Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients
Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/10/1896 |
| _version_ | 1797473096189345792 |
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| author | Amal H. Alharbi Hanan A. Hosni Mahmoud |
| author_facet | Amal H. Alharbi Hanan A. Hosni Mahmoud |
| author_sort | Amal H. Alharbi |
| collection | DOAJ |
| description | Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset. |
| first_indexed | 2024-03-09T20:10:11Z |
| format | Article |
| id | doaj.art-e5c50b74382c4ed3b0a778eebb39dfd2 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T20:10:11Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-e5c50b74382c4ed3b0a778eebb39dfd22023-11-24T00:19:23ZengMDPI AGHealthcare2227-90322022-09-011010189610.3390/healthcare10101896Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly PatientsAmal H. Alharbi0Hanan A. Hosni Mahmoud1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaEarly detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset.https://www.mdpi.com/2227-9032/10/10/1896deep learningelderly patientsfall risksclassification |
| spellingShingle | Amal H. Alharbi Hanan A. Hosni Mahmoud Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients Healthcare deep learning elderly patients fall risks classification |
| title | Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients |
| title_full | Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients |
| title_fullStr | Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients |
| title_full_unstemmed | Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients |
| title_short | Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients |
| title_sort | intelligent monitoring model for fall risks of hospitalized elderly patients |
| topic | deep learning elderly patients fall risks classification |
| url | https://www.mdpi.com/2227-9032/10/10/1896 |
| work_keys_str_mv | AT amalhalharbi intelligentmonitoringmodelforfallrisksofhospitalizedelderlypatients AT hananahosnimahmoud intelligentmonitoringmodelforfallrisksofhospitalizedelderlypatients |