多源異構(gòu)醫(yī)學(xué)數(shù)據(jù)的集成和學(xué)習(xí)

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摘要：隨著人工智能技術(shù)的發(fā)展，人工智能技術(shù)越來(lái)越多地應(yīng)用在醫(yī)學(xué)任務(wù)中。由于醫(yī)學(xué)數(shù)據(jù)來(lái)源和結(jié)構(gòu)上的多樣性，導(dǎo)致其具有多源異構(gòu)的特點(diǎn)。本文將系統(tǒng)性介紹多數(shù)據(jù)集成和學(xué)習(xí)方法，包括基于數(shù)據(jù)倉(cāng)庫(kù)、聯(lián)邦數(shù)據(jù)庫(kù)、中間件的數(shù)據(jù)集成方法，以及聯(lián)邦學(xué)習(xí)、噪聲數(shù)據(jù)學(xué)習(xí)、特征融合學(xué)習(xí)等對(duì)多源異構(gòu)數(shù)據(jù)的學(xué)習(xí)方法。

關(guān)鍵詞：人工智能 ; 多源異構(gòu)數(shù)據(jù) ; 集成和學(xué)習(xí)

中圖分類(lèi)號(hào)：R3; TP8 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-3718.2022.06.00.08

Integration and learning of multi-source heterogeneous data

LI Yinhao3#， LI Chengquan2#， LIU Linwei3， WANG Xia1*， XIAO Li3，4*

（1. School of Clinical Medicine， Tsinghua University， Beijing 100084; 2. School of Medicine， Tsinghua University，Beijing 100084; 3. Institute of Computing Technology， Chinese Academy of Sciences，Cas Key Laboratory of Intelligent Information Processing， Beijing 100090; 4. Savaid Medical school， University of Chinese Academy of SciencesDisciplinary Development Centre， Ningbo，Jiangsu 305010，China）

Abstract： With the development of artificial intelligence technology， artificial intelligence technology is more and more used in medical tasks. Due to the diversity of medical data sources and structures， it has the characteristics of multi-source heterogeneity. This paper will systematically introduce multi data integration and learning methods， including data integration methods based on data warehouse， federated database and middleware， as well as learning methods for multi-source heterogeneous data， such as federated learning， noisy data learning and feature fusion learning.

Keywords： artificial intelligence; multi-source heterogeneous; data integration and learning

隨著我國(guó)醫(yī)療水平的進(jìn)步，越來(lái)越多的醫(yī)院開(kāi)始引進(jìn)數(shù)字化信息系統(tǒng)，如醫(yī)院信息系統(tǒng)（HIS）、醫(yī)學(xué)影像歸檔和通信系統(tǒng)（PACS）、檢驗(yàn)信息系統(tǒng)（LIS）等逐漸被醫(yī)院采用[1]。（剩余17910字）