No abstract

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76–0.85), sensitivity of 0.82 (95% CI of 0.74–0.89), specificity of 0.72 (95% CI of 0.67–0.76), F1 score of 0.76 (95% CI of 0.72–0.79), and accuracy of 0.75 (95% CI of 0.72–0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.

This research introduces an approach to detect malware attacks using blockchain technology that integrates signature-based and behavioralbased methods. The proposed system uses a decentralized blockchain network to share and store malware signatures and behavioral patterns. This enables faster and more efficient detection of new malware files. The signature-based method involves storing the signatures in the blockchain and the sharing of the signature of malware files among the user nodes of the p2p blockchain network, while the behavioral-based approach analyzes the behavior and actions of files in a separate virtualized environment to identify suspicious patterns. This system addresses the limitations of conventional signature-based methods, which can be evaded by polymorphic malware, and behavioral-based methods, which may generate false positives. The results of the evaluation indicate that the proposed system achieves high detection rates while maintaining low false positives. Overall, the proposed system offers an effective and efficient approach to malware detection by utilizing the strengths of both signature-based and behavioral-based methods and utilizing the security and transparency benefits of blockchain technology.

No abstract

No abstract