort to predict the HCV by utilizin…
  crossref Mahdyeh Abbasi Hezari, Marzieh Baes, Atyeh Abassi Hezari, Mobina Hassanbabaei 2024-11-19T08:53:00Z 置信度 0.70
• The Role of Machine Learning in Predicting Energy Consumption Patterns
  Machine learning (ML) techniques have gained significant attention in the prediction of energy consumption patterns due to their ability to handle complex, non-linear relationships within data. This paper explores various ML models, such as regression, classif…
  crossref Dr. Lisa Morgan 2026-03-31T08:00:56Z 置信度 0.70
• Predicting Depressive Disorders in Diabetic Workers: A Comparative Analysis of Relevance Vector Machine and Traditional Machine Learning Models
  crossref 2024-10-14T08:53:35Z 置信度 0.70
• Innovation, KPIs, Best Practices, and More for Machine Learning
  crossref Patanjali Kashyap 2023-12-22T12:03:36Z 置信度 0.70
• Car Price Prediction Using Machine Learning
  crossref Rui Chen 2026-02-19T15:45:45Z 置信度 0.70
• RocMLMs: Predicting Rock Properties Through Machine Learning Models
  Abstract Mineral phase transformations significantly alter the bulk density and elastic properties of mantle rocks and consequently have profound effects on mantle dynamics and seismic wave propagation. These changes in the physical properties of mantle rocks …
  crossref Buchanan Kerswell, Nestor G. Cerpa, Andréa Tommasi, Marguerite Godard 等 2024-10-23T06:45:38Z 置信度 0.70
• Comparison and Recognition of Images
  This chapter discusses the problem of image matching or locating a known part of the image, which is usually described as a pattern. This type of problem is typical in applications, such as the search for reference points in stereo vision, the location of a ce…
  crossref Erik Cuevas, Alma Nayeli Rodríguez 2024-01-02T16:14:23Z 置信度 0.70
• Clinical Applications of Machine Learning in the Diagnosis, Classification and Prediction of Heart Failure
  Heart failure (HF) remains a leading cause of mortality and morbidity worldwide, necessitating innovative approaches to its diagnosis, classification, and management. This paper explores the transformative potential of machine learning (ML) technologies in the…
  crossref Eman H. Abd-Elkawy, Rabie Ahmed 2024-04-08T11:00:16Z 置信度 0.70
• Machine Learning techniques applied to identifying clinical factors regarding automated medical prognosis
  e de Computação, Universidade de São Paulo, São Carlos -SP, 2022.Algoritmos de Aprendizado de Máquina têm apresentado resultados promissores em diversas áreas do conhecimento, entre elas a medicina preventiva.Ao passo que técnicas de aprendizado profundo têm s…
  crossref Pedro Henrique Ferracini de Barros 2023-01-19T11:43:05Z 置信度 0.70
• Predicting Functional Outcomes of Total Hip Arthroplasty Using Machine Learning: A Systematic Review
  The aim of this review was to assess the reliability of machine learning (ML) techniques to predict the functional outcome of total hip arthroplasty. The literature search was performed up to October 2023, using MEDLINE/PubMed, Embase, Web of Science, and NIH …
  crossref Nick D. Clement, Rosie Clement, Abigail Clement 2024-01-22T11:36:41Z 置信度 0.70
• Automated machine learning with R: AutoML tools for beginners in clinical research
  Recently, interest in machine learning (ML) has increased as the application fields have expanded significantly. Although ML methods excel in many fields, establishing an ML pipeline requires considerable time and human resources. Automated ML (AutoML) tools o…
  crossref Youngho Park 2024-09-20T00:48:07Z 置信度 0.70
• Diagnosing coronaviruses (COVID-19) using machine learning
  crossref Sheikh Burhan Ul Haque, Aasim Zafar, Ahmad Raza Shibli, Samudrala Gourinath 2024-07-19T05:34:32Z 置信度 0.70
• Application of machine learning models for power systems security assessment
  The integration of machine learning techniques in power system security assessment has emerged as a transformative approach to enhancing the reliability and efficiency of electrical grids. This study explores the application of machine learning algorithms for …
  crossref Mukesh Singh, Sushil Chauhan, Ankur Maheshwari 2024-12-19T15:48:22Z 置信度 0.70
