Yabana Kiremit Birgül, Dikmetaş Yardan Elif
Department of Healthcare Management, Faculty of Health Sciences, Ondokuz Mayis University, Atakum, Samsun, 55200, Türkiye.
BMC Health Serv Res. 2025 Mar 27;25(1):446. doi: 10.1186/s12913-025-12623-x.
The time a patient spends in the hospital from admission to discharge is known as the length of stay (LOS). Predicting LOS is crucial for enhancing patient care, managing hospital resources, and optimizing the use of patient beds. Therefore, this study aimed to predict the LOS for patients hospitalized in various clinics using different artificial intelligence (AI) models.
The study analyzed 162,140 hospitalized patients aged 18 and older at various clinics of a university hospital in northern Türkiye from 2012 to 2020. Three soft computing methods-Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Multiple Linear Regression Analysis (MLR)-were employed to estimate LOS using inputs such as medical and imaging services (number of CT, USG, ECG, hemogram tests, medical biochemistry, and number of direct x-rays), demographic, and diagnostic data (patients' age, sex, season of hospitalization, type of hospitalization, diagnosis, and second diagnosis). The LOS predictions utilized single and double-hidden layer ANNs with various training algorithms (Levenberg-Marquardt-LM, Bayesian Regularization-BR and Scaled Conjugate Gradient-SCG) and activation functions (tangent-sigmoid, purelin), ANFIS with Grid Partitioning (ANFIS-GP), and MLR. Model performance was evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
Of the patients, 54% were male and 43.5% were treated in surgical clinics. The mean age was 55.1 years, with 32.9% of participants aged 65 years or older. Hospital stays were 2-7 days for 39.7% of patients, over 7 days for 30.9%, and 1 day for 29.4%. Neoplasm-related diagnoses (ICD codes) accounted for 25.1% of admissions. Variables influencing LOS were identified through feature selection from patients in various hospital wards. The most significant factors affecting LOS include second diagnosis, the number of hemogram tests, computerized tomography scans (CT), ultrasonography (USG), and direct X-rays. Utilizing these factors, 12 models with varied input variables were developed and analyzed. The double hidden layer ANN model with the Levenberg-Marquardt (LM) training algorithm outperformed the others, achieving R² values of 0.854 for training and 0.807 for the test dataset, with RMSE values of 2.397 days and 2.774 days and MAE values of 1.787 days and 1.994 days, respectively. Following ANN-LM, the best results were obtained with ANFIS-GP, while MLR exhibited the lowest performance.
Various AI models can effectively predict LOS for patients in different hospital units. Accurate LOS predictions can help health managers allocate resources more equitably across units.
患者从入院到出院在医院度过的时间称为住院时长（LOS）。预测住院时长对于改善患者护理、管理医院资源以及优化病床使用至关重要。因此，本研究旨在使用不同的人工智能（AI）模型预测各科室住院患者的住院时长。
该研究分析了2012年至2020年在土耳其北部一家大学医院各科室住院的162140名18岁及以上患者。采用三种软计算方法——人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）和多元线性回归分析（MLR）——利用医疗和影像服务（CT、超声、心电图、血常规检查、医学生物化学检查的数量以及直接X线检查的数量）、人口统计学和诊断数据（患者年龄、性别、住院季节、住院类型、诊断和二次诊断）等输入信息来估计住院时长。住院时长预测使用了具有不同训练算法（Levenberg-Marquardt-LM、贝叶斯正则化-BR和缩放共轭梯度-SCG）和激活函数（正切-西格蒙德函数、线性函数）的单隐藏层和双隐藏层人工神经网络、具有网格划分的自适应神经模糊推理系统（ANFIS-GP）以及多元线性回归分析。使用决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）评估模型性能。
患者中54%为男性，43.5%在外科科室接受治疗。平均年龄为55.1岁，32.9%的参与者年龄在65岁及以上。39.7%的患者住院时间为2至7天，30.9%超过7天，29.4%为1天。肿瘤相关诊断（国际疾病分类代码）占入院人数的25.1%。通过对各医院病房患者进行特征选择，确定了影响住院时长的变量。影响住院时长的最重要因素包括二次诊断、血常规检查次数、计算机断层扫描（CT）、超声检查（USG）和直接X线检查。利用这些因素，开发并分析了12个具有不同输入变量的模型。采用Levenberg-Marquardt（LM）训练算法的双隐藏层人工神经网络模型表现优于其他模型，训练数据集的R²值为0.854，测试数据集的R²值为0.807，RMSE值分别为2.397天和2.774天，MAE值分别为1.787天和1.994天。仅次于人工神经网络-LM的是ANFIS-GP取得了最佳结果，而多元线性回归分析表现最差。
各种人工智能模型可以有效地预测不同医院科室患者的住院时长。准确的住院时长预测有助于卫生管理人员在各科室更公平地分配资源。
