论文标题
使用规定药物的自动ICD10预测的协作剩余学习者
Collaborative residual learners for automatic icd10 prediction using prescribed medications
论文作者
论文摘要
临床编码是一个管理过程，涉及将护理情节诊断数据转换为标准代码格式（例如ICD10）。它具有许多关键应用，例如计费和病因研究。由于数据稀疏性，数字卫生系统的互操作性低，现实生活诊断的复杂性以及ICD10代码空间的巨大尺寸，因此临床编码的自动化非常具有挑战性。相关工作由于依赖许多数据源，效率低下的建模和较不可概括的解决方案而遭受了低适用性。我们提出了一个新型的基于残差的模型，以自动预测仅使用处方数据的ICD10代码。 Maharaj Nakorn Chiang Mai医院的两个现实世界临床数据集（门诊和住院）进行了广泛的实验。我们获得了平均精度为0.71和0.57的多标签分类精度，F1得分的0.57和0.38和0.73和0.73和0.44的精度分别预测了住院和门诊数据集的主要诊断。
Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interoperability of digital health systems, complexity of real-life diagnosis coupled with the huge size of ICD10 code space. Related work suffer from low applicability due to reliance on many data sources, inefficient modelling and less generalizable solutions. We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. Extensive experiments were performed on two real-world clinical datasets (outpatient & inpatient) from Maharaj Nakorn Chiang Mai Hospital with real case-mix distributions. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
