The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.Comment: Submitted for publication in Nature Scientific Dat

Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician’s point of view

Use of artificial intelligence methods for the prediction of antimicrobial resistance and empirical treatment selection

Introduction: 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 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. As traditional susceptibility tests require more than 24 hours after sample collection to determine susceptibility to specific antibiotics, we propose to apply machine learning techniques to help the clinician assess whether bacteria are resistant to individual antimicrobials before antimicrobial susceptibility testing is completed. Aim: To apply and compare Machine Learning methods using data from the hospital information system and to develop antimicrobial resistance prediction models to support decisions about antimicrobial therapy. Methods: Various artificial intelligence (Machine Learning) methods were applied and compared to hospital information system data on demographic data, culture results and antimicrobial susceptibility data of patients hospitalized in the ICU and other departments of a Greek hospital over three years. The five individual studies use different machine learning classifiers and techniques such as Class Balancer and the Synthetic Minority Oversampling Technique (SMOTE) to deal with data imbalance. In addition, various data analysis techniques are used, including association rule mining with the Apriori algorithm and 10-fold cross-validation. Software tools such as WEKA and the R programming language are used to analyze and visualize the results. Results: From the combined results of the five individual studies, an extensive use of machine learning methods for the assessment and prediction of antimicrobial resistance emerges. Various algorithms and techniques were used and evaluated based on indicators such as TP rate, FP rate, Precision, Recall, F-measure, MMC, area under ROC curve and PRC. Techniques such as kNN, polynomial logistic regression, Multilayer perceptron, JRip and regression classification models were highlighted for their strong performances on different performance measures. The results of the AutoML application confirmed the value of automated machine learning in finding robust predictive models (in particular Stack Ensemble), with high performance on weighted metrics such as AUCW, APSW, F1W and ACC. The importance of various characteristics, such as type of antibiotic, sex, age and type of sample, was highlighted as a critical element in the prediction of antimicrobial resistance. Finally, by analyzing association rules based on minimum support and confidence thresholds, rules with particularly high confidence were extracted, revealing strong associations between data features and antibiotic susceptibility. Conclusion: Applying machine learning algorithms to patient antimicrobial susceptibility data, readily available, from the hospital information system, 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.