开发基于规则的自动算法,从电子健康记录中检测卵巢癌复发。

IF 3.3 Q2 ONCOLOGY
Sanghee Lee, Ji Hyun Kim, Hyeong In Ha, Myong Cheol Lim, Hyunsoon Cho
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引用次数: 0

摘要

目的:由于电子健康记录(EHR)中没有明确记录癌症复发的起始时间,因此需要大量的人工病历审查来检测癌症复发。本研究旨在开发一种基于规则的自动算法,以最小化预处理的电子病历数据为基础检测卵巢癌(OC)复发:方法:基于规则的复发自动检测算法(Auto-Recur)利用图像阅读笔记(正电子发射断层扫描-计算机断层扫描[PET-CT]、CT、磁共振成像[MRI])、生物标志物(CA125)和治疗信息(手术、化疗、放疗)来检测首次卵巢癌复发。自动复发包含三种单一算法(图像、生物标志物、治疗)和混合算法(单一算法的组合)。通过检测复发时间的敏感性、特异性和准确性来评估自动复发的性能。对无复发生存概率进行了估算,并与回顾性病历审查结果进行了比较:结果:提议的自动复发大大减少了人力资源和时间;与传统的回顾性病历审查相比,如果按 10 万名患者计算,自动复发可节省约 1340 天。基于图像、生物标志物和治疗信息组合的混合算法效率最高(灵敏度:93.4%,特异性:97.4%),并能精确捕捉复发时间(平均时间误差:8.5 天)。估计的 3 年无复发生存概率(44%)与回顾性病历审查的估计值(45%,对数秩 P 值 = .894)接近:结论:我们基于规则的算法有效捕捉了大规模电子病历中的首次 OC 复发情况,同时与传统回顾性病历审查所获得的无复发生存概率非常接近。研究结果有助于大规模电子病历分析,增加临床研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.

Purpose: As the onset of cancer recurrence is not explicitly recorded in the electronic health record (EHR), a high volume of manual chart review is required to detect the cancer recurrence. This study aims to develop an automatic rule-based algorithm for detecting ovarian cancer (OC) recurrence on the basis of minimally preprocessed EHR data.

Methods: The automatic rule-based recurrence detection algorithm (Auto-Recur), using notes on image reading (positron emission tomography-computed tomography [PET-CT], CT, magnetic resonance imaging [MRI]), biomarker (CA125), and treatment information (surgery, chemotherapy, radiotherapy), was developed to detect the first OC recurrence. Auto-Recur contains three single algorithms (images, biomarkers, treatments) and hybrid algorithms (combinations of the single algorithms). The performance of Auto-Recur was assessed using sensitivity, specificity, and accuracy of the recurrence time detected. The recurrence-free survival probabilities were estimated and compared with the retrospective chart review results.

Results: The proposed Auto-Recur considerably reduced human resources and time; it saved approximately 1,340 days when scaled to 100,000 patients compared with the conventional retrospective chart review. The hybrid algorithm on the basis of a combination of image, biomarker, and treatment information was the most efficient (sensitivity: 93.4%, specificity: 97.4%) and precisely captured recurrence time (average time error: 8.5 days). The estimated 3-year recurrence-free survival probability (44%) was close to the estimates by the retrospective chart review (45%, log-rank P value = .894).

Conclusion: Our rule-based algorithm effectively captured the first OC recurrence from large-scale EHR while closely approximating the recurrence-free survival estimates obtained by conventional retrospective chart reviews. The study findings facilitate large-scale EHR analysis, enhancing clinical research opportunities.

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来源期刊
CiteScore
6.20
自引率
4.80%
发文量
190
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