Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms

Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients’ cognitive and functional statuses using machine learning algorithms

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

Genome Sequence of the Polymyxin-Producing Plant-Probiotic Rhizobacterium Paenibacillus polymyxa E681▿

Paenibacillus polymyxa E681, a spore-forming, low-G+C, Gram-positive bacterium isolated from the rhizosphere of winter barley grown in South Korea, has great potential for agricultural applications due to its ability to promote plant growth and suppress plant diseases. Here we present the complete genome sequence of P. polymyxa E681. Its 5.4-Mb genome encodes functions specialized to the plant-associated lifestyle and characteristics that are beneficial to plants, such as the production of a plant growth hormone, antibiotics, and hydrolytic enzymes

A comparison of the clinical and epidemiological characteristics of adult patients with laboratory-confirmed influenza A or B during the 2011-2012 influenza season in Korea: a multi-center study.

BACKGROUND: During the 2011/2012 winter influenza season in the Republic of Korea, influenza A (H3N2) was the predominant virus in the first peak period of influenza activity during the second half of January 2012. On the other hand, influenza B was the predominant virus in the second peak period of influenza activity during the second half of March 2012. The objectives of this study were to compare the clinical and epidemiological characteristics of patients with laboratory-confirmed influenza A or influenza B. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed data from 2,129 adult patients with influenza-like illnesses who visited the emergency rooms of seven university hospitals in Korea from October 2011 to May 2012. Of 850 patients with laboratory-confirmed influenza, 656 (77.2%) had influenza A (H3N2), and 194 (22.8%) influenza B. Age, and the frequencies of cardiovascular disorders, diabetes, hypertension were significantly higher in patients with influenza A (H3N2) (P<0.05). The frequencies of leukopenia or thrombocytopenia in patients with influenza B at initial presentation were statistically higher than those in patients with influenza A (H3N2) (P<0.05). The rate of hospitalization, and length of hospital stay were statistically higher in patients with influenza A (H3N2) (P<0.05), and of the 79 hospitalized patients, the frequency of diabetes, hypertension, cases having at least one of the comorbid conditions, and the proportion of elderly were significantly higher in patients with influenza A (H3N2) (P<0.05). CONCLUSIONS: The proportion of males to females and elderly population were significantly higher for influenza A (H3N2) patients group compared with influenza B group. Hypertension, diabetes, chronic lung diseases, cardiovascular disorders, and neuromuscular diseases were independently associated with hospitalization due to influenza. Physicians should assess and treat the underlying comorbid conditions as well as influenza viral infections for the appropriate management of patients with influenza

Clinical outcomes of hospitalized patients with laboratory-confirmed Influenza A (H3N2) and Influenza B from October 2011 to May 2012.

<p>Data are shown as numbers of patients (% of total), unless otherwise indicated.</p>*<p>chi-square test or Fisher’s exact test, unless otherwise indicated.</p>**<p>student’s unpaired t-test.</p

Factors associated with hospitalization due to laboratory-confirmed influenza A (H3N2) or B from October 2011 to May 2012.

*<p>Totally, 79 patients were hospitalized. Full model : Influenza A (H3N2), elderly, cardiovascular disorders, cerebrovascular disorders, chronic lung diseases, chronic renal diseases, diabetes mellitus, hypertension, and neuromuscular diseases.</p