相关实验视频

Updated: May 28, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

机器学习算法对国际疾病分类自动化 (ICD) -10编码在马来西亚死亡记录中的比较分析

Muhammad Naufal B Nordin¹, Vivek J Jayaraj², Muhd Zulfadli Hafiz Ismail²

¹Health Informatics Centre, Ministry of Health Malaysia, Putrajaya, MYS.

|February 13, 2025

概括

像BERT和Gzip+KNN这样的机器学习模型可以提高国际疾病分类 (ICD) -10对死亡率数据的编码精度. 这些算法通过高效地结构化非结构化文本来增强公共卫生决策.

科学领域:

医疗信息学医疗信息学
机器学习应用机器学习应用
公共卫生数据分析

背景情况:

将非结构化文本数据自动翻译成结构化国际疾病分类 (ICD) -10代码对于提高死亡率数据的准确性和可靠性至关重要.
准确的死亡率数据对于基于信息的公共卫生决策和资源分配至关重要.

研究的目的:

探索和识别能够自动化将非结构化文本数据翻译成结构化的ICD-10代码的机器学习 (ML) 算法.
通过自动化编码,提高死亡率数据的准确性和可靠性,用于公共卫生决策.

主要方法:

来自马来西亚的387,650份匿名死亡记录 (2017年1月至2022年6月) 的分析,使用ICD-10编码.
评估包括BERT,Gzip+KNN,XGBoost,TensorFlow,SVM和Naive Bayes在内的ML模型,用于数据预处理和80:20训练测试分割后的自动ICD-10编码.
模型性能使用准确度,F1得分,精度,回忆和特异性进行评估,并对不平衡数据进行敏感性分析.

主要成果:

BERT获得了最高的F1得分 (0.71) 和精度 (0.82),紧随其后的是Gzip+KNN.
Gzip+KNN在精度回忆分析和不平衡数据集上一致的准确性方面表现出优越性.
在回忆方面,TensorFlow表现出色,而SVM表现出最高的特异性; XGBoost在所有指标中表现不那么有效.

结论:

伯特和Gzip+KNN是有效的ML模型,可以优化ICD-10编码,平衡效率,资源使用和准确性.
伯特提供了高精度,但需要更多的内存,而Gzip+KNN提供了强大的精度和回忆.
这些ML模型在推进医疗分析和改善公共卫生决策方面具有重大潜力.

关键词:

人工智能是一种人工智能.自动化自动化自动化自动化医疗信息学健康信息学疾病的国际分类.自然语言处理自然语言处理.文本数据分析数据分析

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

相关概念视频

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...