For severely affected COVID-19 patients, it is crucial to identify high-risk
patients and predict survival and need for intensive care (ICU). Most of the
proposed models are not well reported making them less reproducible and prone
to high risk of bias particularly in presence of imbalance data/class. In this
study, the performances of nine machine and deep learning algorithms in
combination with two widely used feature selection methods were investigated to
predict last status representing mortality, ICU requirement, and ventilation
days. Fivefold cross-validation was used for training and validation purposes.
To minimize bias, the training and testing sets were split maintaining similar
distributions. Only 10 out of 122 features were found to be useful in
prediction modelling with Acute kidney injury during hospitalization feature
being the most important one. The algorithms performances depend on feature
numbers and data pre-processing techniques. LSTM performs the best in
predicting last status and ICU requirement with 90%, 92%, 86% and 95% accuracy,
sensitivity, specificity, and AUC respectively. DNN performs the best in
predicting Ventilation days with 88% accuracy. Considering all the factors and
limitations including absence of exact time point of clinical onset, LSTM with
carefully selected features can accurately predict last status and ICU
requirement. DNN performs the best in predicting Ventilation days. Appropriate
machine learning algorithm with carefully selected features and balance data
can accurately predict mortality, ICU requirement and ventilation support. Such
model can be very useful in emergency and pandemic where prompt and precise

针对 COVID-19 严重患者，本研究利用九种机器学习算法和两种特征选择方法进行预测模型建立，发现重症肾脏损伤是最重要的特征。在预测死亡、ICU 需求和通气天数方面，LSTM 算法表现最佳，准确率达到 90%，敏感性为 92%，特异性为 86%，AUC 为 95%。针对通气天数的预测，DNN 算法准确率达到 88%。综合各因素和局限性，选取合适的特征和数据平衡的机器学习算法可准确预测死亡、ICU 需求和通气支持，在紧急和疫情爆发情况下非常有用。

基于人工智能 (AI) 的新冠肺炎患者死亡预测

Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19  Patients

Clinical decision-making is a fundamental stage in delivering appropriate
care to patients. In recent years several decision-making systems designed to
aid the clinician in this process have been developed. However, technical
solutions currently in use are based on simple regression models and are only
able to take into account simple pre-defined multiple-choice features, such as
patient age, pre-existing conditions, smoker status, etc. One particular source
of patient data, that available decision-making systems are incapable of
processing is the collection of patient consultation GP notes. These contain
crucial signs and symptoms - the information used by clinicians in order to
make a final decision and direct the patient to the appropriate care.
Extracting information from GP notes is a technically challenging problem, as
they tend to include abbreviations, typos, and incomplete sentences.
This paper addresses this open challenge. We present a framework that
performs knowledge graph construction from raw GP medical notes written during
or after patient consultations. By relying on support phrases mined from the
SNOMED ontology, as well as predefined supported facts from values used in the
RECAP (REmote COVID-19 Assessment in Primary Care) patient risk prediction
tool, our graph generative framework is able to extract structured knowledge
graphs from the highly unstructured and inconsistent format that consultation
notes are written in. Our knowledge graphs include information about existing
patient symptoms, their duration, and their severity.
We apply our framework to consultation notes of COVID-19 patients in the UK
COVID-19 Clinical Assesment Servcie (CCAS) patient dataset. We provide a
quantitative evaluation of the performance of our framework, demonstrating that
our approach has better accuracy than traditional NLP methods when answering
questions about patients.

本文提出从患者诊断记录中构建知识图谱的框架，并在英国 COVID-19 临床评估服务（CCAS）患者数据集中对 COVID-19 患者的诊断笔记应用该框架，与传统的 NLP 方法相比，证明该方法在回答患者问题时具有更好的准确性。

RECAP-KG：从原始 GP 笔记中挖掘知识图谱，为初级医疗中的远程 COVID-19 评估提供支持

RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19  Assessment in Primary Care

This paper applies multiple machine learning (ML) algorithms to a dataset of
de-identified COVID-19 patients provided by the COVID-19 Research Database. The
dataset consists of 20,878 COVID-positive patients, among which 9,177 patients
died in the year 2020. This paper aims to understand and interpret the
association of socio-economic characteristics of patients with their mortality
instead of maximizing prediction accuracy. According to our analysis, a
patients households annual and disposable income, age, education, and
employment status significantly impacts a machine learning models prediction.
We also observe several individual patient data, which gives us insight into
how the feature values impact the prediction for that data point. This paper
analyzes the global and local interpretation of machine learning models on
socio-economic data of COVID patients.

本文运用多种机器学习算法对 COVID-19 患者数据库进行分析，以了解和解释患者的社会经济特征与其死亡率的关联性，并分析机器学习模型对 COVID 患者社会经济数据的全局和局部解释。