Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.
J Crit Care
; 55: 73-78, 2020 02.
Article
en En
| MEDLINE
| ID: mdl-31715534
ABSTRACT
PURPOSE:
To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30â¯days (30-day QALY). MATERIAL ANDMETHODS:
Six machine-learning algorithms were applied to predict 30-day QALY for 777 patients admitted in a prospective cohort study conducted in Intensive Care Units (ICUs) of two public Brazilian hospitals specialized in cancer care. The predictors were 37 characteristics collected at ICU admission. Discrimination was evaluated using the area under the receiver operating characteristic (AUROC) curve. Sensitivity, 1-specificity, true/false positive and negative cases were measured for different estimated probability cutoff points (30%, 20% and 10%). Calibration was evaluated with GiViTI calibration belt and test.RESULTS:
Except for basic decision trees, the adjusted predictive models were nearly equivalent, presenting good results for discrimination (AUROC curves over 0.80). Artificial neural networks and gradient boosted trees achieved the overall best calibration, implying an accurately predicted probability for 30-day QALY.CONCLUSIONS:
Except for basic decision trees, predictive models derived from different machine-learning algorithms discriminated the QALY risk at 30â¯days well. Regarding calibration, artificial neural network model presented the best ability to estimate 30-day QALY in critically ill oncologic patients admitted to ICUs.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Calidad de Vida
/
Aprendizaje Automático
/
Unidades de Cuidados Intensivos
/
Neoplasias
Tipo de estudio:
Clinical_trials
/
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adult
/
Aged
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Female
/
Humans
/
Male
/
Middle aged
País/Región como asunto:
America do sul
/
Brasil
Idioma:
En
Revista:
J Crit Care
Asunto de la revista:
TERAPIA INTENSIVA
Año:
2020
Tipo del documento:
Article