Anemia is a prevalent medical condition that typically requires invasive
blood tests for diagnosis and monitoring. Electronic health records (EHRs) have
emerged as valuable data sources for numerous medical studies. EHR-based
hemoglobin level/anemia degree prediction is non-invasive and rapid but still
faces some challenges due to the fact that EHR data is typically an irregular
multivariate time series containing a significant number of missing values and
irregular time intervals. To address these issues, we introduce HgbNet, a
machine learning-based prediction model that emulates clinicians'
decision-making processes for hemoglobin level/anemia degree prediction. The
model incorporates a NanDense layer with a missing indicator to handle missing
values and employs attention mechanisms to account for both local irregularity
and global irregularity. We evaluate the proposed method using two real-world
datasets across two use cases. In our first use case, we predict hemoglobin
level/anemia degree at moment T+1 by utilizing records from moments prior to
T+1. In our second use case, we integrate all historical records with
additional selected test results at moment T+1 to predict hemoglobin
level/anemia degree at the same moment, T+1. HgbNet outperforms the best
baseline results across all datasets and use cases. These findings demonstrate
the feasibility of estimating hemoglobin levels and anemia degree from EHR
data, positioning HgbNet as an effective non-invasive anemia diagnosis solution
that could potentially enhance the quality of life for millions of affected
individuals worldwide. To our knowledge, HgbNet is the first machine learning
model leveraging EHR data for hemoglobin level/anemia degree prediction.

利用电子健康记录数据，我们引入了 HgbNet 预测模型用于血红蛋白水平和贫血程度的预测，该模型通过模拟临床医生的决策过程，利用缺失值处理机制和注意力机制来处理 EHR 数据中的缺失和时间间隔不规则问题，结果表明 HgbNet 优于基线模型，在贫血诊断方面具有潜在的非侵入性和提高生活质量的潜力。