Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.
Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.
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DOI：
10.2196/20545
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发表时间：
2021-02-19
影响因子：
7.4
通讯作者：
Jacobson NC
中科院分区：
文献类型：
--
作者：
Barr PJ;Ryan J;Jacobson NC
COVID-19病例在全球呈指数级增长;然而，其临床表型仍不清楚。自然语言处理（NLP）和机器学习方法可能会产生关键方法，以快速识别COVID-19高风险个体，并了解临床表现和表现的关键症状。由于迫切需要在负担过重的保健机构中治疗病人，关于这些症状的数据可能无法准确地综合到病人记录中。在这种情况下，临床医生可能会专注于记录广泛报告的症状，这些症状表明确诊为COVID-19，尽管代价是不经常报告的症状。虽然NLP解决方案可以在生成COVID-19临床表型方面发挥关键作用，但它们受到电子健康记录（EHR）数据的限制。一个全面的记录诊所访问是必要的录音可能是答案。门诊访视记录代表了患者报告症状的更全面记录。如果大规模完成，来自EHR的数据和诊所就诊记录的组合可以用于为NLP和机器学习模型提供动力，从而快速生成COVID-19的临床表型。我们建议从诊所就诊的音频或视频记录中生成一个管道，以建立一个考虑临床症状并预测COVID-19发病率的模型。凭借大量可用数据，我们相信可以快速开发预测模型，以促进对COVID-19高风险个体的准确筛查，并识别预测更严重感染风险更高的患者特征。如果临床接触记录和我们的NLP模型得到充分的完善，台式病毒学研究结果将更好地知情。虽然门诊记录不是治疗这一流行病的灵丹妙药，但它们是一种低成本的选择，具有许多潜在的好处，最近已开始探索。
COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required—audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.
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影响因子：
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