Predicting pressure injury using nursing assessment phenotypes and machine learning methods

ObjectivePressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical methods, limiting their accuracy. In this paper, we describe the development of machine learning-based predictive models, using phenotypes derived from nurse-entered direct patient assessment data.MethodsWe utilized rich electronic health record data, including full assessment records entered by nurses, from 5 different hospitals affiliated with a large integrated healthcare organization to develop machine learning-based prediction models for pressure injury. Five-fold cross-validation was conducted to evaluate model performance.ResultsTwo pressure injury phenotypes were defined for model development: nonhospital acquired pressure injury (N = 4398) and hospital acquired pressure injury (N = 1767), representing 2 distinct clinical scenarios. A total of 28 clinical features were extracted and multiple machine learning predictive models were developed for both pressure injury phenotypes. The random forest model performed best and achieved an AUC of 0.92 and 0.94 in 2 test sets, respectively. The Glasgow coma scale, a nurse-entered level of consciousness measurement, was the most important feature for both groups.ConclusionsThis model accurately predicts pressure injury development and, if validated externally, may be helpful in widespread pressure injury prevention.     

“A catalyst for action”: Factors for implementing clinical risk prediction models of infection in home care settings

ObjectiveThe study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow.Materials and MethodsThis was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses’ perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis.ResultsTwo themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings.DiscussionThe findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action.ConclusionsIt is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.     

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction

ObjectiveInformative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.Materials and MethodsA systematic literature search was conducted by 2 independent reviewers using prespecified keywords.ResultsThirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).DiscussionThis is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.ConclusionsA growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.     

Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression

Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m² (end-stage renal disease, ESRD) 6 months after collecting their final laboratory test information by evaluating time-related features. A total of 463 CKD patients collected from January 2004 to December 2013 at one of the biggest dialysis centers in southern Taiwan were included in the experimental evaluation. We integrated the temporal abstraction (TA) technique with data mining methods to develop CKD progression prediction models. Specifically, the TA technique was used to extract vital features (TA-related features) from high-dimensional time-series data, after which several data mining techniques, including C4.5, classification and regression tree (CART), support vector machine, and adaptive boosting (AdaBoost), were applied to develop CKD progression prediction models. The results revealed that incorporating temporal information into the prediction models increased the efficiency of the models. The AdaBoost+CART model exhibited the most accurate prediction among the constructed models (Accuracy: 0.662, Sensitivity: 0.620, Specificity: 0.704, and AUC: 0.715). A number of TA-related features were found to be associated with the deterioration of renal function. These features can provide further clinical information to explain the progression of CKD. TA-related features extracted by long-term tracking of changes in laboratory test values can enable early diagnosis of ESRD. The developed models using these features can facilitate medical personnel in making clinical decisions to provide appropriate diagnoses and improved care quality to patients with CKD.     

基于改进传染病动力学易感-暴露-感染-恢复（SEIR）模型预测新型冠状病毒肺炎疫情

目的 研究基于传染病动力学易感-暴露-感染-恢复（SEIR）模型对新型冠状病毒肺炎（COVID-19）疫情发展情况的预测效果,为有效应对疫情提供指导。方法 利用Python爬虫自动更新功能获取中华人民共和国国家卫生健康委员会公布的疫情数据,通过改进传染病动力学SEIR模型,自动修正COVID-19基本再生数（R₀）,对中国湖北省和韩国的COVID-19疫情发展趋势进行预测。结果 模型预测的湖北省COVID-19疫情顶点在2020年2月21日,现有确诊病例数约为50 000例（2月19日）,预计疫情将于3月4日回落至30 000例以下,并在5月10日左右结束。中华人民共和国国家卫生健康委员会公布的实际数据显示,确诊人数顶点为53 371例。模型预测的韩国疫情峰值在3月7日,将于4月底结束。结论 改进的传染病动力学SEIR模型在COVID-19疫情早期实现了较准确的数据预测,政府相关部门在疫情中及时、有效的强力干预明显影响了疫情的发展进程,东亚其他国家如韩国的疫情在3月仍处于上升期,提示中国需要提防输入性疫情风险。     

