AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling

Ageing Research Reviews, Год журнала: 2024, Номер unknown, С. 102497 - 102497

Опубликована: Сен. 1, 2024

Язык: Английский

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Navigating the Convergence of IoMT and Generative AI in Global Healthcare

Advances in healthcare information systems and administration book series, Год журнала: 2025, Номер unknown, С. 513 - 534

Опубликована: Янв. 17, 2025

This chapter examines the transformative potential of combining Generative AI with Internet Medical Things (IoMT) in healthcare. IoMT's capacity to empower ceaseless wellbeing observing and ongoing information assortment, joined man-made intelligence's high-level prescient logical power, guarantees huge progressions diagnostics, patient administration, customized medication. The focuses on case studies that demonstrate improved healthcare delivery outcomes as it novel applications these technologies. Be may, additionally addresses basic difficulties like security, moral situations, including insurance, algorithmic predisposition, independence. Systems for beating hindrances, particularly asset compelled settings, are talked about, accentuating requirement hearty structures administrative approaches.

Язык: Английский

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Disease Prediction Using Machine Learning

Опубликована: Июль 12, 2024

Язык: Английский

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Analysis of Doppler Audio Signals from the Carotid Artery

Journal of Medical and Biological Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

Язык: Английский

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Artificial Intelligence in Bacterial Infections Control: A Scoping Review

Antibiotics, Год журнала: 2025, Номер 14(3), С. 256 - 256

Опубликована: Март 2, 2025

Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role infection control remains uncertain. This scoping review aims understand the characteristics of AI applications bacterial control. Results: examines control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total 1165 articles, only articles met eligibility criteria were extracted analyzed. Five scopes synthesized data; countries, aim, type AI, advantages, limitations prevention majority reported high-income mainly by USA. most common are pathogen identification risk assessment. used is machine learning. commonest advantage predictive modeling assessment, disadvantage generalizability models. Methods: was developed according Arksey O’Malley frameworks. A comprehensive PubMed, Embase, Web Science conducted using terms, with no restrictions. Publications focusing included. Citations managed via EndNote, initial title abstract screening two authors. Data underwent narrative mapping categorization, followed construction Conclusions: need be strengthened for low-income countries. More efforts should dedicated investing models that have proven their effectiveness maximize utilization tackle challenges.

Язык: Английский

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Unveiling exosomal biomarkers in neurodegenerative diseases: LC-MS-based profiling

Extracellular Vesicle, Год журнала: 2025, Номер 5, С. 100071 - 100071

Опубликована: Март 22, 2025

Язык: Английский

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Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings

BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)

Опубликована: Март 28, 2025

Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims construct prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms. A retrospective analysis was conducted on electronic medical records of aged 20 and above, admitted Chi Mei Medical Center's intensive care unit between January 2016 December 2021. The utilized 37 features related mortality. Predictive models were developed logistic regression, Random forest, LightGBM, XGBoost, Multi-layer Perceptron (MLP), assessments feature importance, Area under the curve (AUC). total 1451 enrolled. Factors associated included lower initial GCS scores (p < 0.001), pupillary changes (P kidney disease respiratory failure requiring intubation 0.001). Negative correlations observed pupil light reflexes, as well components E(r=-0.4602), V (r=-0.4132), M(r=-0.4082). Positive identified vasopressors (r = 0.4464), FiO2 0.3901), sedative-hypnotic drugs 0.1178). XGBoost demonstrated best predictive performance (AUC 0.913), outperforming LR (0.899), RF (0.905), LightGBM (0.909), MLP (0.892). model, utilizing both 18 36 features, continues outperform Acute Physiology Chronic Health Evaluation (APACHE II) 0.001) Sequential Organ Failure Assessment (SOFA) scoring systems successfully an AI patients, exhibiting superior performance. incorporating key has been integrated into clinical practice assisting clinicians in treatment decisions communication patients' families.

Язык: Английский

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Asthma Prediction Using Fuzzification and Machine Learning Based Ensemble Classifier

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 189 - 202

Опубликована: Янв. 1, 2025

Язык: Английский

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Comprehensive insights into healthcare IoT: the role of machine learning and deep learning approaches

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Апрель 12, 2025

Язык: Английский

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Automatic priority analysis of emergency response systems using internet of things (IoT) and machine learning (ML)

Transportation Engineering, Год журнала: 2025, Номер unknown, С. 100304 - 100304

Опубликована: Янв. 1, 2025

Язык: Английский

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