Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation.

Objective: The paper introduces a framework for the evaluation of the encoding of factual scientific knowledge, designed to streamline the manual evaluation process typically conducted by domain experts. Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially define a step change in biomedical discovery, reducing the barriers for accessing and integrating existing medical evidence. This work explores the potential of LLMs for dialoguing with biomedical background knowledge, using the context of antibiotic discovery.

Epidemiological and clinical analysis of polish short-term and long-term travelers returning from tropical countries.

Background: There is a lack of information about health problems after returning from tropical countries among travelers from Poland. The aim was to create characteristics of diseases imported to Poland from tropical regions and to determine the changes in the travel trends.

Method: This retrospective study is based on medical records of 2391 Polish patients >18 years old, hospitalized between 2006 and 2016 after returning from tropical areas.

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.

Background: There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings.

Methods: This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis.

Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy.

Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown otherwise promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta-review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies.

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic.