基于概率分布差异的医学命名实体识别方法

doi:10.11959/j.issn.2096-0271.2023008

Abstract

Abstract:

With the improvement of data abilities and the development of emerging technologies, there are profound changes occurring in economic patterns and competitive structure of industries.In order to better respond to future opportunities and challenges, and to improve competitiveness of enterprises in new situations, it is necessary to understand and master the knowledge of digital transformation.The new competitive situation was discussed in which traditional enterprises would gradually be replaced by digital-transformed ones, digital transformation was differentiated from digitalization.Main challenges facing traditional enterprises while undergoing digital transformation were pinpointed, which were the lack of funds, talents, data and consciousness.A digital transformation service platform oriented to new competitive situation was proposed, which provided a feasible solution to enhancing enterprise competitiveness and conducting digital transformation.

Key words: digital transformation, emerging technologies, data asset, digital economy

CLC Number:

TP391

Cong LIU, Xuefeng LYU, Honglin WANG, Xiaowei WANG, Jin LU, Shun SUN, Songqi HU. Medical named entity recognition algorithm based on probability distribution difference[J]. Big Data Research, 2023, 9(4): 159-171.

Figures/Tables 13

References 29

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数据	标注结果
DR摄片（二次曝光）[右手正斜位]	[b:{[11,12,‘右手’],1}, c:{[13,15,’正斜位’],1}]
磁共振3.0T平扫	[b:{[],0},c:{[7,8,’平扫’],1}]
头颅神经外科移动CT平扫+三维（神外专用）	[b:{[0,1,”头颅”],1}, c:{[9,10,”平扫”],[12,13,”三维”],2}]

模型算法	轮次	批大小	初始学习率
ALBERT-base^[28]	10	32	5×10^-5
ALBERT-xxlarge^[28]	10	12	1×10^-5
ZEN^[29]	10	20	4×10^-5
BiLSTM-CRF^[15]	10	32	3×10^-4
本文方法	10	32	1×10^-4

模型算法	性能指标(F1值)	训练集样本数/条
ALBERT-base^[28]	58.5	15 000
ALBERT-xxlarge^[28]	61.7	15 000
ZEN^[29]	60.9	15 000
BiLSTM-CRF^[15]	57.6	15 000
本文方法	61.6	10 000

Medical named entity recognition algorithm based on probability distribution difference

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算法 1:基于概率分布差异的主动学习算法流程
输入：未标注的样本集U
Step1: 从U中随机抽取部分样本L，通过标注平台A，进行样本标注；
Step2: 构建实体识别BERT-BiLSTM-CRF模型M，使用现有标注样本训练模型M；
Step3: 通过采样策略P从未标注样本集合U中筛选出差异值较大的数据集合；
Step4: 通过标注平台A进行标注，得到标注样本集；
Step5: 更新标注样本集；
Step6: 基于更新的样本集L，更新采样策略函数P；
Step7: 基于更新的样本集L，更新训练模型M；
Step8: 将更新后的模型M在测试集中验证；
if 达到收敛的条件：停止迭代；
else：重复step3-step8；
输出：新增后的样本集L，最终训练的模型M