دانلود مقاله انگلیسی رایگان:استفاده از تکنیک های هوش مصنوعی  برای شناسایی ضعف در یک مجموعه داده های اداری مراقبت از سالمندان مسکونی - 2020
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  • The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set
    The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set


    ترجمه فارسی عنوان مقاله:

    استفاده از تکنیک های هوش مصنوعی برای شناسایی ضعف در یک مجموعه داده های اداری مراقبت از سالمندان مسکونی


    منبع:

    Sciencedirect - Elsevier - International Journal of Medical Informatics, 136 (2020) 104094. doi:10.1016/j.ijmedinf.2020.104094


    نویسنده:

    R.C. Ambagtsheera,d,*, N. Shafiabadya, E. Denta,b,c, C. Seibothc, J. Beilbya,d


    چکیده انگلیسی:

    Introduction: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. Objectives: We aimed to determine the effectiveness of artificial intelligence (AI) algorithms in accurately identifying frailty among residents aged 75 years and over in comparison with a calculated electronic Frailty Index (eFI) based on a routinely-collected residential aged care administrative data set drawn from 10 residential care facilities located in Queensland, Australia. A secondary objective included the identification of best-performing candidate algorithms. Methods: We designed a frailty prediction system based on the eFI identification of frailty, allocating 84.5 % and 15.5 % of the data to training and test data sets respectively. We compared the performance of 18 specific scenarios to predict frailty against eFI based on unique combinations of three ML algorithms (support vector machines [SVM], decision trees [DT] and K-nearest neighbours [KNN]) and six cases (6, 10, 11, 14, 39 and 70 input variables). We calculated accuracy, percentage positive and negative agreement, sensitivity, specificity, Cohen’s kappa and Prevalence- and Bias- Adjusted Kappa (PABAK), table frequencies and positive and negative predictive values. Results: Of 592 eligible resident records, 500 were allocated to the training set and 92 to the test set. Three scenarios (10, 11 and 70 input variables), all based on SVM algorithm, returned overall accuracy above 75 %. Conclusions: There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.
    Keywords: Artificial intelligence| Frailty | Residential facilities | Machine learning | Health records | Personal


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 182 کیلوبایت

    قیمت: رایگان


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