Oskar Wysocki
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View article: Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis
Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis Open
We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain, with a focus on cancer research. M-Reason leverages recent advances in large language models (LLMs) and m…
View article: Translating the machine; An assessment of clinician understanding of ophthalmological artificial intelligence outputs
Translating the machine; An assessment of clinician understanding of ophthalmological artificial intelligence outputs Open
Clinicians' trust in AI algorithms are affected by explainability methods and factors, including AI's performance, personal judgments and clinical experience. The development of clinical AI systems should consider the above and these respo…
View article: Integrating Expert Knowledge into Logical Programs via LLMs
Integrating Expert Knowledge into Logical Programs via LLMs Open
This paper introduces ExKLoP, a novel framework designed to evaluate how effectively Large Language Models (LLMs) integrate expert knowledge into logical reasoning systems. This capability is especially valuable in engineering, where exper…
View article: SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning Open
View article: Toward Low-Cost Digital Twins for Urban Transportation Systems
Toward Low-Cost Digital Twins for Urban Transportation Systems Open
This study introduces an advanced Digital Twin (DT) framework for trams, transforming public transport maintenance through intelligent analytics and real-time data monitoring. Our approach includes developing the DT concept, identifying es…
View article: SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning
SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning Open
Syllogistic reasoning is crucial for Natural Language Inference (NLI). This capability is particularly significant in specialized domains such as biomedicine, where it can support automatic evidence interpretation and scientific discovery.…
View article: Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation
Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation Open
While LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases in a zero-shot setting, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale up i…
View article: An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery Open
We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large La…
View article: An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery Open
We present BioLunar, developed using the Lunar framework, as a tool for supporting biological analyses, with a particular emphasis on molecular-level evidence enrichment for biomarker discovery in oncology. The platform integrates Large La…
View article: Towards Low-Cost Digital Twins for Urban Transportation Systems
Towards Low-Cost Digital Twins for Urban Transportation Systems Open
View article: Large Language Models, Scientific Knowledge and Factuality: A Systematic Analysis in Antibiotic Discovery
Large Language Models, Scientific Knowledge and Factuality: A Systematic Analysis in Antibiotic Discovery Open
View article: Towards Low-Cost Digital Twins for Urban Transportation Systems
Towards Low-Cost Digital Twins for Urban Transportation Systems Open
View article: Public and patient involvement: a survey on knowledge, experience and opinions among researchers within a precision oncology European project
Public and patient involvement: a survey on knowledge, experience and opinions among researchers within a precision oncology European project Open
View article: Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery
Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery Open
Background Inferring over and extracting information from Large Language Models (LLMs) trained on a large corpus of scientific literature can potentially drive a new era in biomedical research, reducing the barriers for accessing existing …
View article: Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation
Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation Open
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…
View article: A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data Open
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 explainab…
View article: Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy
Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy Open
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 coul…
View article: On the Visualisation of Argumentation Graphs to Support Text Interpretation
On the Visualisation of Argumentation Graphs to Support Text Interpretation Open
The recent evolution in Natural Language Processing (NLP) methods, in particular in the field of argumentation mining, has the potential to transform the way we interact with text, supporting the interpretation and analysis of complex disc…
View article: A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data Open
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 explainab…
View article: Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making
Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making Open
View article: Transformers and the Representation of Biomedical Background Knowledge
Transformers and the Representation of Biomedical Background Knowledge Open
Specialized transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We i…
View article: An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves
An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves Open
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 …
View article: A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data Open
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, whic…
View article: Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy
Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy Open
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 promising results in cancer treatment. When emerging, CRS could be ident…
View article: Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital
Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital Open
PURPOSE Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early interve…
View article: Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making Open
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for ML explanation models, when these are pragmatically embedde…
View article: Transformers and the representation of biomedical background knowledge
Transformers and the representation of biomedical background knowledge Open
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora. As such, they have the potential to encode large-scale biological knowledge. We i…
View article: Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital
Biomarker identification using dynamic time warping analysis: a longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital Open
Objectives COVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regar…
View article: Wave comparisons of clinical characteristics and outcomes of COVID-19 admissions - Exploring the impact of treatment and strain dynamics
Wave comparisons of clinical characteristics and outcomes of COVID-19 admissions - Exploring the impact of treatment and strain dynamics Open
View article: Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems
Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems Open
This paper proposes a novel statistical corpus analysis framework targeted towards the interpretation of Natural Language Processing (NLP) architectural patterns at scale. The proposed approach combines saturation-based lexicon constructio…