Prof. Seong Eun Kim

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Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2020 in Journal of Korean Medical Science

Journal Article

Clinical Course and Outcomes of Patients with Severe Acute Respiratory Syndrome Coronavirus 2 Infection: a Preliminary Report of the First 28 Patients from the Korean Cohort Study on COVID-19

on behalf of the Korea National Committee for Clinical Management of COVID-19

+14

Kim ES, et al. J Korean Med Sci. 2020 Feb;35(13):e142. https://doi.org/10.3346/jkms.2020.35.e142

1 June 2020 in International Journal of Infectious Diseases

Journal Article

Viral kinetics of SARS-CoV-2 in asymptomatic carriers and presymptomatic patients

Seong Eun Kim

Hae Seong Jeong

Yohan Yu

Sung Un Shin

Soosung Kim

Kyung-Hwa Park

From 71 laboratory-confirmed cases, we found three presymptomatic patients and 10 entirely asymptomatic infections. In two of the three incubation period patients, the viral titer in the presymptomatic period was very high (Ct value 35 RT-PCR within 14 days after diagnosis. Patients who have COVID-19 may already be infectious before there are symptoms, and 14 days of isolation after diagnosis may be sufficient in entirely asymptomatic cases.

23 December 2020 in Journal of Medical Internet Research

Journal Article

An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model

Background: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.