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Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms
المؤلفون المشاركون
El-Faramawi, Mohammed
Orloff, Mohammed S.
Delongchamp, Robert
Thapa, Susan
Fischbach, Lori A.
المصدر
Gastroenterology Research and Practice
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-04-01
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Background.
Gastric cancer is the fourth most common cancer and the third most common cause of cancer deaths worldwide.
Morbidity and mortality from gastric cancer may be decreased by identification of those that are at high risk for progression in the gastric precancerous process so that they can be monitored over time for early detection and implementation of preventive strategies.
Method.
Using machine learning, we developed prediction models for gastric precancerous progression in a population from a developing country with a high rate of gastric cancer who underwent gastroscopies for dyspeptic symptoms.
In the data imputed for completeness, we divided the data into a training and a validation test set.
Using the training set, we used the random forest method to rank potential predictors based on their predictive importance.
Using predictors identified by the random forest method, we conducted best subset linear regressions with the leave-one-out cross-validation approach to select predictors for overall progression and progression to dysplasia or cancer.
We validated the models in the test set using leave-one-out cross-validation.
Results.
We observed for all models that complete intestinal metaplasia and incomplete intestinal metaplasia were the strongest predictors for further progression in the precancerous process.
We also observed that a diagnosis of no gastritis, superficial gastritis, or antral diffuse gastritis at baseline was a predictor of no progression in the gastric precancerous process.
The sensitivities and specificities were 86% and 79% for the general model and 100% and 82% for the location-specific model, respectively.
Conclusion.
We developed prediction models to identify gastroscopy patients that are more likely to progress in the gastric precancerous process, among whom routine follow-up gastroscopies can be targeted to prevent gastric cancer.
Future external validation is needed.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Thapa, Susan& Fischbach, Lori A.& Delongchamp, Robert& El-Faramawi, Mohammed& Orloff, Mohammed S.. 2019. Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms. Gastroenterology Research and Practice،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1155504
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Thapa, Susan…[et al.]. Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms. Gastroenterology Research and Practice No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1155504
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Thapa, Susan& Fischbach, Lori A.& Delongchamp, Robert& El-Faramawi, Mohammed& Orloff, Mohammed S.. Using Machine Learning to Predict Progression in the Gastric Precancerous Process in a Population from a Developing Country Who Underwent a Gastroscopy for Dyspeptic Symptoms. Gastroenterology Research and Practice. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1155504
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1155504
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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