Ocampo Osorio Felipe, Pedroza Gomez Santiago, Rebellón Sanchez David Esteban, Ramirez Fernandez Richard, Tabares-Soto Reinel, Bravo-Ortíz Mario Alejandro, Cruz Suarez Gustavo Adolfo
Unidad de Inteligencia Artificial, Fundación Valle del Lili, Cali, Valle del Cauca, Colombia.
Centro de Investigaciones Clínicas, Fundación Valle del Lili, Cali, Valle del Cauca, Colombia.
PeerJ Comput Sci. 2025 Apr 22;11:e2762. doi: 10.7717/peerj-cs.2762. eCollection 2025.
The high prevalence of patient absenteeism in medical appointments poses significant challenges for healthcare providers and patients, causing delays in service delivery and increasing operational inefficiencies. Addressing this issue is crucial in the internal medicine department, a fundamental pillar of comprehensive adult healthcare that manages various chronic and complex conditions. To mitigate absenteeism, we present an innovative application of machine learning models specifically designed to predict the risk of patient absenteeism in the internal medicine department of Fundación Valle del Lili, a high-complexity hospital in Colombia. Leveraging an institutional database, we conducted a statistical analysis to identify critical variables influencing absenteeism risk, including clinical and sociodemographic factors and characteristics of previously attended appointments. Our study evaluated seven distinct machine learning models, explored various data processing techniques, and addressed class imbalance through oversampling and undersampling strategies. Hyperparameter optimization was conducted for each model configuration, culminating in selecting the Bagging RandomForest model, which demonstrated outstanding performance when combined with standardized data and balanced using the Synthetic Minority Oversampling Technique (SMOTE). Additionally, Shapley values (SHAP) were applied to enhance the interpretability of the model, enabling the identification of the most influential variables in predicting medical absenteeism, such as the number of previous absences, the day and month of the appointment, and diagnosed diseases. The selected model achieved a predictive accuracy of 84.80 ± 0.81%, an AUC value of 0.89, an F1-score of 84.75%, and a recall of 83.02% in cross-validation experiments. These results highlight the potential of our experimental approach to identify the most suitable model for proactively predicting patients at high risk of absenteeism, optimizing resource allocation, and improving the quality of medical care in internal medicine in the future. Our methodology provides a foundation for reducing operational inefficiencies and strengthening intervention strategies. This benefits healthcare providers and patients through more timely and effective care. Ultimately, this approach contributes to improving patient outcomes and institutional efficiency.
患者在医疗预约中缺勤率高，给医疗服务提供者和患者带来了重大挑战，导致服务提供延迟，运营效率低下。解决这一问题在内科部门至关重要，内科是成人综合医疗保健的基本支柱，负责管理各种慢性和复杂病症。为了减少缺勤情况，我们展示了机器学习模型的一种创新应用，该模型专门设计用于预测哥伦比亚高复杂性医院Fundación Valle del Lili内科患者的缺勤风险。利用机构数据库，我们进行了统计分析，以确定影响缺勤风险的关键变量，包括临床和社会人口学因素以及之前就诊预约的特征。我们的研究评估了七种不同的机器学习模型，探索了各种数据处理技术，并通过过采样和欠采样策略解决了类别不平衡问题。对每个模型配置进行了超参数优化，最终选择了Bagging随机森林模型，该模型在与标准化数据结合并使用合成少数过采样技术（SMOTE）进行平衡时表现出色。此外，应用了Shapley值（SHAP）来提高模型的可解释性，从而能够识别预测医疗缺勤中最具影响力的变量，如之前的缺勤次数、预约的日期和月份以及诊断出的疾病。在交叉验证实验中，所选模型的预测准确率为84.80±0.81%，AUC值为0.89，F1分数为84.75%，召回率为83.02%。这些结果凸显了我们实验方法的潜力，即识别最适合主动预测高缺勤风险患者的模型，优化资源分配，并在未来提高内科医疗质量。我们的方法为减少运营效率低下和加强干预策略提供了基础。这通过更及时有效的护理使医疗服务提供者和患者受益。最终，这种方法有助于改善患者预后和机构效率。
