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Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers
المؤلفون المشاركون
Pieszko, Konrad
Hiczkiewicz, Jarosław
Budzianowski, Paweł
Budzianowski, Jan
Rzeźniczak, Janusz
Pieszko, Karolina
Burchardt, Paweł
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-01-30
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Introduction.
Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome.
The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before.
Objective.
We aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers.
Patients and Methods.
We obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period.
The time of follow-up ranged from 12 to 72 months.
A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission.
Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset.
Results.
For in-hospital mortality, our model achieved a c-statistic of 0.89 while the GRACE score 2.0 achieved 0.90.
For six-month mortality, the results of our model and the GRACE score on the test set were 0.77 and 0.73, respectively.
Red cell distribution width (HR 1.23; 95% CL 1.16-1.30; P<0.001) and neutrophil to lymphocyte ratio (HR 1.08; 95% CL 1.05-1.10; P<0.001) showed independent association with all-cause mortality in multivariable Cox regression.
Conclusions.
Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome.
A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Pieszko, Konrad& Hiczkiewicz, Jarosław& Budzianowski, Paweł& Budzianowski, Jan& Rzeźniczak, Janusz& Pieszko, Karolina…[et al.]. 2019. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Pieszko, Konrad…[et al.]. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Pieszko, Konrad& Hiczkiewicz, Jarosław& Budzianowski, Paweł& Budzianowski, Jan& Rzeźniczak, Janusz& Pieszko, Karolina…[et al.]. Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers. Disease Markers. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1147948
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1147948
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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