Automated Primary Hyperparathyroidism Screening with Neural Networks
This work addresses a critical healthcare issue for patients with PHPT by improving diagnostic accuracy, though it is incremental as it applies existing neural network methods to a specific medical domain.
The paper tackled the problem of screening for Primary Hyperparathyroidism (PHPT), which is often undiagnosed due to difficulties in interpreting blood test results, by proposing neural network models that achieved over 97% accuracy with common blood values and over 99% accuracy with additional lab tests, reducing false negatives by 99% compared to traditional methods.
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 the clinic data from the real world, in this work, we propose a novel approach to screening PHPT with neural network (NN) architecture, 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\%.