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人工神经网络用于预测全甲状腺切除术后的术后低血症

Karthik Nagaraja Rao¹, Ripudaman Arora², Renu Rajguru²

¹Principal Consultant, Head and Neck Oncology, Sri Shankara Cancer Hospital and Research Center, Bangalore, India.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India

|August 12, 2024

概括

人工神经网络准确地预测手术后的低血症和完全甲状腺切除术后的严重程度. 该模型强调完整的副激素水平作为关键预测因素,强调其临床实用性.

科学领域:

内分泌学和外科瘤学.
医疗人工智能医疗人工智能
临床决策支持系统临床决策支持系统

背景情况:

手术后的低血是完全甲状腺切除术后的一个常见并发症.
准确预测低血症及其严重程度对于患者管理至关重要.
当前的预测方法可能缺乏准确性和全面的因素分析.

研究的目的:

开发和验证一个人工神经网络 (ANN) 模型,用于预测手术后的低血症和完全甲状腺切除术后的严重程度.
为了确定各种因素在预测低血症中的相对重要性,使用ANN.

主要方法:

一个追溯的病例系列,包括196名接受全甲状腺切除术的患者.
多层感知器 (MLP) 人工神经网络 (ANN) 用于预测和验证.
多变量分析以评估预测因素权重和构造有效性.

主要成果:

该ANN模型实现了高准确性,预测了96.3%的测试样本的低血症和92.8%的严重程度.
错误预测的平均值很低 (3.18%的培训,3.66%的测试).
完整的偏激素 (iPTH) 水平被确定为最重要的预测因素 (100%的权重),其次是需要进行胸腔切除 (28.55%).

结论:

ANN模型,特别是MLP,显示出在完全甲状腺切除术后准确预测术后低血症和严重性的巨大潜力.
iPTH水平是预测甲状腺切除术后低血症的一个关键因素.
虽然有希望,但ANN模型应该补充,而不是取代临床专业知识.

关键词:

人工智能的人工智能是人工智能.过低血症的情况.神经网络的神经网络副荷尔蒙是一种副荷尔蒙.甲状腺切除术 (thyroidectomy) 是一种切除甲状腺的方法.

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