• Clinical Utility of Psychiatric Neuroimaging: Exploring Psychoradiology and Use of Machine Learning in Psychoradiology
  Psychoradiology, an emerging discipline, applies advanced radiological imaging technologies to psychiatric disorders.Over the past thirty years, progress in brain imaging has significantly enhanced our understanding of psychiatric illnesses and treatment outco…
  crossref Ishaan Bakshi 2023-12-15T06:06:33Z 置信度 0.70
• Machine Learning Algorithmic Model for Pairs Trading
  crossref R. Sivasamy, Dinesh K. Sharma, Sediakgotla, B. Mokgweetsi 2024-09-20T13:01:53Z 置信度 0.70
• A Machine Learning-Based Fall Risk Prediction Model for Chinese Older Adults
  OBJECTIVE: To predict the risk of falls in older Chinese adults, and to develop a machine learning model for assessing fall risk based on longitudinal study data, which can help identify high-risk individuals and provide a basis for formulating effective inter…
  crossref Jie Ren, Shumin Yin, Fengjun Li, Fang Xia 等 2025-04-14T17:35:47Z 置信度 0.70
• Food Sales Analysis and Prediction Using Machine Learning
  This study uses Support Vector Machines (SVM) with Positive and Negative Binomial distributions to give a thorough analysis and prediction model for food sales. The project's goal is to improve sales forecasting's resilience and accuracy in the dynamic and int…
  crossref Sadasivam V.R, Narendharakumar G, Ragavan M, Prabhuram A 2024-05-21T17:20:38Z 置信度 0.70
• Effect of Data Heterogeneity in Clinical MALDI-TOF Mass Spectra Profiles on Direct Antimicrobial Resistance Prediction through Machine Learning
  Abstract The matrix-assisted laser desorption-ionization time-of-flight mass spectrometry has become a powerful tool for accurate species identification in routine diagnostic microbiology. Recently, the application of machine learning models with MALDI-TOF mas…
  crossref Youngjun Park, Michael Weig, Christine Noll, Oliver Bader 等 2024-10-20T13:20:13Z 置信度 0.70
• The Intersection of Data Governance and Machine Learning in Clinical Trials
  The Intersection of Data Governance and Machine Learning inClinical Trials
  crossref Ammar Hassan 2023-09-07T05:03:10Z 置信度 0.70
• Managing Postembolization Syndrome Through a Machine Learning–Based Clinical Decision Support System
  Although transarterial chemoembolization has improved as an interventional method for hepatocellular carcinoma, subsequent postembolization syndrome is a threat to the patients' quality of life. This study aimed to evaluate the effectiveness of a clinical deci…
  crossref Minkyeong Kang, Myoung Soo Kim 2024-09-26T17:00:23Z 置信度 0.70
• Unlocking the future of leadership: Navigating human-machine collaboration in the age of machine learning
  I n an era where prolonged stability often masks the underlying fragility of our society, awareness of these vulnerabilities tends to diminish.The rapid pace of technological advancements and the increasing complexities of our global landscape present unpreced…
  crossref Christopher Peterka 2024-08-14T09:57:55Z 置信度 0.70
• Enhancing Clinical Research with Synthetic Patient Data: Leveraging ChatGPT for Improved Diagnostic Models
  Recent strides in machine learning (ML) and generative artificial intelligence (GenAI) are transforming clinical research, opening new possibilities for privacy-conscious, data-driven insights into diagnosis, treatment and patient care.In clinical research, da…
  crossref Mahnoor Mughal 2024-12-23T06:09:24Z 置信度 0.70
• Benchmarking Machine Learning Missing Data Imputation Methods in Large-Scale Mental Health Survey Databases
  Abstract Databases with mental and behavioral health surveys suffer from missingness when participants skip the entire survey, affecting the data quality and sample size. We investigated the missing data patterns and evaluate the imputation performance in Simo…
  crossref Preethi Prakash, Kelly Street, Shrikanth Narayanan, Bridget A. Fernandez 等 2024-05-15T02:41:15Z 置信度 0.70
• Machine-Learning-Assisted Impedance Component Analysis Enables Standardizable Surface Protein Analysis of Extracellular Vesicles Using Engineered Nanovesicles.
  Characterizing surface protein heterogeneity on extracellular vesicles remains challenging but essential for understanding their biological functions and clinical applications. Here, this study introduces an integrated platform that combines engineered cell-de…
  europepmc 2026 置信度 0.80
• Machine Learning Models for Predicting Pain, Fatigue, Depression, Anxiety, and Malnutrition in Cancer Patients: A Systematic Review and Meta-Analysis.
  Introduction Cancer-related symptoms including pain, fatigue, depression, anxiety, and malnutrition drive poor quality of life and adverse clinical outcomes in cancer patients. While machine learning (ML) models are increasingly developed to predict these symp…
  europepmc 2026 置信度 0.80
• Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.
  Background Cardiovascular disease (CVD) pathogenesis is strongly associated with environmental exposures and metabolic factors. This study aimed to investigate the relationship between urinary levels of volatile organic compounds (VOCs), heavy metals, and seru…
  europepmc 2026 置信度 0.80
• Comparison of Machine Learning Models for Predicting Recurrent Lumbar Disc Herniation After Percutaneous Endoscopic Lumbar Discectomy.
  Background : Recurrent lumbar disc herniation (rLDH) significantly impairs outcomes following percutaneous endoscopic lumbar discectomy (PELD). Accurate individualized risk prediction remains challenging. This study aimed to develop and compare multiple machin…
  europepmc 2026 置信度 0.80
• Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.
  Hirschsprung's disease (HD) is characterised by absence of ganglion cells in the distal large intestine, requiring accurate histopathological diagnosis. Conventional diagnostic methods are time-consuming, subjective, and demand specialised expertise. While art…
  europepmc 2026 置信度 0.80
• From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).
  Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (A…
  europepmc 2026 置信度 0.80
• Identification and risk-factor analysis for individuals at high risk for keratoconus via machine learning and logistic regression.
  Purpose To evaluate keratoconus (KC) risk factors and to develop a machine-learning (ML) model for KC and myopia classification. Methods In this retrospective single-center cross-sectional study, demographic and lifestyle data from patients with KC and individ…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning for 30-Day Mortality in PIVSR: A Cohort Study.
  Objective Post-infarction ventricular septal rupture (PIVSR) is a fatal mechanical complication of acute myocardial infarction. We aimed to develop and interpret a machine learning (ML) model to predict 30-day mortality using routinely available clinical varia…
  europepmc 2026 置信度 0.80
• Integrating MR dynamic radiomics and clinical parameters for machine learning-based prediction of short-term response to induction chemotherapy in nasopharyngeal carcinoma.
  Background and purpose Accurate prediction of short-term response to induction chemotherapy (ICT) in nasopharyngeal carcinoma (NPC) remains a critical unmet clinical need. We aimed to construct a multimodal, interpretable machine learning (ML) framework integr…
  europepmc 2026 置信度 0.80
• Interpretable Machine Learning for Predicting Suboptimal 12-Month Growth Response to Recombinant Human Growth Hormone in Children with Idiopathic Short Stature: A Dual-Center External Validation Study.
  Background/Objectives : Individual responses to recombinant human growth hormone (rhGH) therapy in children with idiopathic short stature (ISS) vary substantially, limiting pretreatment decision-making. This study aimed to develop and externally validate an in…
  europepmc 2026 置信度 0.80
• Machine learning prediction of postoperative pulmonary infection in patients who underwent thoracoscopic lung cancer resection: a retrospective case-control study.
  Background Accurate identification of patients at high risk of pulmonary infection after thoracoscopic lung cancer resection is important for timely and targeted preventive measures. Methods for determining the risk of pulmonary infection after thoracoscopic l…
  europepmc 2026 置信度 0.80
• FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.
  Background/Objectives : The precise identification of Alzheimer's disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phase…
  europepmc 2026 置信度 0.80
• Development of a prediction model for infectious mononucleosis using machine learning algorithms based on blood cell analysis parameters.
