用于癌症检测和筛查的医疗记录人工智能辅助数据挖掘

Amalie Dahl Haue, Jessica Xin Hjaltelin, Peter Christoffer Holm, Davide Placido, S⊘ren Brunak
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引用次数: 0

摘要

人工智能方法在电子病历中的应用为多模态数据的大规模分析铺平了道路。这种描述由数千个特征组成的深层表型的人口范围内的数据现在被用来创建数据驱动的算法,这反过来又导致了早期癌症检测和筛查方法的改进。其余的挑战包括建立对这些方法进行前瞻性测试的基础设施,评估数据偏差的方法,以及收集反映人群疾病异质性的足够大和多样化的数据集。本综述概述了旨在早期发现癌症的人工智能方法,包括关注的关键方面(例如,数据漂移问题——当基础卫生保健数据随时间变化时)、伦理方面以及高收入国家与低收入和中等收入国家在获得癌症筛查方面的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence-aided data mining of medical records for cancer detection and screening
The application of artificial intelligence methods to electronic patient records paves the way for large-scale analysis of multimodal data. Such population-wide data describing deep phenotypes composed of thousands of features are now being leveraged to create data-driven algorithms, which in turn has led to improved methods for early cancer detection and screening. Remaining challenges include establishment of infrastructures for prospective testing of such methods, ways to assess biases given the data, and gathering of sufficiently large and diverse datasets that reflect disease heterogeneities across populations. This Review provides an overview of artificial intelligence methods designed to detect cancer early, including key aspects of concern (eg, the problem of data drift—when the underlying health-care data change over time), ethical aspects, and discrepancies between access to cancer screening in high-income countries versus low-income and middle-income countries.
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