ObjectivesTo investigate risk factors for unplanned readmission in ischemic stroke patients within 31 days by using random forest algorithm.MethodsThe record of readmission patients with ischemic stroke within 31 days from 24 hospitals in Beijing between between 2015 and 2016 were collected. Patients were divided into two groups according to the occurrence of readmission within 31 days or not. Chi-squared or Mann-Whitney U test was used to select variables into the random forest algorithm. The precision coefficient and the Gini coefficient were used to comprehensively assess the importance of all variables, and select the more important variables and use the margind effect to assess relative risk of different levels.ResultsA total of 3 473 patients were included, among them 960 (27.64%) were readmitted within 31 days after stroke hospitalization. Based on the result of random forest, the most important variables affecting the risk of unplanned readmission within 31 days included the length of hospital stay, age, medical expense payment, rank of hospital, and occupation. When hospitalization was within 1 month, 10-day-hospitalization-stay patients had the lowest risk of rehospitalization; the younger the patients was, the higher the risk of readmission was. For ranks of hospital, patients from tertiary hospital had higher risk than secondary hospital. Furthermore, patients whose medical expenses were paid by free medical service and whose occupations were managers or staffs had higher risk of readmission within 31 days.ConclusionsThe unplanned readmission risk within 31 days of discharged ischemic stroke patients was connected not only with disease, but also with personal social and economic factors. Thus, more attention should be paid to both the medical process and the personal and family factors of stroke patients.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.
Alzheimer’s disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject’s MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.