Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.

Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting. Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.

Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients.

Artificial intelligence processes are increasingly being used in emergency medicine, notably for supporting clinical decisions and potentially improving healthcare services. This study investigated demographics, coagulation tests, and biochemical markers routinely used for patients seen in the Emergency Department (ED) concerning hospitalization. This retrospective observational study included 13,991 emergency department visits of patients who had undergone biomarker testing to a tertiary public hospital in Greece during 2020. After applying five well-known classifiers of the caret package for machine learning of the R programming language in the whole data set and to each ED unit separately, the best performance regarding AUC ROC was observed in the Pulmonology ED unit. Furthermore, among the five classification techniques evaluated, a random forest classifier outperformed other models.

Exploratory Clustering for Emergency Department Patients.

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

Prediction of Hospitalization Using Machine Learning for Emergency Department Patients.

The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).

Predicting Hospital Admission for Emergency Department Patients: A Machine Learning Approach.

The objective of this study was to establish a machine learning model and to evaluate its predictive capability of admission to the hospital. This observational retrospective study included 3204 emergency department visits to a public tertiary care hospital in Greece from 14 March to 4 May 2019. We investigated biochemical markers and coagulation tests that are routinely checked in patients visiting the Emergency Department (ED) in relation to the ED outcome (admission or discharge). Among the most popular classification techniques of the scikit-learn library through a 10-fold cross-validation approach, a GaussianNB model outperformed other models with respect to the area under the receiver operating characteristic curve.

Admission and Discharge Following Ambulance Transport to the Emergency Department.

Emergency ambulance use is deemed necessary for the transport of acutely ill patients to hospital emergency departments (ED). However, some patients are discharged as they present low acuity or chronic problems and should receive primary healthcare services, while the most severely ill are admitted. In the present study, we examined the descriptive epidemiology of ambulance transports for emergencies in the ED by utilizing the data of the information systems of a public tertiary general hospital in Greece. More than half of the patients transferred to the ED by an ambulance were finally admitted to the hospital (52.25%), whereas only one-third (33.74%) of those transferred by other means. A statistically significant association was detected between ambulance use and hospital admission. Age was also statistically significantly higher in the ambulance group. Higher mean values of creatinine, CRP, LDH, urea, white-blood-cell count, and neutrophils were detected in the ambulance group, in contrast to hemoglobin and lymphocyte count which were higher in the non-ambulance group.

Machine Learning for Antibiotic Resistance Prediction: A Prototype Using Off-the-Shelf Techniques and Entry-Level Data to Guide Empiric Antimicrobial Therapy.

OBJECTIVE: In the era of increasing antimicrobial resistance, the need for early identification and prompt treatment of multi-drug-resistant infections is crucial for achieving favorable outcomes in critically ill patients. As traditional microbiological susceptibility testing requires at least 24 hours, automated machine learning (AutoML) techniques could be used as clinical decision support tools to predict antimicrobial resistance and select appropriate empirical antibiotic treatment. METHODS: An antimicrobial susceptibility dataset of 11,496 instances from 499 patients admitted to the internal medicine wards of a public hospital in Greece was processed by using Microsoft Azure AutoML to evaluate antibiotic susceptibility predictions using patients' simple demographic characteristics, as well as previous antibiotic susceptibility testing, without any concomitant clinical data. Furthermore, the balanced dataset was also processed using the same procedure. The datasets contained the attributes of sex, age, sample type, Gram stain, 44 antimicrobial substances, and the antibiotic susceptibility results. RESULTS: The stack ensemble technique achieved the best results in the original and balanced dataset with an area under the curve-weighted metric of 0.822 and 0.850, respectively. CONCLUSIONS: Implementation of AutoML for antimicrobial susceptibility data can provide clinicians useful information regarding possible antibiotic resistance and aid them in selecting appropriate empirical antibiotic therapy by taking into consideration the local antimicrobial resistance ecosystem.

Analyzing Acute Care Surgery Patient Flow in the Emergency Department During COVID-19 Pandemic.

During the COVID-19 pandemic, the number of visits in emergency departments (ED) worldwide decreased significantly based on several studies. This study aims to compare the patient flow in the emergency surgery department during the COVID-19 pandemic and a control period in the emergency department of a public tertiary care hospital in Greece. The overall patient flow reduction regarding the ED visits between the two examined periods was 49.07%. The emergency surgery department's corresponding visits were 235 and 552, respectively, which indicated an overall patient flow decrease of 57.43%. Chi-square analysis showed that age groups and ambulance use had statistically significant associations with the periods examined. An independent samples t-test was applied and deduced that the average patient's age was statistically significantly higher in the COVID-19 pandemic than in the non-pandemic period. By analyzing hospital information system data, useful conclusions can be drawn to prepare a surgical emergency unit better and optimize resource allocation in a healthcare facility in similar critical situations.

Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains.

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient's basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immense costs for their treatment. Early identification of the pathogen and its antibiotic resistance profile is crucial for a favorable outcome. Given the fact that more than 24 hours are usually required to perform common antibiotic resistance tests after the sample collection, the implementation of machine learning methods could be of significant help in selecting empirical antibiotic treatment based only on the sample type, Gram stain, and patient's basic characteristics. In this paper, five machine learning (ML) algorithms have been tested to determine antibiotic susceptibility predictions using simple demographic data of the patients, as well as culture results and antibiotic susceptibility tests. Implementing ML algorithms to antimicrobial susceptibility data may offer insightful antibiotic susceptibility predictions to assist clinicians in decision-making regarding empirical treatment.

Using Machine Learning Techniques to Aid Empirical Antibiotic Therapy Decisions in the Intensive Care Unit of a General Hospital in Greece.

Hospital-acquired infections, particularly in the critical care setting, have become increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. Time is critical to identifying bacteria and their resistance to antibiotics due to the critical health status of patients in the intensive care unit (ICU). As common antibiotic resistance tests require more than 24 h after the sample is collected to determine sensitivity in specific antibiotics, we suggest applying machine learning (ML) techniques to assist the clinician in determining whether bacteria are resistant to individual antimicrobials by knowing only a sample's Gram stain, site of infection, and patient demographics. In our single center study, we compared the performance of eight machine learning algorithms to assess antibiotic susceptibility predictions. The demographic characteristics of the patients are considered for this study, as well as data from cultures and susceptibility testing. Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, solely from the Microbiology Laboratory without any of the patient's clinical data, even in resource-limited hospital settings, can provide informative antibiotic susceptibility predictions to aid clinicians in selecting appropriate empirical antibiotic therapy. These strategies, when used as a decision support tool, have the potential to improve empiric therapy selection and reduce the antimicrobial resistance burden.

Using Microbiological Data Analysis to Tackle Antibiotic Resistance of Klebsiella Pneumoniae.

Optimal antibiotic use for the treatment of nosocomial infections plays a central role in the effort to control the rapidly increasing prevalence of multidrug-resistant bacteria. Antibiotic selection should be based on accurate knowledge of local susceptibility rates. Traditional methods of resistance reporting, which are in routine use by microbiology laboratories could be enhanced by using statistically significant results. We present a method of reporting based on antibiotic susceptibility data analysis which offers an accurate tool that reduces clinician uncertainty and enables optimization of the antibiotic selection process.

A 2-Year Single-Centre Audit on Antibiotic Resistance of Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae Strains from an Intensive Care Unit and Other Wards in a General Public Hospital in Greece.

Hospital-acquired infections, particularly in the critical care setting, are becoming increasingly common during the last decade, with Gram-negative bacterial infections presenting the highest incidence among them. Multi-drug-resistant (MDR) Gram-negative infections are associated with high morbidity and mortality, with significant direct and indirect costs resulting from long hospitalization due to antibiotic failure. As treatment options become limited, antimicrobial stewardship programs aim to optimize the appropriate use of currently available antimicrobial agents and decrease hospital costs. Pseudomonas aeruginosa, Acinetobacter baumannii and Klebsiella pneumoniae are the most common resistant bacteria encountered in intensive care units (ICUs) and other wards. To establish preventive measures, it is important to know the prevalence of Gram-negative isolated bacteria and antibiotic resistance profiles in each ward separately, compared with ICUs. In our single centre study, we compared the resistance levels per antibiotic of P. aeruginosa, A. baumannii and K.pneumoniae clinical strains between the ICU and other facilities during a 2-year period in one of the largest public tertiary hospitals in Greece. The analysis revealed a statistically significant higher antibiotic resistance of the three bacteria in the ICU isolates compared with those from other wards. ICU strains of P. aeruginosa presented the highest resistance rates to gentamycin (57.97%) and cefepime (56.67%), followed by fluoroquinolones (55.11%) and carbapenems (55.02%), while a sensitivity rate of 97.41% was reported to colistin. A high resistance rate of over 80% of A. baumannii isolates to most classes of antibiotics was identified in both the ICU environment and regular wards, with the lowest resistance rates reported to colistin (53.37% in ICU versus an average value of 31.40% in the wards). Statistically significant higher levels of resistance to most antibiotics were noted in ICU isolates of K. pneumoniae compared with non-ICU isolates, with the highest difference-up to 48.86%-reported to carbapenems. The maximum overall antibiotic resistance in our ICU was reported for Acinetobacter spp. (93.00%), followed by Klebsiella spp. (72.30%) and Pseudomonas spp. (49.03%).