Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults

Eickelberg, Garrett; Sanchez-Pinto, L. Nelson; Luo, Yuan

doi:10.1016/j.jbi.2020.103540

Cited by 17 publications

(19 citation statements)

References 45 publications

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“…This is consistent with [44] where the same SINAN-TB data set was used and features selected by a specialist. We used the entire data set and applied k-fold cross validation, with k = 10 as per [71][72][73][74].…”

Section: Methodsmentioning

confidence: 99%

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

et al. 2021

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Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

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Section: Methodsmentioning

confidence: 99%

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

et al. 2021

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“…At present, sometimes empiric antimicrobials are prescribed for patients who do not need it, or they are not stopped in a timely manner. Eickelberg et al investigated whether machine learning could help identify patients at low risk for bacterial infection and hence suitable for antimicrobial discontinuation [34]. Different machine learning models were investigated, using clinical parameters and characteristics, blood gas and laboratory results, as well as certain administered medications, to evaluate the bacterial infection risk at three time points after initiation of empirical antimicrobial therapy: 24 h, 48 h and 72 h. Interestingly, there was little variation in performance between the 24 h and the 72 h models.…”

Section: End Of Antimicrobial Therapymentioning

confidence: 99%

Artificial Intelligence in Infection Management in the ICU

Corte

Hoecke

Waele

2022

Annual Update in Intensive Care and Emergency Medicine

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“…There have been extensive studies using EHR data to deliver precision care through clinical decision support systems, and, among these, many have focused on utilizing microbial culture results found in electronic health records to improve antibiotic stewardship measures in clinical settings. 18,19,20 Here, we seek to investigate the use of machine learningdriven approaches for predicting antibiotic susceptibility to aid in empiric antibiotic therapy to one of the most preva-Figure 3. The longitudinal data is extracted such that we create a table consisting of the subject and hospital admission identifiers, the time of data collection, and then the type of data (corresponding to label) and the value of that label.…”

Section: −10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Antibiotic Stewardship in the Treatment of Stapholycoccus Bacterial Infections

Brokowski¹,

Chiang²

2022

Preprint

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Antibiotic resistance is one of the leading issues in modern healthcare due to the inability to treat common infections with available antibiotics. Many of the mechanisms of resistance have been caused by the inappropriate prescription of antibiotics to treat illnesses such as the cold or flu or the over-prescription of broad-spectrum antibiotics. Epitomizing this problem is the Staphylococcus bacteria where certain strains have become resistant to penicillin-related drugs and Vancomycin, one of the treatments for MRSA. To address this, we developed machine learning models to predict antibiotic activity and susceptibility using a patient's entire available electronic health record. We selected patients who were suspected of having a staph infection from the Medical Information Mart for Intensive Care III (MIMIC-III) data set and utilized their microbiological culture results to identify the number of patients that were prescribed an inappropriate antibiotic and then propose suitable alternatives. In our test set, we identified that empiric prescriptions had an efficiency rate of 40 percent (the rate at which an antibiotic that would provide activity was prescribed), and the other 60 percent of cases were not susceptible to the prescribed antibiotic or the antibiotic that they were given was not tested for susceptibility against their infection. Our best models identified antibiotic susceptibility with AUROCs up to 0.9 and raw specificity up to 0.7. The models were also able to propose suitable alternatives in all but 10 cases. Overall these results demonstrate the need for implementing clinical decision support systems advising clinicians during the prescription process, and our further work will address this issue.

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Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults

Cited by 17 publications

References 45 publications

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Artificial Intelligence in Infection Management in the ICU

Machine Learning for Antibiotic Stewardship in the Treatment of Stapholycoccus Bacterial Infections

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