Clinical data generated in healthcare systems is increasingly recognized as a key resource for biomedical research, healthcare optimization, and population health monitoring. However, its full potential remains underexploited due to fragmentation, heterogeneity, and lack of interoperability between data sources. The ELADAIS project addresses this challenge by designing, developing, and deploying a scalable, modular, and interoperable technological platform for the extraction, transformation, storage, and advanced analysis of high-impact clinical data. Grounded in the OMOP Common Data Model (OMOP-CDM), ELADAIS integrates a microservice-based architecture, analytical environments, workflow orchestration, and federated data capabilities. The platform will be deployed at two major hospitals in Madrid, Spain, with the expectation of standardizing access to over 1 million patient records and more than 19 million clinical events. This paper presents the architectural principles and expectations of ELADAIS, highlighting its potential to accelerate reproducible and collaborative clinical research.

Abstract In recent years, there has been a remarkable surge in the development of Natural Language Processing (NLP) models, particularly in the realm of Named Entity Recognition (NER). Models such as BERT have demonstrated exceptional performance, leveraging annotated corpora for accurate entity identification. However, the question arises: Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR). This study utilized a dataset of Spanish EHRs related to breast cancer and implemented both a traditional NER method using BERT, and a contemporary approach that combines few shot learning and integration of external knowledge, driven by LLMs using GPT, to structure the data. The analysis involved a comprehensive pipeline that included these methods. Key performance metrics, such as precision, recall, and F-score, were used to evaluate the effectiveness of each method. This comparative approach aimed to highlight the strengths and limitations of each method in the context of structuring Spanish EHRs efficiently and accurately.The comparative analysis undertaken in this article demonstrates that both the traditional BERT-based NER method and the few-shot LLM-driven approach, augmented with external knowledge, provide comparable levels of precision in metrics such as precision, recall, and F score when applied to Spanish EHR. Contrary to expectations, the LLM-driven approach, which necessitates minimal data annotation, performs on par with BERT’s capability to discern complex medical terminologies and contextual nuances within the EHRs. The results of this study highlight a notable advance in the field of NER for Spanish EHRs, with the few shot approach driven by LLM, enhanced by external knowledge, slightly edging out the traditional BERT-based method in overall effectiveness. GPT’s superiority in F-score and its minimal reliance on extensive data annotation underscore its potential in medical data processing.

This study explores a multilingual transfer learning strategy for Named Entity Recognition (NER) in mammography radiology reports, aiming to improve breast cancer diagnosis. By utilizing a dataset from TecSalud, which includes mammograms and Electronic Health Records (EHRs) over ten years, this study seeks to address the linguistic barriers in medical documentation through advanced Natural Language Processing (NLP) models. Our approach involves meticulously labeling twenty-four distinct entities within the predominantly Spanish dataset, covering a range of diagnostic features and interpretive findings, highlighting the challenge of linguistic diversity in medical records and the potential of NLP to bridge this gap.The results demonstrate that fine-tuning on the last layer offers a balanced approach between simplicity and accuracy, avoiding overfitting and achieving state-of-art results.

In the rapidly evolving field of biomedical text mining, the extraction of phenotypic entities from unstructured texts remains a pivotal challenge. This paper introduces a novel method that leverage Large Language Models (LLMs) to extract phenotypical entities from freely available texts such as Wikipedia. Our approach goes beyond traditional Named Entity Recognition (NER) techniques by utilizing both local and cloud-based LLMs. We present a comprehensive comparison with state-of-the-art tools. Our study confirms the significant advantages of LLMs in identifying relevant phenotypic entities, thus enhancing the ability of researchers and clinicians to understand and respond to disease dynamics more effectively. Therefore, this work underscores the potential of next-generation LLMs to redefine the standards for the extraction of phenotypic entities in biomedical research.

Using Natural Language Processing (NLP) in the clinical domain has increased the possibility of automatically extracting information from oncology clinical narratives. Specifically, deep learning methods have been used to extract information in the cancer domain. However, most of the above proposals have concentrated only on extracting named entities from clinical narratives, but those proposals do not include a methodology for structuring the information after an information extraction step. In this paper, we propose an automatic pipeline based on deep learning for structuring breast cancer information from clinical narratives written in Spanish. The pipeline inputs a set of clinical documents written in narrative form and automatically generates a structured JSON file that contains the information for each patient. This pipeline integrates both clinical entity extraction and negation and uncertainty detection. Obtained results have shown that deep learning methods are feasible for structuring information in the breast cancer domain.

