一个机器学习模型来评估潜在的错误诊断登革热住院情况
Claudia Yang Santos1, Suely Tuboi1, Ariane de Jesus Lopes de Abreu2
1Takeda Pharmaceuticals Brazil, Av. das Nações Unidas 14401, São Paulo, SP, Brazil.
Heliyon
|June 14, 2023
概括
一个机器学习模型在巴西发现了可能被误诊的登革热住院病例. 该工具估计,2014-2020年公共卫生保健住院病人的3.4%可能是登革热,有助于资源分配.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 机器学习 机器学习
背景情况:
- 登革热与其他传染病共享症状,导致误诊.
- 严重的登革热疫情可能会给医疗保健系统带来压力.
- 准确的住院数据对于资源分配至关重要.
研究的目的:
- 开发一种机器学习模型来识别巴西错误诊断的登革热住院病例.
- 为了估计错误诊断的登革热病例的负担.
- 支持公共卫生决策.
主要方法:
- 利用了巴西公共卫生系统和国家气象研究所的数据.
- 开发了一套与住院水平相关的数据集.
- 评估随机森林,物流回归和支持矢量机算法,随机森林表现最好 (85%的准确性).
主要成果:
- 随机森林模型估计,在2014-2020年期间,3.4% (13,608) 的公共住院可能被误诊为登革热.
- 该模型在识别潜在的错误诊断方面表现出很高的性能.
结论:
- 机器学习可以有效地识别潜在的错误诊断登革热住院情况.
- 这些发现凸显了对登革热负担的严重低估.
- 该模型可以协助公共卫生官员进行资源规划和管理.
相关概念视频
Steps in Outbreak Investigation
155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Documentation of Nursing Diagnosis
1.3K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.3K