  Background Infectious mononucleosis (IM) presents with nonspecific clinical manifestations, leading to frequent misdiagnosis or delayed diagnosis, and is associated with potentially severe complications. Existing etiological diagnostic methods are characterize…
  europepmc 2026 置信度 0.80
• MACHINE LEARNING VERSUS PENALISED LOGISTIC REGRESSION FOR PREDICTING IN-HOSPITAL MORTALITY IN RUPTURED ABDOMINAL AORTIC ANEURYSM: A COMPARISON USING A UNIFORM VALIDATION FRAMEWORK.
  Background Ruptured abdominal aortic aneurysm (rAAA) remains associated with substantial in-hospital mortality. Although machine-learning methods can model complex nonlinear relationships between admission characteristics and outcome, their incremental value o…
  europepmc 2026 置信度 0.80
• Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.
  Background Significant liver fibrosis is often clinically silent but predicts adverse outcomes. We developed and externally validated an interpretable machine-learning (ML) framework using readily obtainable demographic, anthropometric, and clinical variables …
  europepmc 2026 置信度 0.80
• A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.
  Background Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require multidimensional perioperative risk assessment. Large language models (LLMs) have shown potential in clinical pr…
  europepmc 2026 置信度 0.80
• Machine learning classification of prevalent chronic disease using multidimensional social, behavioral, and psychological determinants: A cross-sectional analysis of the 2024 national health interview survey.
  Background Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional clinical risk factors have been extensively studied, the predictive utility of multidimensional social determin…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus.
  Aims/introduction Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed …
  europepmc 2026 置信度 0.80
• A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.
  Artificial intelligence (AI) is increasingly applied in healthcare to support diagnosis and treatment by leveraging longitudinal multimodal data, despite persistent challenges such as missing data and heterogeneity. This systematic review, conducted in accorda…
  europepmc 2026 置信度 0.80
• Plasma Amino Acid Signatures Associated with Disease Progression and Hypertension in Autosomal Dominant Polycystic Kidney Disease: A Targeted Metabolomics and Machine Learning Approach.
  Background: Autosomal dominant polycystic kidney disease (ADPKD) is a clinically heterogeneous disorder often leading to end-stage renal disease (ESRD). Prognostication of disease progression remains a major clinical challenge. This study aimed to identify pla…
  europepmc 2026 置信度 0.80
• Unsupervised Ensemble Learning for Active Drug-Induced Liver Injury Surveillance: Integrating Pharmacokinetic Burden With Enzyme Trajectories.
  Purpose Traditional drug-induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. To address this, a fundamentally drug-agnostic unsupervised ensemble framework, integrating pharmacokinetic (WC…
  europepmc 2026 置信度 0.80
• Construction and Validation of a Machine Learning Model Based on Clinical and Microbiomic Features for Predicting High Mucus Secretion in COPD.
  Objective To evaluate clinical and airway microbiome features of excessive mucus secretion (CMH) in COPD progression and apply machine learning for CMH status identification. Methods A total of 319 COPD patients from Changzhi People's Hospital (May 2020-March …
  europepmc 2026 置信度 0.80
• Novel indicator for colonoscopy insertion difficulty and its application in predictive modelling: a multicentre prospective study protocol in China.
  Background Colonoscopy is a cornerstone for the screening and diagnosis of colorectal diseases. Colonoscopy insertion difficulty is associated with examination quality, procedural efficiency and patient comfort. Cecal intubation time (CIT) is commonly used as …
  europepmc 2026 置信度 0.80
• Machine-Learning Prediction of Contrast-Induced Encephalopathy After Neurointerventional Procedures.
  CIE is a rare yet severe complication following neurointerventional procedures, for which reliable noninvasive predictive tools remain lacking. This study aimed to develop and validate a ML model for predicting CIE using perioperative clinical and procedural d…
  europepmc 2026 置信度 0.80
• An Interpretable Machine Learning Model for Predicting in-Hospital Progression in Initially Mild Hypertriglyceridemia-Induced Acute Pancreatitis Using Clinical and Non-Contrast CT Features.
  Introduction Early identification of patients with initially mild hypertriglyceridemia-induced acute pancreatitis (HTG-AP) who are at risk of an unstable disease course remains challenging using conventional assessment alone. This study aimed to develop an int…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.
