Management of imported malaria cases and healthcare institutions in central China, 2012–2017: application of decision tree analysis

Abstract Background Imported malaria has been an important challenge for China. Fatality rates from malaria increased in China, particularly in Henan Province, primarily due to malpractice and misdiagnoses in healthcare institutions, and the level of imported malaria. This study aims to investigate the relationship between the state of diagnosis and subsequent complications among imported malaria cases at healthcare institutions, based on malaria surveillance data in Henan Province from 2012 to 2017. Methods A retrospective descriptive analysis was performed using data from the Centre for Disease Control and Prevention, Zhengzhou City, the capital of Henan Province. A decision tree method was exploited to provide valuable insight into the correlation between imported malaria cases and healthcare institutions. Results From 2012 to 2017, there were 371 imported malaria cases, mostly in males aged between 20 and 50 years, including 319 Plasmodium falciparum cases. First visits of 32.3%, 19.9% and 15.9% malaria cases for treatment were to provincial, municipal and county healthcare institutions, respectively. The time interval between onset and initial diagnosis of 284 cases (76.5%) and the time interval between initial diagnosis and final diagnosis of 197 cases (53.1%) was no more than 72 h. An apparent trend was found that there were notably fewer patients misdiagnosed at first visit to healthcare institutions of a higher administrative level; 12.5% of cases were misdiagnosed in provincial healthcare institutions compared to 98.2% in private clinics, leading to fewer complications at healthcare institutions of higher administrative level due to correct initial diagnosis. In the tree model, the rank of healthcare facilities for initial diagnosis, and number of days between onset and initial diagnosis, made a major contribution to the classification of initial diagnosis, which subsequently became the most significant factor influencing complications developed in the second tree model. The classification accuracy were 82.2 and 74.1%, respectively for the tree models of initial diagnosis and complications developed. Conclusion Inadequate seeking medical care by imported malaria patients, and insufficient capacity to diagnose malaria by healthcare institutions of lower administrative level were identified as major factors influencing complications of imported malaria cases in Henan Province. The lack of connection between uncommon imported malaria cases and superior medical resources was found to be the crucial challenge. A web-based system combined with WeChat to target imported malaria cases was proposed to cope with the challenge.

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Efficient Management Strategy of Malaria Patients in China: A Decision-Tree Modelling Approach

10.21203/rs.3.rs-657912/v2 ◽

2021 ◽

Author(s):

Gang Li ◽

Donglan Zhang ◽

Zhuo Chen ◽

Da Feng ◽

Xinyan Cai ◽

...

Keyword(s):

Decision Tree ◽

Median Time ◽

Diagnostic Errors ◽

Initial Diagnosis ◽

Categorical Variables ◽

Time Interval ◽

Insurance Status ◽

Healthcare Facilities ◽

Health Insurance Status ◽

Control And Prevention

Abstract Background: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis, and preventing them from progressing to severe or critical state. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics, as well as the factors that affect the accuracy in the context of the agenda for a world free of malaria.Methods: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed.Results: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level, and provincial-level hospitals were 27.5% (n=136), 26.3% (n=130), and 8.3% (n=41), respectively; the proportions of patients seeking care in clinic, township health center, and Centers for Disease Control and Prevention were 25.9% (n=128), 4.1% (n=20), and 7.9% (n=39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% of patients had complications. The median time from onset to the first visit was 2 days (IQR: 0 - 3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0 - 4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age.Conclusion: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. Researchers and clinicians should consider strengthening the primary care facilities, time interval between onset and initial diagnosis, administrated residence, and health insurance status to reduce diagnostic errors.

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Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach

Malaria Journal ◽

10.1186/s12936-021-04006-4 ◽

2022 ◽

Vol 21 (1) ◽

Author(s):

Gang Li ◽

Donglan Zhang ◽

Zhuo Chen ◽

Da Feng ◽

Xinyan Cai ◽

...

Keyword(s):

Decision Tree ◽

Median Time ◽

Health Centre ◽

Diagnostic Errors ◽

Initial Diagnosis ◽

Categorical Variables ◽

Time Interval ◽

Insurance Status ◽

Healthcare Facilities ◽

Health Insurance Status

Abstract Background Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. Methods A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. Results Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. Conclusions Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status.

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A novel enhanced decision tree model for detecting chronic kidney disease

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-021-00302-w ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Avijit Kumar Chaudhuri ◽

Deepankar Sinha ◽

Dilip K. Banerjee ◽

Anirban Das

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Decision Tree ◽

Decision Tree Model ◽

Tree Model

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Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Algorithms ◽

10.3390/a14060176 ◽

2021 ◽

Vol 14 (6) ◽

pp. 176

Author(s):

Wei Zhu ◽

Xiaoyang Zeng

Keyword(s):

Decision Tree ◽

Adaptive Algorithms ◽

Memory Access ◽

Access Time ◽

Decision Tree Algorithm ◽

Verilog Hdl ◽

Tree Model ◽

Cache Associativity ◽

Cache Scheme ◽

Reconfigurable Cache

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

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Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China

Land ◽

10.3390/land10050471 ◽

2021 ◽

Vol 10 (5) ◽

pp. 471

Author(s):

Bo Mu ◽

Guohang Tian ◽

Gengyu Xin ◽

Miao Hu ◽

Panpan Yang ◽

...

Keyword(s):

Surface Water ◽

Nearest Neighbor ◽

Dynamic Change ◽

Henan Province ◽

Central China ◽

Node Degree ◽

Human Society ◽

Stepping Stones ◽

Dynamic Changes ◽

Graph Metrics

An understanding of the scientific layout of surface water space is crucial for the sustainable development of human society and the ecological environment. The objective of this study was to use land-use/land-cover data to identify the spatiotemporal dynamic change processes and the influencing factors over the past three decades in Henan Province, central China. Multidisciplinary theories (landscape ecology and graph theory) and methods (GIS spatial analysis and SPSS correlation analysis) were used to quantify the dynamic changes in surface water pattern and connectivity. Our results revealed that the water area decreased significantly during the periods of 1990–2000 and 2010–2018 due to a decrease in tidal flats and linear waters, but increased significantly in 2000–2010 due to an increase in patchy waters. Human construction activities, socioeconomic development and topography were the key factors driving the dynamics of water pattern and connectivity. The use of graph metrics (node degree, betweenness centrality, and delta probability of connectivity) in combination with landscape metrics (Euclidean nearest-neighbor distance) can help establish the parameters of threshold distance between connected habitats, identify hubs and stepping stones, and determine the relatively important water patches that require priority protection or development.

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Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽

10.3390/diagnostics11061094 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1094

Author(s):

Michael Wong ◽

Nikolaos Thanatsis ◽

Federica Nardelli ◽

Tejal Amin ◽

Davor Jurkovic

Keyword(s):

Decision Tree ◽

Expectant Management ◽

Decision Tree Model ◽

Decision Tree Analysis ◽

Focal Lesions ◽

Tree Model ◽

Normal Endometrium ◽

Endometrial Polyps ◽

Tree Analysis ◽

Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

International Journal of Endocrinology ◽

10.1155/2017/3894870 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Zhong Xin ◽

Lin Hua ◽

Xu-Hong Wang ◽

Dong Zhao ◽

Cai-Guo Yu ◽

...

Keyword(s):

Decision Tree ◽

Predictive Value ◽

Early Stage ◽

Current Model ◽

External Validation ◽

Area Under The Curve ◽

Decision Tree Model ◽

Tree Model ◽

Chinese Adult ◽

Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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