Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables
| Autor: | Lourdes Arriaga-Pizano, Ron Leder, Eduardo Ferat-Osorio, Ana Gabriela Gallardo-Hernández, Cristina Revilla-Monsalve, J. Escobar, Graciela Libier Cabrera-Rivera, José Israel León-Pedroza, Ana Luisa Hernández-Pérez, Marcos A. Gonzalez-Olvera, Constatino Lopez-Macias, Uriel Guadarrama-Aranda |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Vital signs Pilot Projects Health Informatics law.invention Sepsis Artificial Intelligence law Intensive care Health care medicine Humans Intensive care medicine Retrospective Studies Artificial neural network business.industry medicine.disease Intensive care unit Computer Science Applications Intensive Care Units Cross-Sectional Studies Cohort Neural Networks Computer business Software Test data |
| Zdroj: | Computer Methods and Programs in Biomedicine. 210:106366 |
| ISSN: | 0169-2607 |
| DOI: | 10.1016/j.cmpb.2021.106366 |
| Popis: | Background and objectives Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated because the initial signs and symptoms are not specific. Biomarkers that have been proposed have low specificity and sensitivity, are expensive, and not available in every hospital. In this study, we propose the use of artificial intelligence in the form of a neural network to diagnose sepsis using only common laboratory tests and vital signs that are routine and widely available. Methods A retrospective, cross sectional cohort of 113 patients from an intensive care unit, each with 48 routinely evaluated vital signs and biochemical parameters was used to train, validate and test a neural network with 48 inputs, 10 neurons in a single hidden layer and one output. The sensitivity and specificity of the neural network as a point sampled diagnostic test was calculated. Results All but one case were correctly diagnosed by the neural network, with 91% sensitivity and 100% specificity in the validation data set, and 100% sensitivity and specificity in the test data set. Conclusions The designed neural network system can identify patients with sepsis, with minimal resources using standard laboratory tests widely available in most health care facilities. This should reduce the burden on the medical staff of a difficult diagnosis and should improve outcomes for patients with sepsis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect