Delirium is associated with low levels of upright activity in geriatric inpatients—results from a prospective observational study

Aging Clinical and Experimental Research, Год журнала: 2024, Номер 36(1)

Опубликована: Фев. 14, 2024

Abstract Background Delirium is common in geriatric inpatients and associated with poor outcomes. Hospitalization low levels of physical activity. Motor symptoms are delirium, but how delirium affects activity remains unknown. Aims To investigate differences between without delirium. Methods We included acutely admitted patients ≥ 75 years a prospective observational study at medical ward Norwegian University Hospital. was diagnosed according to the DSM-5 criteria. Physical measured by an accelerometer-based device worn on right thigh. The main outcome time upright position (upright time) per 24 h (00.00 23.59) first day hospitalization verified status. Group were analysed using t test. Results 237 patients, mean age 86.1 (Standard Deviation (SD) 5.1), 73 (30.8%) had Mean 1 for entire group 92.2 min (SD 84.3), 50.9 50.7) 110.6 89.7) no-delirium group, difference 59.7 minutes, 95% Confidence Interval 41.6 77.8, p value < 0.001. Discussion Low raise question if immobilization may contribute outcomes Future studies should mobilization interventions could improve Conclusions In this sample inpatients, lower than

Язык: Английский

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Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique

Journal Medical Informatics Technology, Год журнала: 2024, Номер unknown, С. 6 - 10

Опубликована: Март 31, 2024

This study presents a predictive modeling framework for osteoporosis risk assessment using ensemble techniques, specifically XGBoost and Bagging. Leveraging dataset comprising comprehensive health factors influencing development, including demographic details, lifestyle choices, medical history, bone indicators, the aim is to facilitate accurate identification of individuals at risk. The consists 1958 samples, evenly distributed between osteoporosis-positive osteoporosis-negative cases. methodology involves separation features labels, followed by data splitting into training testing sets. XGBoost, powerful gradient boosting algorithm, employed as base estimator within Bagging ensemble, enhancing accuracy generalization. model trained on set evaluated cross-validation techniques ensure robustness mitigate overfitting. results classification report demonstrate promising performance metrics, with an overall 88% test set. Precision recall scores indicate strong capabilities, particularly in correctly identifying novel integration provides innovative approach prediction, harnessing strengths both algorithms improve performance. research contributes advancement management prevention strategies providing reliable tool early assessment. combination machine learning offers valuable personalized healthcare, enabling targeted interventions optimized resource allocation. Ultimately, this aims enhance patient outcomes reduce burden osteoporosis-related morbidity mortality.

Язык: Английский

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A bibliometric analysis of the advance of artificial intelligence in medicine

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, Год журнала: 2024, Номер 14(3), С. 3350 - 3350

Опубликована: Апрель 4, 2024

This bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 September 2023. Using Scopus database and keywords related AI, machine learning, deep learning medicine, tools such as VOSviewer Bibliometrix were used explore publication trends, subject areas, co-authorship networks, most productive countries, among others. 2,064 articles analyzed, a significant increase global academic production has been evident last five years. International collaboration was notable, with China United States leading knowledge contribution. The keyword analysis highlights breadth topics applications AI particular emphasis on cancer detection, dengue diagnosis, medical image analysis, In conclusion, this growing interest application need for collaborative research. findings underscore relevance these technologies key areas health care, contributing significantly advances diagnosis prognosis.

Язык: Английский

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From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

GeroScience, Год журнала: 2024, Номер unknown

Опубликована: Авг. 1, 2024

Язык: Английский

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Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review

IEEE Access, Год журнала: 2024, Номер 12, С. 122557 - 122587

Опубликована: Янв. 1, 2024

Язык: Английский

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