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Antibiotics
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Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and...
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Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Antibiotics Use and Antimicrobial Stewardship".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 16430

Special Issue Editors

Dr. Rita Murri

E-Mail Website
Guest Editor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
Interests: antibiotic stewardship; bloodstream infections; abdominal infections; candidemia and invasive fungal infections; COVID-19
Dr. Massimo Fantoni

E-Mail Website
Guest Editor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
Interests: antibiotic stewardship; bone and joint infections; COVID-19; bloodstream infections; abdominal infections; candidemia and invasive fungal infections

Special Issue Information

Dear Colleagues,

In the last years a large number of digital innovations are transforming healthcare. The implementation of digitization and artificial intelligence in the healthcare system as well as the machine learning techniques for data analyses represents an inestimable added value.

Even in the field of Infectious Diseases and Clinical Microbiology, the artificial intelligence and machine learning techniques facilitate the extraction and analyses of large amounts of data from multiple sources, correlation with many different parameters including genomic and proteomic, the study of outcome determinants and predictive models of antimicrobial treatment success. The new frontiers of digitization could have interesting applications especially in pandemic settings.

Therefore, the main topic of this Special Issue includes the development and application in clinical practice of digitization, artificial intelligence and machine learning techniques in Infectious diseases and Clinical Microbiology, and in particular studies on:

  • Machine learning predictive models for the diagnosis of bloodstream infections, sepsis, pneumonia, COVID-19, Clostridium difficile colitis, tuberculosis, malaria, and other infectious diseases
  • Machine learning-based models to identify determinants of adverse outcomes or treatment response in infectious diseases
  • Machine learning-based predictive models of multidrug resistant organisms (MDRO) carriage and of infections caused by MDRO
  • Internet of things (IoT) applied to infectious diseases
  • Clinical decision support systems in the antibiotic choice using machine learning
  • Data Mart buildings for data analyses on Infectious Diseases and in Microbiology
  • Network and consortium building to operate multicenter studies on antimicrobial treatments based on machine learning techniques
  • Limitations and barriers of artificial intelligence
  • Use of digitalization and artificial intelligence for saving resources
  • Big data-based dashboards for daily routine practice
  • Machine learning approach and Genome-based prediction to fight bacterial antibiotic resistance
  • Computational approaches for prediction of pathogen-host interactions
  • Machine-learning techniques applied to antibacterial drug discovery or vaccine development
  • Analysis of medical imaging of infectious diseases
  • Methods to track a pandemic or to alert through the investigation on digital behavior patterns
  • Use of sensors, watches, electronic alarms applied to evaluate determinants of antimicrobial resistance and prevent multidrug resistant infections in a hospital
  • Automatized or semi-automatized surveillance systems of healthcare-associated infection

Dr. Rita Murri
Dr. Massimo Fantoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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15 pages, 1432 KiB  
Article
Machine Learning and Antibiotic Management
by Riccardo Maviglia, Teresa Michi, Davide Passaro, Valeria Raggi, Maria Grazia Bocci, Edoardo Piervincenzi, Giovanna Mercurio, Monica Lucente and Rita Murri
Antibiotics 2022, 11(3), 304; https://doi.org/10.3390/antibiotics11030304 - 24 Feb 2022
Cited by 3 | Viewed by 2110
Abstract
Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic [...] Read more.
Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases)
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16 pages, 1237 KiB  
Article
Resistance Trend Estimation Using Regression Analysis to Enhance Antimicrobial Surveillance: A Multi-Centre Study in London 2009–2016
by Bernard Hernandez, Pau Herrero-Viñas, Timothy M. Rawson, Luke S. P. Moore, Alison H. Holmes and Pantelis Georgiou
Antibiotics 2021, 10(10), 1267; https://doi.org/10.3390/antibiotics10101267 - 18 Oct 2021
Cited by 5 | Viewed by 3175
Abstract
In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote [...] Read more.
In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians’ adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it provides fundamental knowledge to the wide range of stakeholders to revise and potentially tailor existing guidelines to the specific needs of each hospital. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases)
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11 pages, 550 KiB  
Article
Prognostic Role of Serum Procalcitonin Measurement in Adult Patients Admitted to the Emergency Department with Fever
by Marcello Covino, Alberto Manno, Giuseppe De Matteis, Eleonora Taddei, Luigi Carbone, Andrea Piccioni, Benedetta Simeoni, Massimo Fantoni, Francesco Franceschi and Rita Murri
Antibiotics 2021, 10(7), 788; https://doi.org/10.3390/antibiotics10070788 - 29 Jun 2021
Cited by 8 | Viewed by 1655
Abstract
Background and Objectives. Fever is one of the most common presenting complaints in the Emergency Department (ED). This study aimed at evaluating the prognostic role of serum Procalcitonin (PCT) measurement among adult patients admitted to the ED with fever. Materials and Methods. This [...] Read more.
Background and Objectives. Fever is one of the most common presenting complaints in the Emergency Department (ED). This study aimed at evaluating the prognostic role of serum Procalcitonin (PCT) measurement among adult patients admitted to the ED with fever. Materials and Methods. This is a retrospective cross-sectional study including all consecutive patients admitted to ED with fever and subsequently hospitalized in a period of six-year (January 2014 to December 2019). Inclusion criteria were age > 18 years, fever (T ≥ 38 °C) or chills within 24 h from presentation to the ED as the main symptom, and availability of a PCT determination obtained <24 h since ED access. The primary endpoint was overall in-hospital mortality. Results. Overall, 6595 patients were included in the study cohort (3734 males, 55.6%), with a median age of 71 [58–81] years. Among these, based on clinical findings and quick sequential organ failure assessment (qSOFA), 422 were considered septic (36.2% deceased), and 6173 patients non-septic (16.2% deceased). After correction for baseline covariates, a PCT > 0.5 ng/mL was an independent risk factor for all-cause in-hospital death in both groups (HR 1.77 [1.27–2.48], and 1.80 [1.59–2.59], respectively). Conclusions. Among adult patients admitted with fever, the PCT assessment in ED could have reduced prognostic power for patients with a high suspicion of sepsis. On the other hand, it could be useful for sepsis rule-out for patients at low risk. In these latter patients, the prognostic role of PCT is higher for those with a final diagnosis of bloodstream infection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases)
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Review

