使用机器学习方法提高输卵管异位妊娠的决策期望管理:一篇临床文章。

IF 2.8 2区 医学 Q1 OBSTETRICS & GYNECOLOGY
Liron Jurman, Karin Brisker, Raz Ruach Hasdai, Omer Weitzner, Yair Daykan, Zvi Klein, Ron Schonman, Yael Yagur
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引用次数: 0

摘要

目的:利用机器学习技术改进异位妊娠(EP)的预期治疗决策。方法:回顾性研究2014-2022年稳定型壶腹性EP患者的预期治疗。电子病历数据包括人口统计、病史、入院数据、超声检查结果和实验室结果。收集βhCG水平和成功率的随访数据。统计分析采用了决策树分类器,这是一种基于决策树的机器学习模型。队列被分为机器学习模型的训练组和测试组。对该模型的准确性、精密度、召回率和F1评分进行评估,以预测期望管理的成功。结果:878例EP患者中,预期治疗组221例,成功率为79.6%,20.4%需要后续干预。入院时平均βhCG水平为1056.8±1323.5 mIU。决策树分类器的准确率为89%,准确率、召回率和F1分数分别为92%、95%和94%。预测成功的因素包括临床症状,如疼痛、βhCG水平下降百分比、胎龄和决策日βhCG水平。中等影响的特征是白细胞计数、重力和最大输卵管尺寸。吸烟状况、从EP诊断到第二次βhCG测试的持续时间(小时)和婚姻状况是成功的最小显著预测因素。结论:基于决策树的分类器模型具有92%的准确率和95%的召回率,可能是预测血流动力学稳定的EP患者治疗成功的有价值的工具,特别是在βhCG随访的最初24小时内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing decision-making in tubal ectopic pregnancy using a machine learning approach to expectant management: a clinical article.

Objective: To refine decision-making regarding expectant management for ectopic pregnancy (EP) using machine learning.

Methods: This retrospective study addressed expectant management in stable patients with ampullar EP, 2014-2022. Electronic medical record data included demographics, medical history, admission data, sonographic findings, and laboratory results. Follow-up data on βhCG levels and success rates were collected. Statistical analysis incorporated a Decision Tree Classifier, a decision tree-based machine learning model. The cohort was divided into training and testing groups for the machine learning model. This model was evaluated for accuracy, precision, recall, and F1 score to predict success of expectant management.

Results: Among 878 cases of EP, the expectant management cohort, comprising 221 cases, exhibited a success rate of 79.6%, with 20.4% requiring subsequent intervention. Mean βhCG levels on admission were 1056.8 ± 1323.5 mIU. The Decision Tree Classifier demonstrated an accuracy of 89%, with precision, recall, and F1 scores of 92%, 95%, and 94%, respectively. Factors for predicting success included clinical symptoms such as pain, the percentage decrease in βhCG levels, gestational age and βhCG level at decision day. Moderate impactful features were white blood cell count, gravidity and maximum tubal dimensions. Smoking status, duration (hours) from time of EP diagnosis to second βhCG test and marital status were minimal significant predictors of success.

Conclusion: The Decision Tree-Based classifier model, with 92% precision and 95% recall, may be a valuable tool for predicting treatment success in hemodynamically stable patients with EP, particularly within the initial 24 h of βhCG follow-up.

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来源期刊
BMC Pregnancy and Childbirth
BMC Pregnancy and Childbirth OBSTETRICS & GYNECOLOGY-
CiteScore
4.90
自引率
6.50%
发文量
845
审稿时长
3-8 weeks
期刊介绍: BMC Pregnancy & Childbirth is an open access, peer-reviewed journal that considers articles on all aspects of pregnancy and childbirth. The journal welcomes submissions on the biomedical aspects of pregnancy, breastfeeding, labor, maternal health, maternity care, trends and sociological aspects of pregnancy and childbirth.
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