Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

上海市居家康复治疗项目实施现况及影响因素

目的了解上海市居家康复治疗项目实施现状,探究影响社区开展居家康复的因素,为进一步提升居家康复服务质量及利用率提出可行建议。方法2021年8—12月对上海市3个区的所有社区卫生服务中心(共36家)进行问卷调查,分别建立决策树模型、神经网络模型分析社区开展居家康复服务的影响因素。结果总计发放机构调查问卷36份,回收36份,回收率100%。无论是西医康复治疗项目还是中医康复治疗项目,社区开展率均高于居家开展率。康复床位数、强制性运动疗法、轮椅操作训练等是影响社区开展居家康复的因素。结论上海市居家康复服务开展率低于社区,建议通过增加机构康复床位数量、探索适宜的居家康复治疗项目、增加康复人员轮椅操作培训等方式提高居家康复服务的数量和质量。

Analysis of hospitalization costs related to fall injuries in elderly patients

BACKGROUND With the aging world population,the incidence of falls has intensified and fallrelated hospitalization costs are increasing.Falls are one type of event studied in the health economics of patient safety,and many developed countries have conducted such research on fall-related hospitalization costs.However,China,a developing country,still lacks large-scale studies in this area.AIM To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based,cost-related groupings.METHODS A retrospective study was conducted.Patient information and cost data for elderly inpatients(age≥60 years,n=3362)who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China.Quantile regression(QR)analysis was used to identify the factors related to fall-related hospitalization costs.A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes.RESULTS The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB,and the reimbursement rate of medical benefit funds was 51.0%(92039709.52 RMB/180479203.03 RMB).The medical material costs were the highest component of the total hospitalization cost,followed(in order)by drug costs,test costs,treatment costs,integrated medical service costs and blood transfusion costs The QR results showed that patient age,gender,length of hospital stay,payment method,wound position,wound type,operation times and operation type significantly influenced the inpatient cost(P<0.05).The cost grouping model was established based on the QR results,and age,length of stay,operation type,wound position and wound type were the most important influencing factors in the model.Furthermore,the cost of each combination varied significantly.CONCLUSION Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.

Heuristic solution using decision tree model for enhanced XML schema matching of bridge structural calculation documents

Research on the quality of data in a structural calculation document(SCD)is lacking,although the SCD ofa bridge is used as an essential reference during the entire lifecycle of the facility.XML Schema matching enables qualitative improvement of the stored data.This study aimed to enhance the applicability of XML Schema matching,which improves the speed and quality of information stored in bridge SCDs.First,the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs.The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations.Second,the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree.The decision tree model was built using the content elements stored in the SCD,design companies,bridge types,and weight factors as input variables,and the matching accuracy as the target variable.The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.

Four-protein model for predicting prognostic risk of lung cancer

Patients with lung cancer at the same stage may have markedly different overall outcome and a lack of specific biomarker to predict lung cancer outcome.Heat-shock protein 90β(HSP90β)is overexpressed in various tumor cells.In this study,the ELISA results of HSP90βcombined with CEA,CA125,and CYFRA21-1 were used to construct a recursive partitioning decision tree model to establish a four-protein diagnostic model and predict the survival of patients with lung cancer.Survival analysis showed that the recursive partitioning decision tree could distinguish the prognosis between high-and low-risk groups.Results suggested that the joint detection of HSP90β,CEA,CA125,and CYFRA21-1 in the peripheral blood of patients with lung cancer is plausible for early diagnosis and prognosis prediction of lung cancer.