Prediction of postoperative complications of pediatric cataract patients using data mining

Zhang, Kai; Liu, Xiyang; Jiang, Jiewei; Li, Wangting; Wang, Shuai; Liu, Lin; Zhou, Xiaohan; Wang, Liming

doi:10.1186/s12967-018-1758-2

Cited by 33 publications

(20 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For 5-fold cross validation and external validation, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC) curve of the DLAs were calculated. The number of true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) cases were counted, and the corresponding PPV and negative predictive value (NPV) were calculated 44,45 . The values can be obtained as follows:where, N and P are the numbers of negative samples and positive samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of deep learning algorithms for scoliosis screening using back images

et al. 2019

Self Cite

View full text Add to dashboard Cite

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5–5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure.

show abstract

Section: Methodsmentioning

confidence: 99%

Development and validation of deep learning algorithms for scoliosis screening using back images

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…To predict postoperative pneumonia, the following six different machine learning models were developed and evaluated for their performance: logistic regression (LR) [20], support vector machine (SVM) [21], random forest (RF) [22], MLP (multilayer perceptron) [23], extreme gradient boosting (XGBoost) [24], and gradient boosting machine (GBM) [25].…”

Section: Development Of Machine Learning Modelsmentioning

confidence: 99%

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Chen

Yang²,

Gao

et al. 2021

Respir Res

View full text Add to dashboard Cite

Background Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. Methods Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na+, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

show abstract

“…Jhang ve ark., yaptıkları çalışmada veri madenciliği birliktelik analizlerinden apriori algoritmasını kullanarak bunama teşhisi konulan hastalar ve onların bakım verenler için spesifik bunama alt tiplerine göre özel bakım ihtiyaçları kombinasyonlarını açıklamışlardır [12]. Zhang ve ark., çalışmalarında veri madenciliği teknikleri kullanarak pediatrik katarakt hastalarının postoperatif komplikasyonlarını tahmin etmeye çalışmaışlardır [13]. Yang ve ark., çalışmalarında apriori algoritmasına dayalı ilişkilendirme kuralı analizleri yaparak kolon karsinomunun tedavisinde bileşik kushen enjeksiyonunun kombine ilaç tedavisi arasındaki ilişkiyi doğrulamaya çalışmışlardır [14].…”

Section: İlgili çAlışmalarunclassified

Analysis of the Association Between Vitamin D Deficiency and Other Diagnoses of Patients by Data Mining Techniques

Kaya

2020

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

One of the most common disorders is vitamin D deficiency. Vitamin D deficiency in individuals with a vitamin D level of less than 20 ng / ml has been studied in previous studies, which is also associated with other diagnoses. The aim of this study was to investigate the association of vitamin D deficiency patients with other diagnoses. In this respect, a data set consisting of patients whose vitamin d levels have been measured in a public hospital for the last 3 years has been provided. Using this data set, the association rule analysis of data mining techniques was performed. Apriori algorithm was preferred in the analysis. As a result of the analysis, 22 association rules were determined and interpreted.

show abstract

Prediction of postoperative complications of pediatric cataract patients using data mining

Cited by 33 publications

References 48 publications

Development and validation of deep learning algorithms for scoliosis screening using back images

Development and validation of deep learning algorithms for scoliosis screening using back images

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Analysis of the Association Between Vitamin D Deficiency and Other Diagnoses of Patients by Data Mining Techniques

Contact Info

Product

Resources

About