Εισαγωγή: Οι νοσοκομειακές λοιμώξεις, ιδίως στη μονάδα εντατικής θεραπείας, έχουν γίνει όλο και πιο συχνές κατά την τελευταία δεκαετία, με τις αρνητικές κατά Gram βακτηριακές λοιμώξεις να παρουσιάζουν τη μεγαλύτερη συχνότητα εμφάνισης. Οι πολυανθεκτικές Gram-αρνητικές λοιμώξεις σχετίζονται με υψηλή νοσηρότητα και θνησιμότητα με σημαντικό άμεσο και έμμεσο κόστος που προκύπτει από τη μακρά νοσηλεία λόγω αποτυχίας της αντιβιοτικής αγωγής. Ο χρόνος που απαιτείται για τον εντοπισμό των βακτηρίων και τον έλεγχο της αντοχής στα αντιβιοτικά είναι καίριας σημασίας, λόγω της κρίσιμης κατάστασης της υγείας των ασθενών στη Μονάδα Εντατικής Θεραπείας. Καθώς οι παραδοσιακές δοκιμασίες ευαισθησίας απαιτούν περισσότερες από 24 ώρες μετά τη συλλογή του δείγματος για τον προσδιορισμό της ευαισθησίας σε συγκεκριμένα αντιβιοτικά, προτείνουμε την εφαρμογή τεχνικών μηχανικής μάθησης για να βοηθήσουμε τον κλινικό ιατρό να εκτιμήσει εάν τα βακτήρια είναι ανθεκτικά σε μεμονωμένα αντιμικροβιακά, προτού ολοκληρωθούν οι δοκιμασίες αντιμικροβιακής ευαισθησίας. Σκοπός: Η εφαρμογή και σύγκριση μεθόδων Μηχανικής Μάθησης χρησιμοποιώντας δεδομένα από το πληροφοριακό σύστημα του νοσοκομείου και η ανάπτυξη μοντέλων πρόβλεψης μικροβιακής αντοχής για την υποστήριξη αποφάσεων σχετικά με την αντιμικροβιακή θεραπεία. Υλικό – Μέθοδος: Εφαρμόστηκαν και συγκρίθηκαν διάφορες μέθοδοι τεχνητής νοημοσύνης (Μηχανικής Μάθησης) σε δεδομένα του πληροφοριακού συστήματος του νοσοκομείου σχετικά με δημογραφικά δεδομένα, αποτελέσματα καλλιεργειών και δεδομένα αντιμικροβιακής ευαισθησίας ασθενών που νοσηλεύονται στη ΜΕΘ και σε άλλα τμήματα ενός ελληνικού νοσοκομείου κατά τη διάρκεια τριών ετών. Στις πέντε επιμέρους μελέτες χρησιμοποιούνται διαφορετικοί ταξινομητές μηχανικής μάθησης και τεχνικές, όπως το ClassBalancer και η τεχνική υπερδειγματοληψίας συνθετικής μειονότητας (SMOTE) για την αντιμετώπιση της ανισορροπίας δεδομένων. Επιπλέον, χρησιμοποιούνται διάφορες τεχνικές ανάλυσης δεδομένων, συμπεριλαμβανομένης της εξόρυξης κανόνων συσχέτισης με τον αλγόριθμο Apriori και της 10-πλάσιας διασταυρούμενης επικύρωσης. Εργαλεία λογισμικού όπως το WEKA και η γλώσσα προγραμματισμού R χρησιμοποιούνται για ανάλυση και οπτικοποίηση των αποτελεσμάτων. Αποτελέσματα: Από τα συνολικά αποτελέσματα των πέντε επιμέρους μελετών προκύπτει μια εκτενής χρήση μεθόδων μηχανικής μάθησης για την αξιολόγηση και την πρόβλεψη της μικροβιακής αντοχής. Χρησιμοποιήθηκαν διάφοροι αλγόριθμοι και τεχνικές που εκτιμήθηκαν με βάση δείκτες όπως TP rate, FP rate, Precision, Recall, F-measure, MMC, εμβαδόν κάτω από την καμπύλη ROC και PRC. Τεχνικές όπως kNN, πολυωνυμική λογιστική παλινδρόμηση, Multilayer perceptron, JRip και μοντέλα ταξινόμησης μέσω παλινδρόμησης αναδείχθηκαν για τις ισχυρές τους επιδόσεις σε διαφορετικά μέτρα απόδοσης. Τα αποτελέσματα της εφαρμογής αυτοματοποιημένης μηχανικής μάθησης (AutoML), επιβεβαίωσαν την αξία της στην εύρεση ισχυρών μοντέλων πρόβλεψης (ειδικότερα του StackEnsemble), με υψηλές επιδόσεις σε σταθμισμένες μετρικές όπως AUCW, APSW, F1W και ACC. Η σημασία των διαφόρων χαρακτηριστικών, όπως το είδος του αντιβιοτικού, το φύλο, η ηλικία και το είδος του δείγματος, υπογραμμίστηκε ως κρίσιμο στοιχείο στην πρόβλεψη μικροβιακής αντοχής. Τέλος, με την ανάλυση κανόνων συσχέτισης βάσει κατωφλίων ελάχιστης υποστήριξης και εμπιστοσύνης, εξήχθησαν κανόνες με ιδιαίτερα υψηλή εμπιστοσύνη, αποκαλύπτοντας ισχυρές συσχετίσεις μεταξύ των χαρακτηριστικών των δεδομένων και της ευαισθησίας σε αντιβιοτικά. Συμπέρασμα: Η εφαρμογή αλγορίθμων μηχανικής μάθησης σε δεδομένα αντιμικροβιακής ευαισθησίας ασθενών, τα οποία είναι άμεσα διαθέσιμα, από το πληροφοριακό σύστημα του νοσοκομείου, ακόμη και σε νοσοκομεία με περιορισμένους πόρους, μπορεί να παρέχει κατατοπιστικές προβλέψεις ευαισθησίας στα αντιβιοτικά, ώστε να βοηθήσει τους κλινικούς ιατρούς στην επιλογή της κατάλληλης εμπειρικής αντιβιοτικής θεραπείας. Αυτές οι στρατηγικές, όταν χρησιμοποιούνται ως εργαλείο υποστήριξης αποφάσεων, έχουν τη δυνατότητα να βελτιώσουν την επιλογή εμπειρικής θεραπείας και να μειώσουν το φορτίο της μικροβιακής αντοχής

