模拟发烧性疾病的差异诊断,使用模糊的认知地图
Okure Obot1, Anietie John2, Iberedem Udo1
1Department of Computer Science, University of Uyo, Uyo 520103, Nigeria.
Tropical medicine and infectious disease
|July 28, 2023
概括
一个新的模糊认知地图 (FCM) 模型有助于医生在低资源地区诊断发烧性疾病. 这种人工智能工具实现了87%的准确性,改善了医疗保健的可访问性,并降低了热带地区的死亡率.
科学领域:
- 医疗信息学 医疗信息学
- 计算医学是一种计算医学.
- 公共卫生 公共卫生
背景情况:
- 在低收入至中等收入国家,获得医疗保健的限制有助于高发病率和死亡率.
- 具有重叠症状的发烧性疾病对缺乏经验的临床医生构成诊断挑战.
研究的目的:
- 开发一个模糊认知图 (FCM) 模型,作为诊断发烧性疾病的决策支持工具.
- 改善资源有限的热带地区的诊断准确性和医疗保健可访问性.
主要方法:
- 从尼日利亚易发烧性疾病的地区收集了2465个数据集.
- 利用了60名医生的投入,其中10名专家权衡和模糊症状.
- 基于加权和模糊的症状数据生成了FCM模型.
主要成果:
- 在诊断11种类型的发烧性疾病时,FCM模型的平均准确率为87%.
- 该模型对伴随性疾病和疾病严重性的预测与医生诊断有很强的相关性.
- 该研究强调了人工智能在改善服务不足地区诊断能力方面的潜力.
结论:
- 开发的模糊认知图 (FCM) 模型是发烧性疾病诊断的有效决策支持工具.
- 这项技术可以显著提高诊断准确性和医疗资源有限的地区的患者的治疗结果.
- FCM方法提供了一个可扩展的解决方案,以应对世卫组织报告的医疗保健可访问性挑战.
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