Bacteriemia adquirida en la comunidad: elaboración de un modelo de predicción clínica en pacientes ingresados en un servicio de medicina interna

Palacios, E.; Gutiérrez-Macías, Alfonso; Odriozola, Pedro Martínez; Vicario, Ricardo Franco; Jiménez, Nicanor García; Villa, Felipe Miguel de la

doi:10.1157/13065198

Cited by 10 publications

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“…A predictive model in the medical practice must have an AUC greater than 0.7, and a good predictive model has AUC≥ 0.8. The previous results in the literature using classical modelling techniques in specific types of bacteraemia are as follows: pneumonia [ 32 ] with AUC 0.79, skin-related [ 33 ] with AUC 0.71 or any type [ 34 ] with AUC 0.77. Therefore, the ML values of AUC, sensitivity, specificity, predictive positive and negative values exceed the results described in the literature.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…Regarding prediction models for bacteraemia, a physician’s suspicion of bacteraemia lacks sensitivity, specificity, or predictive values to be clinically useful. Some examples of clinical prediction models have been developed with bacteraemia related to pneumonia [ 31 , 32 ], skin infections [ 33 ], and community-acquired bacteraemias [ 34 ]. Unlike ours, they all are focused on specific infections, which applies to any source of intra- or extra-hospital bacteraemia.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers

et al. 2021

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Background The bacteraemia prediction is relevant because sepsis is one of the most important causes of morbidity and mortality. Bacteraemia prognosis primarily depends on a rapid diagnosis. The bacteraemia prediction would shorten up to 6 days the diagnosis, and, in conjunction with individual patient variables, should be considered to start the early administration of personalised antibiotic treatment and medical services, the election of specific diagnostic techniques and the determination of additional treatments, such as surgery, that would prevent subsequent complications. Machine learning techniques could help physicians make these informed decisions by predicting bacteraemia using the data already available in electronic hospital records. Objective This study presents the application of machine learning techniques to these records to predict the blood culture’s outcome, which would reduce the lag in starting a personalised antibiotic treatment and the medical costs associated with erroneous treatments due to conservative assumptions about blood culture outcomes. Methods Six supervised classifiers were created using three machine learning techniques, Support Vector Machine, Random Forest and K-Nearest Neighbours, on the electronic health records of hospital patients. The best approach to handle missing data was chosen and, for each machine learning technique, two classification models were created: the first uses the features known at the time of blood extraction, whereas the second uses four extra features revealed during the blood culture. Results The six classifiers were trained and tested using a dataset of 4357 patients with 117 features per patient. The models obtain predictions that, for the best case, are up to a state-of-the-art accuracy of 85.9%, a sensitivity of 87.4% and an AUC of 0.93. Conclusions Our results provide cutting-edge metrics of interest in predictive medical models with values that exceed the medical practice threshold and previous results in the literature using classical modelling techniques in specific types of bacteraemia. Additionally, the consistency of results is reasserted because the three classifiers’ importance ranking shows similar features that coincide with those that physicians use in their manual heuristics. Therefore, the efficacy of these machine learning techniques confirms their viability to assist in the aims of predictive and personalised medicine once the disease presents bacteraemia-compatible symptoms and to assist in improving the healthcare economy.

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Section: Discussion Of the Resultsmentioning

confidence: 99%