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Background and Objectives: There is an increasing and renewed interest in Electronic Health Records (EHRs) as a substantial information source for clinical decision making. Consequently, automatic de-identification of EHRs is an indispensable task, since their dissociation from personal data is a necessary prerequisite for their dissemination. Nevertheless, the bulk of prior research in this domain has been conducted using English EHRs, given the limited availability of annotated corpora in other languages, including Spanish., Methods: In this study, the automatic de-identification of medical documents in Spanish was explored. A private corpus comprising 599 genuine clinical cases was annotated with eight different categories of protected health information. The prediction problem was approached as a named entity recognition task and two deep learning-based methodologies were developed. The first strategy was based on recurrent neural networks (RNN) and the second, an end-to-end approach, was based on Transformers. In addition, we have implemented a procedure to expand the amount of texts employed for model training., Results: Our findings demonstrate that Transformers surpass RNNs in the de-identification of clinical data in Spanish. Particularly noteworthy is the excellent performance of the XLM-RoBERTa large Transformer, achieving a rigorous strict-match micro-average of 0.946 for precision, 0.954 for recall, and an F1 score of 0.95 when applied to the amplified version of the corpus. Furthermore, a web-based application has been created to assist specialized clinicians in de-identifying EHRs through the aid of the implemented models., Conclusion: The study's conclusions showcase the practical applicability of the state-of-the-art Transformers models for precise de-identification of clinical notes in real-world medical settings in Spanish, with the potential to improve performance if continual pre-training strategies are implemented., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare facility is essential in restraining the epidemic and working toward its eventual elimination. Most state-of-the-art proposals employ machine learning (ML) methods and structured data to enhance HIV diagnoses, however, there is a dearth of recent proposals utilizing unstructured textual data from Electronic Health Records (EHRs). In this work, we propose to use only the unstructured text of the clinical notes as evidence for the classification of patients as suspected or not suspected. For this purpose, we first compile a dataset of real clinical notes from a hospital with patients classified as suspects and non-suspects of having HIV. Then, we evaluate the effectiveness of two types of classification models to identify patients suspected of being infected with the virus: classical ML algorithms and two Large Language Models (LLMs) from the biomedical domain in Spanish. The results show that both LLMs outperform classical ML algorithms in the two settings we explore: one dataset version is balanced, containing an equal number of suspicious and non-suspicious patients, while the other reflects the real distribution of patients in the hospital, being unbalanced. We obtain F 1 score figures of 94.7 with both LLMs in the unbalanced setting, while in the balance one, RoBERTa Bio model outperforms the other one with a F 1 score of 95.7. The findings indicate that leveraging unstructured text with LLMs in the biomedical domain yields promising outcomes in diminishing missed opportunities for HIV diagnosis. A tool based on our system could assist a doctor in deciding whether a patient in consultation should undergo a serological test., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The development of intelligent operating rooms is an example of a cyber-physical system resulting from the symbiosis of Industry 4.0 and medicine. A problem with this type of systems is that it requires demanding solutions that allow the real time acquisition of heterogeneous data in an efficient way. The aim of the presented work is the development of a data acquisition system, based on a real-time artificial vision algorithm which can capture information from different clinical monitors. The system was designed for the registration, pre-processing, and communication of clinical data recorded in an operating room. The methods for this proposal are based on a mobile device running a Unity application, which extracts information from clinical monitors and transmits the data to a supervision system through a wireless Bluetooth connection. The software implements a character detection algorithm and allows online correction of identified outliers. The results validate the system with real data obtained during surgical interventions, where only 0.42% values were missed and 0.89% were misread. The outlier detection algorithm was able to correct all the reading errors. In conclusion, the development of a low-cost compact solution to supervise operating rooms in real-time, collecting visual information non-intrusively and communicating data wirelessly, can be a very useful tool to overcome the lack of expensive data recording and processing technology in many clinical situations. The acquisition and pre-processing method presented in this article constitutes a key element towards the development of a cyber-physical system for the