机器学习在急性胰腺炎诊疗中的应用与展望

Application and Prospects of Machine Learning in the Diagnosis and Treatment of Acute Pancreatitis

摘要: 急性胰腺炎(acute pancreatitis, AP)是胰腺的一种严重炎症，起病突然，伴有剧烈腹痛，并发严重的局部或全身并发症，发病率和死亡率高。过去几十年进行的广泛研究表明，症状发作后的前24小时对于识别有并发症或死亡风险的患者至关重要。传统的评分系统如Ranson、APACHE-II和BISAP评分等，提供了风险分层的基础方法，但往往这些评分在早期诊断、严重程度评估等方面存在局限性。机器学习(Machine Learning)作为人工智能的一个分支，凭借其对高维医学数据的深度挖掘与复杂模式识别能力，为AP诊疗流程的革新提供了新方向。本文旨在探讨机器学习在AP中的各种应用，重点涵盖了早期诊断、严重程度评估、并发症预测、治疗方案优化及预后评估等方面，分析在上述方面应用中存在的模型通用性差、临床应用困难等挑战，并对未来机器学习与大数据结合临床的发展进行展望，为AP的精准医疗提供一定的思路。

Abstract: Acute pancreatitis (AP) is a severe inflammation of the pancreas, characterized by sudden onset, intense abdominal pain, and serious local or systemic complications, resulting in high morbidity and mortality. Extensive research over the past few decades has demonstrated the critical importance of identifying patients at risk of complications or death within the first 24 hours of symptom onset. Traditional scoring systems such as the Ranson, APACHE-II, and BISAP scores provide fundamental methods for risk stratification, but these scores often have limitations in early diagnosis and severity assessment. Machine learning, as a branch of artificial intelligence, offers new directions for revolutionizing AP diagnosis and treatment processes due to its ability to deeply mine high-dimensional medical data and recognize complex patterns. This article aims to explore various applications of machine learning in AP, focusing on early diagnosis, severity assessment, complication prediction, treatment optimization, and prognostic evaluation. It analyzes the challenges in these applications, such as poor model universality and difficulties in clinical application, and provides a prospective outlook on the future development of machine learning combined with big data in clinical practice, offering some insights for precision medicine in AP.