Automated Primary Hyperparathyroidism Screening with Neural Networks
Automated Primary Hyperparathyroidism Screening with Neural Networks
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DOI：
10.1109/globecom46510.2021.9685376
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Noah Ziems;Shaoen Wu;J. Norman
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文献类型：
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作者：
Noah Ziems;Shaoen Wu;J. Norman
原发性甲状旁腺功能亢进症(PHPT)是一种相对常见的疾病，大约每1000名成年人中就有一人受到影响。然而，PHPT的筛查可能很困难，这意味着它经常在很长一段时间内被漏诊。虽然独立观察特定的血液测试结果可以帮助指示患者是否患有PHPT，但通常情况下，尽管患者患有PHPT，但这些血液结果水平都可以在各自的正常范围内。基于实际的临床数据，我们提出了一种新的基于神经网络结构的PHPT筛查方法，以普通血液值作为输入，准确率超过97%。此外，我们还提出了第二个模型，通过附加的实验室测试值作为输入，可以达到99%以上的准确率。此外，与传统的PHPT筛查方法相比，我们的神经网络模型可以将传统筛查方法的假阴性降低99%。
Primary Hyperparathyroidism(PHPT) is a relatively common disease, affecting about one in every 1,000 adults. However, screening for PHPT can be difficult, meaning it often goes undiagnosed for long periods of time. While looking at specific blood test results independently can help indicate whether a patient has PHPT, often these blood result levels can all be within their respective normal ranges despite the patient having PHPT. Based on clinical data from the real world, in this work, we propose a novel approach to screening PHPT with neural network (NN) architectures, achieving over 97% accuracy with common blood values as inputs. Further, we propose a second model achieving over 99% accuracy with additional lab test values as inputs. Moreover, compared to traditional PHPT screening methods, our NN models can reduce the false negatives of traditional screening methods by 99%.