There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the SESCAM Healthcare Network (Castilla La-Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1st to March 29th, 2020. We extracted related clinical information upon diagnosis, progression and outcome for all COVID-19 cases, focusing in those requiring ICU admission. A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with a mean age of 58.2 and S.D. 19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39 C, (or >39 C without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care. Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.

中文翻译：

---

机器学习和自然语言处理的COVID-19患者ICU入院的临床特征和预后因素

关于正在进行的COVID-19大流行的发病和临床过程，仍然有许多未知数。我们结合经典流行病学方法，自然语言处理（NLP）和机器学习（用于预测建模）的组合来分析COVID-19患者的电子健康记录（EHR）。我们从2020年1月1日至3月29日具有可用EHR（1364924例患者）的整个人群中，探索了SESCAM Healthcare Network（西班牙卡斯蒂利亚拉曼恰）内EHR中的非结构化自由文本。我们在诊断后提取了相关的临床信息，所有COVID-19病例的进展和结果，重点在于需要ICU入院的患者。总共鉴定出10,504例临床或PCR确诊为COVID-19的患者，其中52.5％为男性，平均年龄为58.2和SD为19.7岁。入院后 最常见的症状是咳嗽，发烧和呼吸困难，但只有不到一半的情况。总体而言，有6％的住院患者需要入住ICU。使用机器学习，数据驱动的算法，我们确定了年龄，发烧和呼吸急促的结合是ICU入院的最简约预测指标：年龄小于56岁，无呼吸急促且温度<39 C（或> 39）的那些C，无呼吸裂纹），无ICU入院。相反，年龄在40到79岁之间的COVID-19患者如果在急诊就诊后出现呼吸急促并延误了对ER的访问，则很可能被ICU收治。我们的结果表明，容易获得的临床变量（年龄，发烧和呼吸急促伴/不伴呼吸道crack裂）的组合可预测哪些COVID-19患者需要ICU入院。