血浆D-二聚体与纤维蛋白原检测结合超声检查对盆腔手术后下肢深静脉血栓的早期评价价值

目的 研究血浆D-二聚体（D-dimer）与纤维蛋白原（FIB）的动态变化对早期预测盆腔手术后下肢深静脉血栓（DVT）的意义,探讨彩色多普勒超声检查对患者DVT的早期诊断价值。方法 选取2014年1月至2016年1月在首都医科大学附属北京潞河医院妇科择期行盆腔手术并具有DVT高危因素的231例患者为研究对象,术前所有患者下肢深静脉彩色多普勒超声检查均为阴性。根据患者术后72~120 h下肢彩色多普勒超声检查结果,将患者分为血栓组（n=36）和非血栓组（n=195）。所有患者于手术前、术后第1天和术后第3天晨检测D-dimer和FIB,并进行统计分析。结果 血栓组与非血栓组比较,术后D-dimer和FIB显著增高,差异有统计学意义（P<0.05）,与术前比较,两组患者术后D-dimer和FIB显著增高,差异有统计学意义（P<0.05）。结论 血浆D-dimer、FIB检测结合彩色多普勒超声检查对妇科盆腔术后患者并发下肢DVT的早期诊断具有重要价值。     

基于Fine-Gray竞争风险模型的青年胃癌患者预后影响因素研究

目的 寻找青年胃癌患者预后影响因素,构建预后预测模型列线图,为患者的个体化预后评估提供更精确的工具.方法 通过监测、流行病学和最终结果(SEER)数据库客户端SEER*Stat 8.3.8收集2004-2015年确诊的2673例年龄为18～44岁的青年胃癌患者信息,使用R 4.0.3软件将2673例病例按照约7:3的比例随机分成训练集(1873例)与验证集(800例).以癌症特异性生存(CSS)率为关注点,在训练集中使用Fine-Gray竞争风险模型进行单因素和多因素分析,寻找青年胃癌患者CSS的影响因素,根据影响因素建立预后预测模型并绘制列线图.使用ROC曲线和校准曲线在训练集与验证集数据中对模型的预测效果进行验证.结果 训练集数据多因素分析结果表明肿瘤分级、T分期、N分期、M分期、原发灶手术情况、区域淋巴结手术情况、放化疗情况是青年胃癌患者CSS的独立影响因素.训练集中青年胃癌患者的1、3和5年累积CSS率分别为54.56％、29.70％和23.96％.根据独立预后影响因素构建的列线图,在训练集中1、3和5年CSS率的ROC曲线AUC值分别为0.817、0.864和0.887,在验证集中分别为0.820、0.899和0.890;校准曲线显示在训练集与验证集中1、3、5年CSS率预测模型的预测概率与实际概率基本一致.结论 Fine-Gray竞争风险模型能有效识别青年胃癌患者的预后影响因素,以此为依据构建的预后预测模型能有效预测患者的CSS,可为临床医师做出治疗决策提供参考.     

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

Background As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling.Objective The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation.Methods The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic.Results A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001).Conclusions A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.     

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data

ObjectiveIn applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care grows, there is an increasing need for systematic and reproducible preprocessing techniques for EHR data. Thus, we developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR.Materials and MethodsLargely data-driven, FIDDLE systematically transforms structured EHR data into feature vectors, limiting the number of decisions a user must make while incorporating good practices from the literature. To demonstrate its utility and flexibility, we conducted a proof-of-concept experiment in which we applied FIDDLE to 2 publicly available EHR data sets collected from intensive care units: MIMIC-III and the eICU Collaborative Research Database. We trained different ML models to predict 3 clinically important outcomes: in-hospital mortality, acute respiratory failure, and shock. We evaluated models using the area under the receiver operating characteristics curve (AUROC), and compared it to several baselines.ResultsAcross tasks, FIDDLE extracted 2,528 to 7,403 features from MIMIC-III and eICU, respectively. On all tasks, FIDDLE-based models achieved good discriminative performance, with AUROCs of 0.757–0.886, comparable to the performance of MIMIC-Extract, a preprocessing pipeline designed specifically for MIMIC-III. Furthermore, our results showed that FIDDLE is generalizable across different prediction times, ML algorithms, and data sets, while being relatively robust to different settings of user-defined arguments.ConclusionsFIDDLE, an open-source preprocessing pipeline, facilitates applying ML to structured EHR data. By accelerating and standardizing labor-intensive preprocessing, FIDDLE can help stimulate progress in building clinically useful ML tools for EHR data.     

