Augmentation of disease diagnosis and decision-making in healthcare with
machine learning algorithms is gaining much impetus in recent years. In
particular, in the current epidemiological situation caused by COVID-19
pandemic, swift and accurate prediction of disease diagnosis with machine
learning algorithms could facilitate identification and care of vulnerable
clusters of population, such as those having multi-morbidity conditions. In
order to build a useful disease diagnosis prediction system, advancement in
both data representation and development of machine learning architectures are
imperative. First, with respect to data collection and representation, we face
severe problems due to multitude of formats and lack of coherency prevalent in
Electronic Health Records (EHRs). This causes hindrance in extraction of
valuable information contained in EHRs. Currently, no universal global data
standard has been established. As a useful solution, we develop and publish a
Python package to transform public health dataset into an easy to access
universal format. This data transformation to an international health data
format facilitates researchers to easily combine EHR datasets with clinical
datasets of diverse formats. Second, machine learning algorithms that predict
multiple disease diagnosis categories simultaneously remain underdeveloped. We
propose two novel model architectures in this regard. First, DeepObserver,
which uses structured numerical data to predict the diagnosis categories and
second, ClinicalBERT_Multi, that incorporates rich information available in
clinical notes via natural language processing methods and also provides
interpretable visualizations to medical practitioners. We show that both models
can predict multiple diagnoses simultaneously with high accuracy.

机器学习在疾病诊断和决策方面的应用正变得越来越流行。本文开发了一种 Python 包，将公共卫生数据集转换为易于访问的通用格式，以便于构建有用的疾病诊断预测系统，并提出了两种新的模型架构进行疾病诊断多类别预测，一种使用结构化数字数据，另一种结合自然语言处理方法和可解释的可视化。这些模型都能够高准确度地预测多种诊断。