The wide adoption of electronic health records (EHRs) offers immense potential as a source of support for clinical research. However, previous studies focused on extracting only a limited set of medical concepts to support information extraction in the cancer domain for the Spanish language. Building on the success of deep learning for processing natural language texts, this paper proposes a transformer-based approach to extract named entities from breast cancer clinical notes written in Spanish and compares several language models. To facilitate this approach, a schema for annotating clinical notes with breast cancer concepts is presented, and a corpus for breast cancer is developed. Results indicate that both BERT-based and RoBERTa-based language models demonstrate competitive performance in clinical Named Entity Recognition (NER). Specifically, BETO and multilingual BERT achieve F-scores of 93.71% and 94.63%, respectively. Additionally, RoBERTa Biomedical attains an F-score of 95.01%, while RoBERTa BNE achieves an F-score of 94.54%. The findings suggest that transformers can feasibly extract information in the clinical domain in the Spanish language, with the use of models trained on biomedical texts contributing to enhanced results. The proposed approach takes advantage of transfer learning techniques by fine-tuning language models to automatically represent text features and avoiding the time-consuming feature engineering process.

The wide adoption of electronic health records (EHRs) provides a potential source to support clinical research. The Bidirectional Encoder Representations from Transformers (BERT) has shown promising results in extracting information in the biomedical domain, including the cancer field. However, one of the challenges in the cancer domain is annotating resources to support information extraction. In this paper, we will show how models trained in a lung cancer corpus can be used to extract cancer concepts even in other cancer types. In particular, we will show the performance of BERT models on breast cancer data that was not used to train the models. Results are very promising as they show the possibility of applying deep learning-based models to predict cancer concepts in a different dataset to the one they were trained on, representing a considerable save of time and resources.

Quality indices in clustering are used not only to assess the quality of the partitions but also to determine the number of clusters in the final result. When these indices are evaluated in a case study, real data conditions or different clustering a

Traditionally, clinical data have been used as the only source of information to diagnose diseases. Nowadays, other types of information, such as various forms of omics data (e.g. DNA microarrays), are taken into account to improve diagnosis and even

The correct choice of an evolutionary algorithm, a genetic repre-sentation for the problem being solved (as well as their associated variation operators) and the appropriate values for the parameters of the algorithm is a hard task and it is often considered as an opti-mization problem itself. In this contribution, we propose a new theoretical formalism, called Multiple Offspring Sampling (MOS). This new technique combines different evolutionary approaches taking advantage of the benefits provided by each of them. MOS dynamically bal-ances the participation of different mechanisms to spawn the new offspring population, according to the benefits provided by each of them in previous generations. This approach evaluates multiple offspring generation methods (for example different coding strate-gies), and configures appropriate sampling sizes. This formalism has been applied to a well-known permutation problem, the traveling salesman problem (TSP). The results on sev-eral instances of this problem show that most of the combined tech-niques outperform the results obtained by single ones.

Deceptive problems have always been considered difficult for Genetic Algorithms. To cope with this characteristic, the literature has proposed the use of Parallel Genetic Algorithms (PGAs), particularly multi-population island-based models. Although the existence of multiple populations encourages population diversity, these problems are still difficult to solve. This paper introduces a new initialization mechanism for each of the populations of the islands based on Voronoi cells. In order to analyze the results, a series of different experiments using several real-value deceptive problems and a set of representative parameters (migration ratio, migration frequency and connectivity) have been chosen. The results obtained suggest that the Voronoi initialization method improves considerably the performance obtained with a traditionally uniform random initialization.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

MAPFS is a parallel file system integrated with a multiagent system responsible for the information retrieval. One of the fields where the agents can be very useful is precisely in the development of information recovery systems. The usage of a multiagent system implies coordination among the agents that belong to such system. The main goal of the agent cooperation is the interaction among them for achieving a common objective in a distributed system. Thus, a communication framework must be provided. This paper shows the MAPFS cooperation model and its communication framework, emphasizing its relation with the whole system.

Recommender systems emerged to help users choose among the large amount of options that ecommerce sites offer. Collaborative filtering is one of the most successful recommender techniques. Here we propose an approach to collaborative filtering based on the simple Bayesian classifier. We propose a method of increasing the efficiency of naive Bayes by applying a new semi naive Bayes approach based on interval estimation. To evaluate our algorithm we use a database of Microsoft anonymous Web data from the UCl repository. Our empirical results show that our proposed Interval based naive Bayes approach outperforms typical naive Bayes.