Development and validation of prediction models for mechanical ventilation,renal replacement therapy,and readmission in COVID-19 patients

ObjectiveCoronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.Materials and MethodsFor each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output.ResultsThe predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.DiscussionOur models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.ConclusionsWe develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.     

基于深度学习的白内障识别与分级

目的 利用深度学习方法自动提取眼底白内障特征,构建白内障自动分类器,并可视化分析深度网络中间层特征的逐层变换过程。方法 基于临床眼底图像,使用深度卷积神经网络（CNN）从输入数据的原始表示直接学习有用的特征,对比分析CNN自动提取的特征与预定义特征的性能表现。然后利用反卷积神经网络（DN）量化分析CNN各个中间层的特征,进一步研究输入图像中对CNN的预测贡献最大的像素集,探究CNN表征白内障的具体过程。结果 使用深度学习方法构建的分类器在四分类任务中达到0.818 6的平均准确率。与现有的预定义特征集相比,利用深度CNN自动提取的特征集能提供更好的白内障特征表示。CNN中间层特征呈现从低级抽象到高级抽象的分层变换,如梯度变化到边缘,然后到边缘状发散结构的组合,最后到血管和视神经盘信息的高级抽象,这种变换过程与临床检测白内障的诊断标准相吻合。结论 基于深度学习的分类器在性能表现上优于现有分类器。该方法对检测其他眼病也可能具有潜在的应用前景。     

Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods

ObjectivesThe coronavirus disease 2019 (COVID-19) is a resource-intensive global pandemic. It is important for healthcare systems to identify high-risk COVID-19-positive patients who need timely health care. This study was conducted to predict the hospitalization of older adults who have tested positive for COVID-19.MethodsWe screened all patients with COVID test records from 11 Mass General Brigham hospitals to identify the study population. A total of 1495 patients with age 65 and above from the outpatient setting were included in the final cohort, among which 459 patients were hospitalized. We conducted a clinician-guided, 3-stage feature selection, and phenotyping process using iterative combinations of literature review, clinician expert opinion, and electronic healthcare record data exploration. A list of 44 features, including temporal features, was generated from this process and used for model training. Four machine learning prediction models were developed, including regularized logistic regression, support vector machine, random forest, and neural network.ResultsAll 4 models achieved area under the receiver operating characteristic curve (AUC) greater than 0.80. Random forest achieved the best predictive performance (AUC = 0.83). Albumin, an index for nutritional status, was found to have the strongest association with hospitalization among COVID positive older adults.ConclusionsIn this study, we developed 4 machine learning models for predicting general hospitalization among COVID positive older adults. We identified important clinical factors associated with hospitalization and observed temporal patterns in our study cohort. Our modeling pipeline and algorithm could potentially be used to facilitate more accurate and efficient decision support for triaging COVID positive patients.     

Identifying risk of opioid use disorder for patients taking opioid medications with deep learning

ObjectiveThe United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions.MethodsElectronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner’s Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve.ResultsThe long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN).ConclusionsLSTM–based sequential deep learning models can accurately predict OUD using a patient’s history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.     

Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm

ObjectiveTo build a prostate cancer (PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence (Al) technology under healthcare data platforms.MethodsAfter preprocessing of the data from Population Health Data Archive, smuothly clipped absolute deviation (SCAD) was used to select features. Random forest (RF), support vector machine (SVM), back propagation neural network (BP), and convolutional neural network (CNN) were used to predict the risk of PCa, among which BP and CNN were used on the enhanced data by SMOTE. The performances of models were compared using area under the curve (AUC) of the receiving operating characteristic curve. After the optimal model was selected, we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators.ResultsInorganic phosphorus, triglycerides, and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen (PSA). Among the four models, RF had the best performance in predicting PCa (accuracy: 96.80%; AUC: 0.975, 95% CI: 0.964-0.986). Followed by BP (accuracy: 85.36%; AUC: 0.892, 95% CI: 0.849-0.934) and SVM (accuracy: 82.67%; AUC: 0.824, 95% CI: 0.805-0.844). CNN performed worse (accuracy: 72.37%; AUC: 0.724, 95% CI: 0.670-0.779). An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators.ConclusionsThis study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform, proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy. Besides, the online calculation may enhance the practicability of Al prediction technology and facilitate medical diagnosis.     

Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India

BackgroundIn India, huge mortality occurs due to cardiovascular diseases (CVDs) as these diseases are not diagnosed in early stages. Machine learning (ML) algorithms can be used to build efficient and economical prediction system for early diagnosis of CVDs in India.MethodsA total of 1670 anonymized medical records were collected from a tertiary hospital in South India. Seventy percent of the collected data were used to train the prediction system. Five state-of-the-art ML algorithms (k-Nearest Neighbours, Naïve Bayes, Logistic Regression, AdaBoost and Random Forest [RF]) were applied using Python programming language to develop the prediction system. The performance was evaluated over remaining 30% of data. The prediction system was later deployed in the cloud for easy accessibility via Internet.ResultsML effectively predicted the risk of heart disease. The best performing (RF) prediction system correctly classified 470 out of 501 medical records thus attaining a diagnostic accuracy of 93.8%. Sensitivity and specificity were observed to be 92.8% and 94.6%, respectively. The prediction system attained positive predictive value of 94% and negative predictive value of 93.6%. The prediction model developed in this study can be accessed at http://das.southeastasia.cloudapp.azure.com/predict/ConclusionsML-based prediction system developed in this study performs well in early diagnosis of CVDs and can be accessed via Internet. This study offers promising results suggesting potential use of ML-based heart disease prediction system as a screening tool to diagnose heart diseases in primary healthcare centres in India, which would otherwise get undetected.     

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

ObjectiveWe aimed to develop a model for accurate prediction of general care inpatient deterioration.Materials and MethodsTraining and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call.ResultsTraining, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score.DiscussionLow alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering.ConclusionsMC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.     

早期胃癌临床病理特征与淋巴结转移的相关性

目的探讨早期胃癌临床病理特征与淋巴结转移的关系,为临床选择合理的治疗方案提供依据。方法选择安徽省肿瘤医院2015年3月至2018年8月行胃癌根治术的113例早期胃癌患者,回顾性分析患者的临床病理资料,分析其临床病理特征与淋巴结转移的关系。结果 113例早期胃癌患者中,淋巴结转移率为7. 96%(9/113)。多因素logistic回归分析结果显示,患者年龄为淋巴结转移的保护因素,肿瘤浸润深度及神经脉管侵犯为淋巴结转移的独立危险因素。结论肿瘤浸润深度及脉管神经侵犯与早期胃癌淋巴结转移密切相关。     

小儿原发性肠套叠灌肠复位后早期再套叠的影响因素分析

目的 探讨小儿原发性肠套叠早期再套叠发生的影响因素.方法 回顾性分析阜阳市妇女儿童医院2018年1月至2020年12月收治的320例原发性肠套叠复位成功的患儿临床资料,收集记录一般资料(性别、年龄、发病季节、轮状病毒感染情况、禁食时间)、灌肠方式(空气灌肠、温盐水灌肠)及超声特征(,否有肠系膜淋巴结肿大).根据,否再套叠分为分为有再套组(27例)与无再套组(293例),通过单因素分析筛选出肠套叠灌肠治疗成功后再套叠发生的可能危险因素,采用logistic回归分析患儿发生再套叠的影响因素.结果 单因素分析显示,有再套叠组与无再套叠组在性别、禁食时间方面比较,差异无统计学意义(P>0.05),在年龄、发病季节、轮状病毒感染与否、灌肠方式(空气灌肠、温盐水灌肠)及,否有肠系膜淋巴结肿大方面差异有统计学意义(P<0.05).logistic多因素回归分析显示,年龄、发病季节、灌肠方式、,否有肠系膜淋巴结肿大、轮状病毒感染均,再套叠发生的影响因素(OR=2.511、1.584、3.352、3.232、4.212).结论 小儿原发性肠套叠复位后早期再套叠发生的影响因素分别为年龄、发病季节、灌肠方式、,否有肠系膜淋巴结肿大、轮状病毒感染.     

A Novel Early Warning Model for Hand,Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

ObjectivesHand, foot and mouth disease (HFMD) is a widespread infectious disease that causes a significant disease burden on society. To achieve early intervention and to prevent outbreaks of disease, we propose a novel warning model that can accurately predict the incidence of HFMD.MethodsWe propose a spatial-temporal graph convolutional network (STGCN) that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019. The 2011–2018 data served as the training and verification set, while data from 2019 served as the prediction set. Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.ResultsAs the first application using a STGCN for disease forecasting, we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level, especially for cities of significant concern.ConclusionsThis model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance, which may significantly reduce the morbidity associated with HFMD in the future.