(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.

As the amount of re-sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high-performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high-performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

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.

욕창이 발생하면 환자의 통증과 스트레스가 증가할 뿐만 아니라 치료 기간의 연장과 치료비의 가중으로 인해 신체적, 정신적, 경제적 어려움을 초래하므로 조기진단 및 예측을 통한 욕창의 예방이 매우 중요하다. 본 연구에서는 전자의무기록 중 혈액 검사 데이터에 대한 시계열 분석을 통해 미래의 욕창 발생여부를 예측할 수 있는 딥러닝 기반의 예측 모델을 제안한다. 신뢰도 높은 욕창 발생 여부 예측을 위해 본 연구에서는 욕창 예측에 영향력이 큰 검사 항목을 강조할 수 있도록 새롭게 설계된 attention mechanism과 전자의무기록에 대한 시계열 모델링이 가능한 GRU를 결합한 예측 모델을 제안한다. 제안된 모델의 예측 성능은 accuracy 기준 0.903으로 비교 모델들 대비 우수한 결과를 나타냈다. 본 연구는 전자의무기록 중 비교적 손쉽게 주기적으로 수집될 수 있는 혈액 검사 데이터에 대한 시계열 분석을 통해 정확한 욕창 발생 예측이 가능함을 보였다는 점에서 의의가 있다.

In this work, the recurrent neural networks (RNNs) for medical examination data prediction with missing information are proposed. Simple recurrent network (SRN), long short-term memory (LSTM) and gated recurrent unit (GRU) are selected among many variations of RNNs for the missing information imputation while they are also used to predict the future medical examination data. Besides, the missing information imputation based on bidirectional LSTM is also proposed to consider past information as well as the future information in the imputation process, while the traditional RNNs can only consider the past information during the imputation. We implemented medical examination results prediction experiment using the examination database of Koreans. The experimental results showed that the proposed RNNs worked better than the baseline linear regression method. Besides, the bidirectional LSTM performed best for missing information imputation.

Summary: 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. Contact:melim@etri.re.kr