  Objective To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a p…
  europepmc 2026 置信度 0.80
• Optimizing Laboratory Autoverification in Complete Blood Count Testing Using Machine Learning: A Performance Evaluation Study.
  Background Autoverification improves laboratory efficiency by reducing manual reviews; however, rule-based systems may lack flexibility, particularly in complete blood count (CBC) testing. Machine learning (ML) can potentially enhance performance by recognizin…
  europepmc 2026 置信度 0.80
• Study on multi-modal feature fusion machine learning classification model of Chinese and Western medicine for depressive symptoms in middle-aged and older patients with chronic low back pain.
  Objective To construct a machine learning classification model integrating multi-modal features of Chinese and Western medicine for screening the risk of depressive symptoms in middle-aged and older patients with chronic low back pain (CLBP). Methods A cross-s…
  europepmc 2026 置信度 0.80
• The role of "red flags" in the diagnostic work-up of hereditary transthyretin amyloidosis: a study using a machine-learning approach.
  Introduction Hereditary transthyretin amyloidosis (ATTRv) is a rare progressive, potentially life-threatening multisystem disorder caused by mutations in the transthyretin (TTR) gene, with variable penetrance and heterogeneous phenotypes, often leading to diag…
  europepmc 2026 置信度 0.80
• Machine Learning Prediction of Local Failure in GI Brain Metastases on the Day of Gamma Knife Radiosurgery.
  Background Time to local failure after stereotactic radiosurgery (SRS), including Gamma Knife radiosurgery (GKRS), for gastrointestinal (GI) brain metastases is difficult to predict and may vary substantially across tumors and patients. We developed and intern…
  europepmc 2026 置信度 0.80
• Construction and Validation of a 90-Day Mortality Risk Prediction Model Based on Interpretable Machine Learning for Acute Ischemic Stroke Patients Undergoing Mechanical Thrombectomy.
  Background/Objectives : The accurate prediction of postprocedural mortality is critical for clinical decision-making; however, research on mortality risk models for patients undergoing mechanical thrombectomy remains limited. This study aimed to develop and va…
  europepmc 2026 置信度 0.80
• Development of an interpretable machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia: A retrospective cohort study.
  ObjectiveTo develop a machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia and interpret the model using SHapley additive exPlanations (SHAP) to assist clinicians in early identification of intestinal necrosis an…
  europepmc 2026 置信度 0.80
• Machine learning-derived serum carbon dioxide trajectories in predicting prognosis of patients with acute aortic dissection.
  JOURNAL/mgres/04.03/01612956-990000000-00114/figure1/v/2026-08-19T154102Z/r/image-tiff Venous partial pressure of carbon dioxide (PvCO2) indicates postoperative adverse outcomes in patients with acute aortic dissection, and its trajectories over time affecting…
  europepmc 2026 置信度 0.80
• Integrating Complete Blood Count Parameters with Demographic Characteristics for Obstructive Sleep Apnea Prediction in Chinese Adults: A Machine Learning Approach.
  Purpose To develop and validate machine learning models integrating complete blood count (CBC) parameters with demographic characteristics for obstructive sleep apnea (OSA) risk stratification in adults with suspected OSA referred to a tertiary sleep clinic in…
  europepmc 2026 置信度 0.80
• Predictive Risk Models for Frailty Onset in Older Adults: A Scoping Review of Methodological Trends, Model Performance, and Clinical Translation Gap.
  Purpose Identifying older adults at risk of frailty is crucial for early intervention. Although numerous prediction models have emerged, no scoping review has systematically mapped their methodological trends and barriers to clinical implementation. This scopi…
  europepmc 2026 置信度 0.80
• Prediction of pediatric lower urinary tract dysfunction using uroflowmetry data and machine learning with explainable AI techniques.
  Purpose To develop and evaluate a machine learning-based approach for the interpretation of uroflowmetry in pediatric lower urinary tract dysfunction, aiming to reduce interobserver variability and improve diagnostic consistency while maintaining clinical inte…
  europepmc 2026 置信度 0.80