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

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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Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

10.20944/preprints201802.0087.v1 ◽

2018 ◽

Author(s):

KeumJi Kim ◽

SeongHwan Yoon

Keyword(s):

At Risk ◽

Decision Tree ◽

South Korea ◽

Natural Disasters ◽

Building Damage ◽

Decision Tree Model ◽

Decision Tree Analysis ◽

Tree Model ◽

Tree Analysis ◽

Damage Risk

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

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Predicting peri‐implant disease: Chi‐square automatic interaction detection (CHAID) decision tree analysis of risk indicators

Journal of Periodontology ◽

10.1002/jper.17-0501 ◽

2019 ◽

Vol 90 (8) ◽

pp. 834-846 ◽

Cited By ~ 9

Author(s):

Momen A. Atieh ◽

Ju Keat Pang ◽

Kylie Lian ◽

Stephanie Wong ◽

Andrew Tawse‐Smith ◽

...

Keyword(s):

Decision Tree ◽

Risk Indicators ◽

Decision Tree Analysis ◽

Chi Square ◽

Tree Analysis ◽

Interaction Detection

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Expectant management of tubal ectopic pregnancy: prediction of successful outcome using decision tree analysis

Ultrasound in Obstetrics and Gynecology ◽

10.1002/uog.1061 ◽

2004 ◽

Vol 23 (6) ◽

pp. 552-556 ◽

Cited By ~ 75

Author(s):

J. Elson ◽

A. Tailor ◽

S. Banerjee ◽

R. Salim ◽

K. Hillaby ◽

...

Keyword(s):

Decision Tree ◽

Ectopic Pregnancy ◽

Expectant Management ◽

Successful Outcome ◽

Decision Tree Analysis ◽

Tubal Ectopic Pregnancy ◽

Tree Analysis

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Antibiotic Discontinuation 24 h After Neonatal Late-Onset Sepsis Work-Up—A Validated Decision Tree Model

Frontiers in Pediatrics ◽

10.3389/fped.2021.693882 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ori Goldberg ◽

Nir Sokolover ◽

Ruben Bromiker ◽

Nofar Amitai ◽

Gabriel Chodick ◽

...

Keyword(s):

Blood Culture ◽

Decision Tree ◽

Late Onset ◽

Blood Cultures ◽

Decision Tree Model ◽

Study Cohort ◽

Tree Model ◽

Interaction Detection ◽

Late Onset Sepsis ◽

Work Up

Objectives: Neonatal late-onset sepsis work-up is a frequent occurrence in every neonatal department. Blood cultures are the diagnostic gold standard, however, a negative culture prior to 48–72 h is often considered insufficient to exclude sepsis. We aimed to develop a decision tree which would enable exclusion of late-onset sepsis within 24 h using clinical and laboratory variables.Study Design: Infants evaluated for late-onset sepsis during the years 2016–2019, without major malformations, in a tertiary neonatal center were eligible for inclusion. Blood cultures and clinical and laboratory data were extracted at 0 and 24 h after sepsis work-up. Infants with bacteriologically confirmed late-onset sepsis were compared to matched control infants. Univariate logistic regression identified potential risk factors. A decision tree based on Chi-square automatic interaction detection methodology was developed and validated.Results: The study cohort was divided to a development cohort (105 patients) and a validation cohort (60 patients). At 24 h after initial evaluation, the best variables to identify sepsis were C-reactive protein > 0.75 mg/dl, neutrophil-to-lymphocyte ratio > 1.5 and sick-appearance at 24 h. Use of these 3 variables together with blood culture status at 24 h, enabled identification of all infants that eventually developed sepsis through the decision tree model. Our decision tree has an area under the receiver operating characteristic curve of 0.94 (95% CI: 0.90–0.98).Conclusions: In non-sick appearing infants with a negative blood culture at 24 h and normal laboratory values, sepsis is highly unlikely and discontinuing antibiotics after 24 h is a viable option.

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