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16 pages, 2289 KiB  
Review
Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates
by Ali A. Rabaan, Saad Alhumaid, Abbas Al Mutair, Mohammed Garout, Yem Abulhamayel, Muhammad A. Halwani, Jeehan H. Alestad, Ali Al Bshabshe, Tarek Sulaiman, Meshal K. AlFonaisan, Tariq Almusawi, Hawra Albayat, Mohammed Alsaeed, Mubarak Alfaresi, Sultan Alotaibi, Yousef N. Alhashem, Mohamad-Hani Temsah, Urooj Ali and Naveed Ahmed
Antibiotics 2022, 11(6), 784; https://doi.org/10.3390/antibiotics11060784 - 08 Jun 2022
Cited by 28 | Viewed by 5687
Abstract
Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan [...] Read more.
Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases)
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15 pages, 858 KiB  
Review
Interventions for Early-Stage Pericoronitis: Systematic Review of Randomized Clinical Trials
by Tânia Oppido Schalch, Ana Luiza Cabrera Martimbianco, Marcela Leticia Leal Gonçalves, Lara Jansiski Motta, Elaine Marcilio Santos, Rebeca Boltes Cecatto, Sandra Kalil Bussadori and Anna Carolina Ratto Tempestini Horliana
Antibiotics 2022, 11(1), 71; https://doi.org/10.3390/antibiotics11010071 - 08 Jan 2022
Cited by 4 | Viewed by 2415
Abstract
Background: To investigate the efficacy and safety of interventions for early stage pericoronitis. Methods: We searched for randomized controlled trials (RCTs) in databases from inception to July 2020, without language restriction. RCTs assessing adolescents and adults were included. Results: Seven RCT with clinical [...] Read more.
Background: To investigate the efficacy and safety of interventions for early stage pericoronitis. Methods: We searched for randomized controlled trials (RCTs) in databases from inception to July 2020, without language restriction. RCTs assessing adolescents and adults were included. Results: Seven RCT with clinical diversity were included, so, it was not possible to conduct meta-analyses. Individual study data showed an improvement in oral health quality of life in favor of topical benzydamine versus diclofenac capsule (Mean difference (MD) −1.10, 95% Confidence interval (CI) −1.85 to −0.35), and no difference between topical benzydamine and flurbiprofen capsule (MD −0.55 95% CI −1.18 to 0.0). There was no difference between diclofenac and flurbiprofen capsules (MD 0.55, 95% CI −0.29 to 1.39). An imprecise estimate of effects was found for all outcomes, considering (i) oral versus topic pharmacological treatment, (ii) different oral pharmacological treatments, (iii) pharmacological treatment associated with laser versus placebo laser, (iv) pharmacological treatment associated with different mouthwashes, and (v) conventional treatment associated to antimicrobial photodynamic therapy versus conventional treatment, with low to very low certainty of evidence. Conclusions: Until future well-designed studies can be conducted, the clinical decision for early stage pericoronitis should be guided by individual characteristics, settings and financial aspects. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Epidemiology, Diagnostic and Treatment of Infectious Diseases)
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