Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Random Forest Based Monitoring Approach to Detect Changes in Longitudinal Blood Pressure Data: A Retrospective Study
Uzunlamasına Kan Basıncı Verilerindeki Değişimleri Tespit Etmek İçin Rassal Orman Temelli Bir İzleme Yöntemi: Retrospektif Bir Çalışma
Hilmi FİDANa, Tuğba DÜBEKTAŞ CANBEKb, Eralp DOĞUa
aDepartment of Statistics, Muğla Sıtkı Koçman University Faculty of Science, Muğla, Türkiye
bDepartment of Internal Medicine, Muğla Sıtkı Koçman University Faculty of Medicine, Muğla, Türkiye
Turkiye Klinikleri J Biostat. 2022;14(3):158-67
doi: 10.5336/biostatic.2022-88966
Article Language: EN
Full Text
ABSTRACT
 Objective: High blood pressure is a serious health condition. Patients and clinicians need tools to early detect deviations from normal blood pressure. Adapting modern methods of monitoring to blood pressure monitoring (BPM) has been a favorable solution. This study focuses on adaptation of machine learning to BPM. The daily chrono-biological changes effect blood pressure and may lead to false alarms in an early detection system. We provide an approach that detects changes in blood pressure with limited baseline data. Material and Methods: Our approach uses random forest as an alternative to traditional process monitoring algorithms which test each data point compared to a baseline dataset. In addition, our method converts testing problem into a supervised learning problem using a sliding baseline. We used real data and synthetic data to show the potential of the proposed method for different types of hypertension: sisto-diastolic hypertension, isolated diastolic hypertension and white coat effect. Results: Our observations support that the method can detect various patterns such as sisto-diastolic hypertension, isolated diastolic hypertension and white coat effect successfully. Conclusion: We described the development of a machine learning based monitoring approach to early detect changes in blood pressure. The proposed method (1) requires relatively small baseline data, (2) can be adapted to realtime patient data, and (3) can detect various types of hypertension.

Keywords: Machine learning; blood pressure; process monitoring; random forest
ÖZET
 Amaç: Yüksek tansiyon ciddi bir sağlık durumudur. Hastalar ve klinisyenler, normal kan basıncından sapmaları erken tespit etmek için araçlara ihtiyaç duyarlar. Modern izleme yöntemlerini kan basıncı izlemesine [blood pressure monitoring (BPM)] uyarlamak olumlu bir çözüm olmuştur. Bu çalışma, makine öğreniminin BPM'ye uyarlanmasına odaklanmaktadır. Günlük krono-biyolojik değişiklikler kan basıncını etkiler ve erken tespit sisteminde yanlış alarmlara neden olabilir. Sınırlı temel verilerle kan basıncındaki değişiklikleri tespit eden bir yaklaşım sunuyoruz. Gereç ve Yöntemler: Yaklaşımımız, her veri noktasını bir temel veri kümesiyle karşılaştıran geleneksel süreç izleme algoritmalarına alternatif olarak rassal ormanı kullanır. Bunun yanında, bizim yöntemimiz, kayan bir taban çizgisi kullanarak test problemini denetimli bir öğrenme problemine dönüştürür. Farklı hipertansiyon türleri için önerilen yöntemin potansiyelini göstermek için gerçek veriler ve sentetik veriler kullandık: sisto-diyastolik hipertansiyon, izole diyastolik hipertansiyon ve beyaz önlük etkisi. Bulgular: Gözlemlerimiz, yöntemin sisto-diyastolik hipertansiyon, izole diyastolik hipertansiyon ve beyaz önlük etkisi gibi çeşitli paternleri başarılı bir şekilde saptayabildiğini desteklemektedir. Sonuç: Kan basıncındaki değişiklikleri erken tespit etmek için makine öğrenimine dayalı bir izleme yaklaşımının geliştirilmesini tanımladık. Önerilen yöntem; (1) nispeten küçük temel veriler gerektirir, (2) gerçek zamanlı hasta verilerine uyarlanabilir ve (3) çeşitli hipertansiyon türlerini tespit edebilir.

Anahtar Kelimeler: Makine öğrenmesi; kan basıncı; süreç izleme; rassal orman
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