利用流行病学中的人工智能和软件工程方法,共同创建基于机械模型的决策支持工具
Sébastien Picault1, Guita Niang1, Vianney Sicard1
1Oniris, INRAE, BIOEPAR, 44300, Nantes, France.
Preventive veterinary medicine
|May 31, 2024
概括
人工智能 (AI) 和软件工程将复杂的流行病学模型转化为可访问的农场决策支持工具 (DST). 这增强了传染病控制,减少了农场对抗微生物的使用.
科学领域:
- 兽医流行病学 兽医流行病学
- 计算生物学 计算生物学
- 人工智能在农业中的应用
背景情况:
- 机械流行病学模型对于了解疾病传播和农场干预策略至关重要.
- 目前在将这些复杂的模型转化为农民和兽医的实用,及时的决策支持工具 (DST) 方面存在局限性.
- 牛呼吸道疾病 (BRD) 在肥料养殖场面临着重大挑战,往往导致广泛使用抗菌素.
研究的目的:
- 展示人工智能 (AI) 技术如何提高机械流行病学模型的可访问性和可用性.
- 根据机械模型,为农民和兽医开发用户友好的决策支持工具 (DST).
- 通过可访问的建模,改善传染病控制和优化畜牧业中抗菌剂的使用 (AMU).
主要方法:
- 借助知识表示方法,包括特定领域语言 (DSL),以高效地共同设计模型和DST.
- 利用人工智能和软件工程从现有机械模型自动生成基于Web的DST应用程序.
- 基于现有的机械模型开发了牛呼吸系统疾病 (BRD) 的DST,允许场景定制.
主要成果:
- 成功地将机械流行病学模型转化为用户友好的DST,使它们更容易被最终用户访问.
- 自动化Web应用程序生成大大简化了DST开发,使得专注于用户需求和结果.
- BRD DST使用户能够模拟特定农场的场景,比较干预影响,并将疾病控制与AMU减少平衡起来.
结论:
- 人工智能和软件工程提供了一种强大的方法,可以从兽医流行病学中的机械模型中共同创建有效的DST.
- 这种方法弥合了理论建模和实际现场应用之间的差距,增强了疾病管理.
- 开源管道促进了对动物健康的先进决策支持系统的更广泛采用和协作开发.
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