Intelligent Diagnosis and Treatment Model Based on Clinical Data for Home Rehabilitation Management of Chronic Obstructive Pulmonary Disease

Summary

A clinical data-driven model combining XGBoost and LSTM enhances home rehabilitation for COPD, improving data accuracy and prediction. It achieves 98% accuracy and 42.5% indicator improvement, addressing limitations of traditional hospital-based treatment.

Abstract

Traditional in-hospital rehabilitation treatment for chronic obstructive pulmonary disease (COPD) has problems such as resource shortage, high cost, and inconvenient transportation. To improve the convenience of rehabilitation treatment for COPD, a clinical data-driven intelligent diagnosis and treatment model is proposed for home rehabilitation management of COPD. The intelligent diagnosis and treatment model for home rehabilitation of COPD is optimized by combining the XGBoost algorithm and the long short-term memory network. The outcomes indicate that the XGBoost algorithm has the highest accuracy in processing clinical structured data, while the random forest (RF) algorithm has the lowest accuracy in processing clinical structured data. When the quantity of training samples is 300, the accuracy rates are 98% and 83%, respectively. The integration of the XGBoost algorithm and the long short-term memory network is used to process the monitoring indicators, resulting in the highest improvement rate and the smallest mean square error. When the sample size is 1000, the improvement rate of monitoring indicators is 42.5%, and the mean square error is 0.041. The method proposed in the study can effectively process clinical data, improve the accuracy of data processing, and accurately predict future changes in COPD.

Introduction

COPD is a common chronic disease with high disability and mortality rates, which significantly impacts patients' living standards. The traditional rehabilitation management model has problems such as information lag and untimely intervention in dealing with COPD. However, with the advancement of technology, some emerging technologies, such as the Extreme Gradient Boosting (XGBoost) algorithm, have brought new possibilities for the management of COPD¹. While XGBoost has demonstrated applicability across various health monitoring contexts, including in sports medicine, this study specifically leverages its strengths for managing COPD in a home re....

Protocol

This study was reviewed and approved by the Ethics Committee of Taizhou Cancer Hospital. Written informed consent was obtained from all participants before data collection, in accordance with institutional and national ethical standards. The reagents and the software used are listed in the Table of Materials.

1. Workflow overview

The end-to-end workflow for processing structured clinical data with XGBoost is shown in Figure 1 and includes feature ingestion, tree construction, residual updates, regularization, and evaluation. The preprocessing and seque....

Results

Sample-size stability and optimal performance
To explicitly demonstrate the stability threshold, we first present Table 1, followed by learning curves in Figure 3 and Figure 4. Table 1 reports the mean ± SD of accuracy, AUC, and MSE across increasing training sizes for the XGBoost classifier on structured data and for the LSTM on multivariate time series. As shown in Fig.......

Discussion

Summary of principal findings
COPD imposes a substantial clinical and societal burden through impaired breathing, reduced mobility, and recurrent acute exacerbations¹⁰. Leveraging continuous home monitoring with explainable analytics can mitigate this burden by enabling earlier detection and targeted intervention. In this study, a hybrid workflow integrating XGBoost for structured clinical variables and LSTM for multivariate time-series signals achieved consistently strong p.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA	NVIDIA	11.6	A parallel computing platform and programming model used to speed up computations on GPUs.
cuDNN	NVIDIA	8.2	A GPU-accelerated library for deep neural networks to optimize model training performance.
ECG Machine	Philips	PageWriter TC70	Used to validate heart rate measurements from the wearable device.
GPU	NVIDIA	RTX 3080	High-performance GPU used for accelerating model training on large datasets.
LSTM Model	-	-	Long Short-Term Memory neural network used for time-series data processing.
Pulse Oximeter	Masimo	Rad-97	Used to measure blood oxygen saturation (SpO2) and validate the accuracy of the wearable device.
Python	Python Software Foundation	3.10.6	Programming language used for data processing, model training, and evaluation.
TensorFlow	Google	2.13	Software library used for training the LSTM model and performing neural network computations.
Wearable Activity Tracker	Fitbit	Charge 5	Used for tracking steps and physical activity levels.
XGBoost	-	-	Software library used for gradient boosting (machine learning).