Development of a care plan recommendation system for physical therapy patients

Öz

Physical therapy includes many different treatment methods for patients' musculoskeletal problems. However, each patient has different complaints, lifestyles, and recovery rates. The importance of personalized care in physical therapy practices has therefore increased. The aim of this study is to develop a personalized recommendation system for patients' physical therapy processes. A dataset of 12,393 patients was collected from a private hospital and analyzed using machine learning and deep learning algorithms. Prediction was performed with Multi-Layer Perceptron (MLP) Classifier, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) models using 5-fold cross-validation. Among single models, the ANN achieved the best performance with Precision, Recall, F1-Score, and Accuracy values of 0.8733, 0.8666, 0.8600, and 0.8666, respectively. In the ensemble stage, a Voting Classifier combining Random Forest and LightGBM achieved 96% accuracy for predicting treatment session numbers, while the stacked-ensemble pipeline integrating ANN and CNN achieved 86% success in personalized treatment plan recommendation. The novelty of this study lies in the design of a stacked ensemble pipeline that integrates treatment session prediction with personalized care plan recommendation, enhancing both effectiveness and patient-centeredness in physical therapy. Each patient can thus receive the most appropriate treatment for their health status and needs. Personalized care can lead to faster recovery, greater participation in treatment processes, and more positive long-term outcomes. This study contributes to making physical therapy more effective and patient-centered by emphasizing the importance of personalized care in healthcare.

Anahtar Kelimeler

• Artificial Intelligence
• Deep Learning
• Physical Therapy
• Clinical Decision Support

Fizik tedavi hastalari için bir bakim plani öneri sisteminin geliştirilmesi

Öz

Fizik tedavi, hastaların kas-iskelet sistemi sorunları için birçok farklı tedavi yöntemini içerir. Ancak her hastanın şikayetleri, yaşam tarzları ve iyileşme oranları farklıdır. Bu nedenle, fizik tedavi uygulamalarında kişiselleştirilmiş bakımın önemi artmıştır. Bu çalışmanın amacı, hastaların fizik tedavi süreçleri için kişiselleştirilmiş bir öneri sistemi geliştirmektir. Özel bir hastaneden 12.393 hastadan oluşan bir veri seti toplanmış ve makine öğrenimi ve derin öğrenme algoritmaları kullanılarak analiz edilmiştir. Tahmin, 5 katlı çapraz doğrulama kullanılarak Çok Katmanlı Algılayıcı (MLP) Sınıflandırıcı, Yapay Sinir Ağı (YSA), Evrişimli Sinir Ağı (CNN) ve İleri Beslemeli Sinir Ağı (FNN) modelleri ile gerçekleştirilmiştir. Tek modeller arasında YSA, sırasıyla 0,8733, 0,8666, 0,8600 ve 0,8666'lık Hassasiyet, Geri Çağırma, F1 Puanı ve Doğruluk değerleriyle en iyi performansı elde etmiştir. Topluluk aşamasında, Random Forest ve LightGBM'yi birleştiren bir Oylama Sınıflandırıcısı, tedavi seansı sayılarını tahmin etmede %96 doğruluk oranına ulaşırken, ANN ve CNN'i entegre eden yığılmış topluluk hattı, kişiselleştirilmiş tedavi planı önerisinde %86 başarı elde etti. Bu çalışmanın yeniliği, tedavi seansı tahminini kişiselleştirilmiş bakım planı önerisiyle entegre eden ve fizyoterapide hem etkinliği hem de hasta merkezliliği artıran bir yığılmış topluluk hattının tasarımında yatmaktadır. Böylece her hasta, sağlık durumu ve ihtiyaçlarına en uygun tedaviyi alabilir. Kişiselleştirilmiş bakım, daha hızlı iyileşmeye, tedavi süreçlerine daha fazla katılıma ve daha olumlu uzun vadeli sonuçlara yol açabilir. Bu çalışma, sağlık hizmetlerinde kişiselleştirilmiş bakımın önemini vurgulayarak fizyoterapiyi daha etkili ve hasta merkezli hale getirmeye katkıda bulunmaktadır.

Anahtar Kelimeler

• Yapay Zeka
• Derin Öğrenme Fizik Tedavi
• Klinik Karar Desteği

Kaynakça

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