Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.

Lung cancer is still a leading cause of cancer-related deaths worldwide. Vital to ameliorating patient survival rates are early detection, precise evaluation, and personalized treatments. Recent years have witnessed a profound transformation in the field, marked by intricate diagnostic processes and intricate therapeutic protocols that integrate diverse omics domains, heralding a paradigm shift towards personalized and preventive healthcare.

A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.

Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules' identification.

Real-world evidence evaluation of LDL-C in hospitalized patients: a population-based observational study in the timeframe 2021-2022.

Aims: European registries and retrospective cohort studies have highlighted the failure to achieve low-density lipoprotein-cholesterol (LDL-C) targets in many very high-risk patients. Hospitalized patients are often frail, and frailty is associated with all-cause and cardiovascular mortality. The aim of this study is to evaluate LDL-C levels in a real-world inpatient setting, identifying cardiovascular risk categories and highlighting treatment gaps in the implementation of LDL-C management.

Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves.

Increased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy).