Dataset Information

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

ABSTRACT: Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

SUBMITTER: Dipaola F

PROVIDER: S-EPMC10322913 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Dipaola Franca F Gatti Mauro M Giaj Levra Alessandro A Menè Roberto R Shiffer Dana D Faccincani Roberto R Raouf Zainab Z Secchi Antonio A Rovere Querini Patrizia P Voza Antonio A Badalamenti Salvatore S Solbiati Monica M Costantino Giorgio G Savevski Victor V Furlan Raffaello R

Scientific reports 20230705 1

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2 ...[more]

PMID: 37407595

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Dataset Information

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Publications

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

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