A 2-Year Single-Centre Audit on Antibiotic Resistance of <i>Pseudomonas aeruginosa</i>, <i>Acinetobacter baumannii</i> and <i>Klebsiella pneumoniae</i> 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&#8212;up to 48.86%&#8212;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%)

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

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

Presentation, management, and outcomes of older compared to younger adults with hospital-acquired bloodstream infections in the intensive care unit: a multicenter cohort study

Purpose: Older adults admitted to the intensive care unit (ICU) usually have fair baseline functional capacity, yet their age and frailty may compromise their management. We compared the characteristics and management of older (≥ 75&nbsp;years) versus younger adults hospitalized in ICU with hospital-acquired bloodstream infection (HA-BSI). Methods: Nested cohort study within the EUROBACT-2 database, a multinational prospective cohort study including adults (≥ 18&nbsp;years) hospitalized in the ICU during 2019-2021. We compared older versus younger adults in terms of infection characteristics (clinical signs and symptoms, source, and microbiological data), management (imaging, source control, antimicrobial therapy), and outcomes (28-day mortality and hospital discharge). Results: Among 2111 individuals hospitalized in 219 ICUs with HA-BSI, 563 (27%) were ≥ 75&nbsp;years old. Compared to younger patients, these individuals had higher comorbidity score and lower functional capacity; presented more often with a pulmonary, urinary, or unknown HA-BSI source; and had lower heart rate, blood pressure and temperature at presentation. Pathogens and resistance rates were similar in both groups. Differences in management included mainly lower rates of effective source control achievement among aged individuals. Older adults also had significantly higher day-28 mortality (50% versus 34%, p &lt; 0.001), and lower rates of discharge from hospital (12% versus 20%, p &lt; 0.001) by this time. Conclusions: Older adults with HA-BSI hospitalized in ICU have different baseline characteristics and source of infection compared to younger patients. Management of older adults differs mainly by lower probability to achieve source control. This should be targeted to improve outcomes among older ICU patients

The role of centre and country factors on process and outcome indicators in critically ill patients with hospital-acquired bloodstream infections

Purpose: The primary objective of this study was to evaluate the associations between centre/country-based factors and two important process and outcome indicators in patients with hospital-acquired bloodstream infections (HABSI). Methods: We used data on HABSI from the prospective EUROBACT-2 study to evaluate the associations between centre/country factors on a process or an outcome indicator: adequacy of antimicrobial therapy within the first 24&nbsp;h or 28-day mortality, respectively. Mixed logistical models with clustering by centre identified factors associated with both indicators. Results: Two thousand two hundred nine patients from two hundred one intensive care units (ICUs) were included in forty-seven countries. Overall, 51% (n = 1128) of patients received an adequate antimicrobial therapy and the 28-day mortality was 38% (n = 839). The availability of therapeutic drug monitoring (TDM) for aminoglycosides everyday [odds ratio (OR) 1.48, 95% confidence interval (CI) 1.03-2.14] or within a few hours (OR 1.79, 95% CI 1.34-2.38), surveillance cultures for multidrug-resistant organism carriage performed weekly (OR 1.45, 95% CI 1.09-1.93), and increasing Human Development Index (HDI) values were associated with adequate antimicrobial therapy. The presence of intermediate care beds (OR 0.63, 95% CI 0.47-0.84), TDM for aminoglycoside available everyday (OR 0.66, 95% CI 0.44-1.00) or within a few hours (OR 0.51, 95% CI 0.37-0.70), 24/7 consultation of clinical pharmacists (OR 0.67, 95% CI 0.47-0.95), percentage of vancomycin-resistant enterococci (VRE) between 10% and 25% in the ICU (OR 1.67, 95% CI 1.00-2.80), and decreasing HDI values were associated with 28-day mortality. Conclusion: Centre/country factors should be targeted for future interventions to improve management strategies and outcome of HABSI in ICU patients

Epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study

Purpose In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes