Advantages and limitations of large language models for antibiotic prescribing and antimicrobial stewardship.

Antibiotic prescribing requires balancing optimal treatment for patients with reducing antimicrobial resistance. There is a lack of standardization in research on using large language models (LLMs) for supporting antibiotic prescribing, necessitating more efforts to identify biases and misinformation in their outputs. Educating future medical professionals on these aspects is crucial for ensuring the proper use of LLMs for supporting antibiotic prescribing, providing a deeper understanding of their strengths and limitations.

Antibiotics and Artificial Intelligence: Clinical Considerations on a Rapidly Evolving Landscape.

The growing interest in leveraging artificial intelligence (AI) tools for healthcare decision-making extends to improving antibiotic prescribing. Large language models (LLMs), a type of AI trained on extensive datasets from diverse sources, can process and generate contextually relevant text. While their potential to enhance patient outcomes is significant, implementing LLM-based support for antibiotic prescribing is complex.

Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review.

Antimicrobial resistance in bacteria has been associated with significant morbidity and mortality in hospitalized patients. In the era of big data and of the consequent frequent need for large study populations, manual collection of data for research studies on antimicrobial resistance and antibiotic use has become extremely time-consuming and sometimes impossible to be accomplished by overwhelmed healthcare personnel. In this review, we discuss relevant concepts pertaining to the automated extraction of antibiotic resistance and antibiotic prescription data from laboratory information systems and electronic health records to be used in clinical studies, starting from the currently available literature on the topic.

COVID-19 throughout pandemic waves and virus variants: a real-life experience in an Italian hospital.

New therapies and vaccines changed the management of COVID-19. The aim of this retrospective study was to describe characteristics, in-hospital mortality and its predictors in patients with moderate/severe COVID-19, considering the 4 different pandemic waves and viral variants' prevalence from February 2020 to January 2022. Among 1135 patients included, 873 (77%) had at least one comorbidity, 177 (16%) were immunocompromised.

Artificial intelligence and prescription of antibiotic therapy: present and future.

Introduction: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

Areas Covered: In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription.

Prediction of candidemia with machine learning techniques: state of the art.

In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia.

Transforming Ontology Web Language Elements into Common Terminology Service 2 Terminology Resources.

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution.

Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.

Objectives: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management can be assessed with the EQUAL Candida Score.

Perceptions of Animal Welfare on Livestock: Evidence from College Agronomy Students in Costa Rica.

Ethical considerations regarding our treatment of animals have gained strength, leading to legislation and a societal focus across various disciplines. This is a subject of study within curricula related to agri-food sciences. The aim was to determine the perceptions of agronomy university students concerning animal welfare in livestock production systems.

Weight Gain in Overweight and Obese People with HIV-The OBHIV Cohort.

Background: HIV and non-HIV-related factors have been related to weight gain (WG); however, their specific impact on people with HIV (PWH) who are overweight or obese remains unclear.

Methods: This is a single-center observational study enrolling PWH with a BMI > 25 kg/m. A generalized linear model was used to assess variables related to greater WG during 12 years of observation.

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models.

De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.

The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Processing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is annotated with PHI information.

Early diagnosis of candidemia with explainable machine learning on automatically extracted laboratory and microbiological data: results of the AUTO-CAND project.

Background: Candidemia is associated with a heavy burden of morbidity and mortality in hospitalized patients. The availability of blood culture results could require up to 48-72 h after blood draw; thus, early treatment decisions are made in the absence of a definite diagnosis.

Methods: In this retrospective study, we assessed the performance of different supervised machine learning algorithms for the early differential diagnosis of candidemia and bacteremia in adult patients on a large dataset automatically extracted within the AUTO-CAND project.

Defining a Preprocessing Pipeline for the MULTI-SITA Project and General Medical Italian Natural Language Data.

The application of Natural Language Processing (NLP) to medical data has revolutionized different aspects of health care. The benefits obtained from the implementation of this technique spill over into several areas, including in the implementation of chatbots, which can provide medical assistance remotely. Every possible application of NLP depends on one first main step: the pre-processing of the corpus retrieved.

Barriers to HCV micro-elimination in a cohort of people living with HIV (PLWH).

To achieve the World Health Organization goal of hepatitis C virus (HCV) eradication, barriers to treatment should be investigated and overcome. The aim of this study was to identify those barriers and describe the strategies adopted to achieve HCV micro-elimination in a cohort of coinfected people living with HIV (PLWH-HCV). Adult PLWH-HCV followed at our hospital with detectable serum HCV-RNA in 2018 were enrolled.

Effect of SARS-CoV-2 vaccination in a vulnerable COVID-19 cohort: a real-life experience in an Italian Hospital.

Clinical trials demonstrated that SARS-CoV-2 vaccines reduce COVID-19-related mortality and morbidity. We describe the effect of vaccination on COVID-19-patients admitted at our hospital. Retrospective, single-center study conducted in Genoa, Italy, including patients ≥18years hospitalized for COVID-19 from May to December 2021.

Trend over time of HIV-1 drug resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs) and their drivers: A cohort study from Antiviral Response Cohort Analysis (ARCA).

The rise of HIV-1 drug resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs) threatens the long-term success of NNRTI-based therapies. Our study aims to describe the circulation of major resistance-associated mutations (RAMs) for NNRTIs in people living with HIV (PLWH) in Italy from 2000 to 2020. We included 5982 naïves and 28 505 genotypes from 9387 treatment-experienced PLWH from the Antiviral Response Cohort Analysis (ARCA) cohort.

Protocol of Validation Mechanisms Within the Design of an Electronic Case Report Form for the MULTI-SITA Project.

With the wide diffusion of web technology, dedicated electronic Case Report Forms (eCRFs) became the main tool for collecting patient data. The focus of this work is to thoroughly consider the data quality in every aspect of the design of the eCRF, with the result of having multiple steps of validation that should produce a diligent and multidisciplinary approach towards every step of data acquisition. This goal affects every aspect of the system design.

External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact.

Background: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis.

Methods: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method.

Validation of an Automated System for the Extraction of a Wide Dataset for Clinical Studies Aimed at Improving the Early Diagnosis of Candidemia.

There is increasing interest in assessing whether machine learning (ML) techniques could further improve the early diagnosis of candidemia among patients with a consistent clinical picture. The objective of the present study is to validate the accuracy of a system for the automated extraction from a hospital laboratory software of a large number of features from candidemia and/or bacteremia episodes as the first phase of the AUTO-CAND project. The manual validation was performed on a representative and randomly extracted subset of episodes of candidemia and/or bacteremia.

Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network.

Background: It is 30 years since evidence-based medicine became a great support for individual clinical expertise in daily practice and scientific research. Electronic systems can be used to achieve the goal of collecting data from heterogeneous datasets and to support multicenter clinical trials. The (LIDN) is a web-based platform for data collection and reuse originating from a regional effort and involving many professionals from different fields.

CTS2 OWL: Mapping OWL Ontologies to CTS2 Terminology Resources.

The advancement of healthcare towards P5 medicine requires communication and cooperation between all actors and institutions involved. Interoperability must go beyond integrating data from different sources and include the understanding of the meaning of the data in the context of concepts and contexts they represent for a specific use case. In other words, we have to advance from data sharing through sharing semantics up to sharing clinical and medical knowledge.

A NLP Pipeline for the Automatic Extraction of a Complete Microorganism's Picture from Microbiological Notes.

The Italian "Istituto Superiore di Sanità" (ISS) identifies hospital-acquired infections (HAIs) as the most frequent and serious complications in healthcare. HAIs constitute a real health emergency and, therefore, require decisive action from both local and national health organizations. Information about the causative microorganisms of HAIs is obtained from the results of microbiological cultures of specimens collected from infected body sites, but microorganisms' names are sometimes reported only in the notes field of the culture reports.