Jose Sousa

Luca Gherardini, Varun Ravi Varma, Karol Capala et al.

The availability of large data sets is providing an impetus for driving current artificial intelligent developments. There are, however, challenges for developing solutions with small data sets due to practical and cost-effective deployment and the opacity of deep learning models. The Comprehensive Abstraction and Classification Tool for Uncovering Structures called CACTUS is presented for improved secure analytics by effectively employing explainable artificial intelligence. It provides additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It shows to the user the frequency of the attributes in each class and ranks them by their discriminative power. Its performance is assessed by application to the Wisconsin diagnostic breast cancer and Thyroid0387 data sets.

Justyna Andrys-Olek, Paulina Tworek, Luca Gherardini et al.

Machine learning models are increasingly applied to biomedical data, yet their adoption in high stakes domains remains limited by poor robustness, limited interpretability, and instability of learned features under realistic data perturbations, such as missingness. In particular, models that achieve high predictive performance may still fail to inspire trust if their key features fluctuate when data completeness changes, undermining reproducibility and downstream decision-making. Here, we present CACTUS (Comprehensive Abstraction and Classification Tool for Uncovering Structures), an explainable machine learning framework explicitly designed to address these challenges in small, heterogeneous, and incomplete clinical datasets. CACTUS integrates feature abstraction, interpretable classification, and systematic feature stability analysis to quantify how consistently informative features are preserved as data quality degrades. Using a real-world haematuria cohort comprising 568 patients evaluated for bladder cancer, we benchmark CACTUS against widely used machine learning approaches, including random forests and gradient boosting methods, under controlled levels of randomly introduced missing data. We demonstrate that CACTUS achieves competitive or superior predictive performance while maintaining markedly higher stability of top-ranked features as missingness increases, including in sex-stratified analyses. Our results show that feature stability provides information complementary to conventional performance metrics and is essential for assessing the trustworthiness of machine learning models applied to biomedical data. By explicitly quantifying robustness to missing data and prioritising interpretable, stable features, CACTUS offers a generalizable framework for trustworthy data-driven decision support.

Paulina Tworek, Miłosz Bargieł, Yousef Khan et al.

Extracting structured medical insights from unstructured clinical text using Natural Language Processing (NLP) remains an open challenge in healthcare, particularly in non-English contexts where resources are scarce. This study presents a comparative analysis of NLP low-compute rule-based methods and Large Language Models (LLMs) for information extraction from electronic health records (EHR) obtained from the Voivodeship Rehabilitation Hospital for Children in Ameryka, Poland. We evaluate both approaches by extracting patient demographics, clinical findings, and prescribed medications while examining the effects of lack of text normalisation and translation-induced information loss. Results demonstrate that rule-based methods provide higher accuracy in information retrieval tasks, particularly for age and sex extraction. However, LLMs offer greater adaptability and scalability, excelling in drug name recognition. The effectiveness of the LLMs was compared with texts originally in Polish and those translated into English, assessing the impact of translation. These findings highlight the trade-offs between accuracy, normalisation, and computational cost when deploying NLP in healthcare settings. We argue for hybrid approaches that combine the precision of rule-based systems with the adaptability of LLMs, offering a practical path toward more reliable and resource-efficient clinical NLP in real-world hospitals.

Luca Gherardini, Imre Lengyel, Tunde Peto et al.

Machine Learning (ML) is used to tackle various tasks, such as disease classification and prediction. The effectiveness of ML models relies heavily on having large amounts of complete data. However, healthcare data is often limited or incomplete, which can hinder model performance. Additionally, issues like the trustworthiness of solutions vary with the datasets used. The lack of transparency in some ML models further complicates their understanding and use. In healthcare, particularly in the case of Age-related Macular Degeneration (AMD), which affects millions of older adults, early diagnosis is crucial due to the absence of effective treatments for reversing progression. Diagnosing AMD involves assessing retinal images along with patients' symptom reports. There is a need for classification approaches that consider genetic, dietary, clinical, and demographic factors. Recently, we introduced the -Comprehensive Abstraction and Classification Tool for Uncovering Structures-(CACTUS), aimed at improving AMD stage classification. CACTUS offers explainability and flexibility, outperforming standard ML models. It enhances decision-making by identifying key factors and providing confidence in its results. The important features identified by CACTUS allow us to compare with existing medical knowledge. By eliminating less relevant or biased data, we created a clinical scenario for clinicians to offer feedback and address biases.

Alfredo Ibias, Karol Capala, Varun Ravi Varma et al.

Noise is a fundamental problem in learning theory with huge effects in the application of Machine Learning (ML) methods, due to real world data tendency to be noisy. Additionally, introduction of malicious noise can make ML methods fail critically, as is the case with adversarial attacks. Thus, finding and developing alternatives to improve robustness to noise is a fundamental problem in ML. In this paper, we propose a method to deal with noise: mitigating its effect through the use of data abstractions. The goal is to reduce the effect of noise over the model's performance through the loss of information produced by the abstraction. However, this information loss comes with a cost: it can result in an accuracy reduction due to the missing information. First, we explored multiple methodologies to create abstractions, using the training dataset, for the specific case of numerical data and binary classification tasks. We also tested how these abstractions can affect robustness to noise with several experiments that explore the robustness of an Artificial Neural Network to noise when trained using raw data \emph{vs} when trained using abstracted data. The results clearly show that using abstractions is a viable approach for developing noise robust ML methods.

Karol Capała, Paulina Tworek, Jose Sousa

In a world where Machine Learning (ML) is increasingly deployed to support decision-making in critical domains, providing decision-makers with explainable, stable, and relevant inputs becomes fundamental. Understanding how machine learning works under missing data and how this affects feature variability is paramount. This is even more relevant as machine learning approaches focus on standardising decision-making approaches that rely on an idealised set of features. However, decision-making in human activities often relies on incomplete data, even in critical domains. This paper addresses this gap by conducting a set of experiments using traditional machine learning methods that look for optimal decisions in comparison to a recently deployed machine learning method focused on a classification that is more descriptive and mimics human decision making, allowing for the natural integration of explainability. We found that the ML descriptive approach maintains higher classification accuracy while ensuring the stability of feature selection as data incompleteness increases. This suggests that descriptive classification methods can be helpful in uncertain decision-making scenarios.