FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4290744487> ?p ?o ?g. }

• W4290744487 abstract "Backgrounds Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence. Materials and methods Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged &gt;65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression. Results From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy. Conclusion This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis." @default.
• W4290744487 created "2022-08-09" @default.
• W4290744487 creator A5014618160 @default.
• W4290744487 creator A5031161487 @default.
• W4290744487 creator A5031860989 @default.
• W4290744487 creator A5032192464 @default.
• W4290744487 creator A5043800775 @default.
• W4290744487 creator A5054035494 @default.
• W4290744487 creator A5058543790 @default.
• W4290744487 creator A5081650664 @default.
• W4290744487 date "2022-08-09" @default.
• W4290744487 modified "2023-09-26" @default.
• W4290744487 title "A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment" @default.
• W4290744487 cites W105070736 @default.
• W4290744487 cites W1900956557 @default.
• W4290744487 cites W1991596156 @default.
• W4290744487 cites W2078415962 @default.
• W4290744487 cites W2132343904 @default.
• W4290744487 cites W2138967628 @default.
• W4290744487 cites W2158190397 @default.
• W4290744487 cites W2161840762 @default.
• W4290744487 cites W2240915444 @default.
• W4290744487 cites W2314896697 @default.
• W4290744487 cites W2334026431 @default.
• W4290744487 cites W2404160305 @default.
• W4290744487 cites W2521242676 @default.
• W4290744487 cites W2529734919 @default.
• W4290744487 cites W2585390632 @default.
• W4290744487 cites W267168440 @default.
• W4290744487 cites W2736694775 @default.
• W4290744487 cites W2741811224 @default.
• W4290744487 cites W2753390782 @default.
• W4290744487 cites W2767236442 @default.
• W4290744487 cites W2772680887 @default.
• W4290744487 cites W2774747745 @default.
• W4290744487 cites W2781666465 @default.
• W4290744487 cites W2783718203 @default.
• W4290744487 cites W2792162277 @default.
• W4290744487 cites W2801919427 @default.
• W4290744487 cites W2803935479 @default.
• W4290744487 cites W2805432200 @default.
• W4290744487 cites W2921459696 @default.
• W4290744487 cites W2968303179 @default.
• W4290744487 cites W2973067981 @default.
• W4290744487 cites W2992403767 @default.
• W4290744487 cites W3003190291 @default.
• W4290744487 cites W3005406145 @default.
• W4290744487 cites W3005855978 @default.
• W4290744487 cites W3010369769 @default.
• W4290744487 cites W3036473980 @default.
• W4290744487 cites W3086019551 @default.
• W4290744487 cites W3087375354 @default.
• W4290744487 cites W3093479290 @default.
• W4290744487 cites W3093997868 @default.
• W4290744487 cites W3109307855 @default.
• W4290744487 cites W3159837338 @default.
• W4290744487 cites W3183975690 @default.
• W4290744487 cites W3191137513 @default.
• W4290744487 cites W3206536868 @default.
• W4290744487 cites W4295989807 @default.
• W4290744487 doi "https://doi.org/10.3389/fmed.2022.937216" @default.
• W4290744487 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36016999" @default.
• W4290744487 hasPublicationYear "2022" @default.
• W4290744487 type Work @default.
• W4290744487 citedByCount "4" @default.
• W4290744487 countsByYear W42907444872022 @default.
• W4290744487 countsByYear W42907444872023 @default.
• W4290744487 crossrefType "journal-article" @default.
• W4290744487 hasAuthorship W4290744487A5014618160 @default.
• W4290744487 hasAuthorship W4290744487A5031161487 @default.
• W4290744487 hasAuthorship W4290744487A5031860989 @default.
• W4290744487 hasAuthorship W4290744487A5032192464 @default.
• W4290744487 hasAuthorship W4290744487A5043800775 @default.
• W4290744487 hasAuthorship W4290744487A5054035494 @default.
• W4290744487 hasAuthorship W4290744487A5058543790 @default.
• W4290744487 hasAuthorship W4290744487A5081650664 @default.
• W4290744487 hasBestOaLocation W42907444871 @default.
• W4290744487 hasConcept C119857082 @default.
• W4290744487 hasConcept C120665830 @default.
• W4290744487 hasConcept C121332964 @default.
• W4290744487 hasConcept C126322002 @default.
• W4290744487 hasConcept C151956035 @default.
• W4290744487 hasConcept C154945302 @default.
• W4290744487 hasConcept C169258074 @default.
• W4290744487 hasConcept C195910791 @default.
• W4290744487 hasConcept C41008148 @default.
• W4290744487 hasConcept C61511704 @default.
• W4290744487 hasConcept C70153297 @default.
• W4290744487 hasConcept C71924100 @default.
• W4290744487 hasConcept C84525736 @default.
• W4290744487 hasConceptScore W4290744487C119857082 @default.
• W4290744487 hasConceptScore W4290744487C120665830 @default.
• W4290744487 hasConceptScore W4290744487C121332964 @default.
• W4290744487 hasConceptScore W4290744487C126322002 @default.
• W4290744487 hasConceptScore W4290744487C151956035 @default.
• W4290744487 hasConceptScore W4290744487C154945302 @default.
• W4290744487 hasConceptScore W4290744487C169258074 @default.
• W4290744487 hasConceptScore W4290744487C195910791 @default.
• W4290744487 hasConceptScore W4290744487C41008148 @default.
• W4290744487 hasConceptScore W4290744487C61511704 @default.

• next