Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients.

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.

Design of a context-aware model to enhance medication adherence.

Medication adherence is important to patients who suffer from chronic disease. Regular medication activity reduces the cost of caring disease and prohibits the worsening of disease condition. To support patients taking medicine correctly, we developed a medication assistance system which alarms medication situation through multimedia messages and help patients to take a medicine. To enable the system copes with various situations related to a medication service, we designed a medication context model and implemented a state based context aware application. We also applied our system to patients and saw a little improvement in medication adherence.

Proactive medication assistances based on spatiotemporal context awareness of aged persons.

In this paper, we developed a system that could assist appropriate activities for medication adherence of the elderly. It employs a proactive knowledge which is represented as templates predefined for their medication activities. The knowledge-based assistance depends on the contexts considerably, which the system can recognize by continuously monitoring the current position and the time-schedule for their medications. The monitoring is performed with ultrasonic sensors and infrared sensors mounted in a display and a pillbox mainly. According to the recognized contexts, the medication activities can he serviced through old person-friendly multimedia display. In special, since the knowledge is well-defined by XML, only its content modification can provide a variety of services individually customized to the elderly.

Distance correction system for localization based on linear regression and smoothing in ambient intelligence display.

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression and smoothing method, by which distance information of a user who approaches to the display can he accurately output even in an unanticipated condition using a passive infrared VIR) sensor and an ultrasonic device. The developed system consists of an ambient intelligence display and an ultrasonic transmitter, and a sensor gateway. Each module communicates with each other through RF (Radio frequency) communication. The ambient intelligence display includes an ultrasonic receiver and a PIR sensor for motion detection. In particular, this system selects and processes algorithms such as smoothing or linear regression for current input data processing dynamically through judgment process that is determined using the previous reliable data stored in a queue. In addition, we implemented GUI software with JAVA for real time location tracking and an ambient intelligence display.

Design of a description language for generating wrapper to collect biological data.

The biological data are scattered in various areas with various formats and they are changing continuously. Therefore, data integration becomes an important issue to provide researcher a dynamic access of data. In the data integration process, the method of extracting heterogeneous data dynamically from the data source is an essential part. Data extraction method using wrapper can provide flexibility and extensibility to an integration system.