development of intelligent operating rooms., Competing Interests: Declaration of Competing Interest None Declared, (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Among all the cancers known today, prostate cancer is one of the most commonly diagnosed in men. With modern advances in medicine, its mortality has been considerably reduced. However, it is still a leading type of cancer in terms of deaths. The diagnosis of prostate cancer is mainly conducted by biopsy test. From this test, Whole Slide Images are obtained, from which pathologists diagnose the cancer according to the Gleason scale. Within this scale from 1 to 5, grade 3 and above is considered malignant tissue. Several studies have shown an inter-observer discrepancy between pathologists in assigning the value of the Gleason scale. Due to the recent advances in artificial intelligence, its application to the computational pathology field with the aim of supporting and providing a second opinion to the professional is of great interest., Method: In this work, the inter-observer variability of a local dataset of 80 whole-slide images annotated by a team of 5 pathologists from the same group was analyzed at both area and label level. Four approaches were followed to train six different Convolutional Neural Network architectures, which were evaluated on the same dataset on which the inter-observer variability was analyzed., Results: An inter-observer variability of 0.6946 κ was obtained, with 46% discrepancy in terms of area size of the annotations performed by the pathologists. The best trained models achieved 0.826±0.014κ on the test set when trained with data from the same source., Conclusions: The obtained results show that deep learning-based automatic diagnosis systems could help reduce the widely-known inter-observer variability that is present among pathologists and support them in their decision, serving as a second opinion or as a triage tool for medical centers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Niemann-Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%-50%, an F 1 score of 6%-30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 disease., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background and Objective: Infertility affects approximately one in ten couples, and almost half of the infertility cases are due to the malefactor. To diagnose infertility and determine future treatment, a semen analysis is performed. Evaluation of sperm morphology is one of several steps in semen analysis, in which the shape and size of sperm parts are examined. The laboratories dedicated to this use traditional methods susceptible to errors. An alternative to replace the poor visual ability to assess sperm size and shape is to analyze sperm morphology with a computer's help. However, since the automatic sperm classification rates do not show an acceptable precision rate for use in the clinical setting, it is considered an exciting approach to focus efforts on improving the precision in sperm segmentation to extract the contour sperm before classification. This work aims to assess the utility of two image segmentation deep learning models for segmenting human sperm heads, acrosome, and nucleus., Methods: In this work, we evaluate the use of two well-known deep learning architectures (U-Net and Mask-RCNN) to segment parts of human sperm cells using data augmentation, cross-validation, hyperparameter tuning, and transfer learning. The experimental results are carried out using SCIAN-SpermSegGS, a public dataset with more than two hundred manually segmented sperm cells and widely used to validate segmentation methods of human sperm parts., Results: Experimental evaluation shows that U-net with transfer learning achieves up to 95% overlapping against hand-segmented masks for sperm head (0.96), acrosome (0.94), and nucleus (0.95), using Dice coefficient as the evaluation metric. These results outperform state-of-the-art sperm parts segmentation methods., Conclusions: The impact of transfer learning is substantial, significantly improving the results of state-of-the-art methods with a higher Dice coefficient, less dispersion, and fewer cases where the model failed to segment sperm parts. These results represent a promising advance in the ultimate goal of performing computer-assisted morphological sperm analysis., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. Artificial Intelligence algorithms have had a huge impact on medical image analysis, including digital histopathology, where Convolutional Neural Networks (CNNs) are used to provide a fast and accurate diagnosis, supporting experts in this task. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Due to the size of these images, neural networks cannot use them as input and, therefore, small subimages called patches are extracted and predicted, obtaining a patch-level classification. In this work, a novel patch aggregation method based on a custom Wide & Deep neural network model is presented, which performs a slide-level classification using the patch-level classes obtained from a CNN. The malignant tissue ratio, a 10-bin malignant probability histogram, the least squares regression line of the histogram, and the number of malignant connected components are used by the proposed model to perform the classification. An accuracy of 94.24% and a sensitivity of 98.87% were achieved, proving that the proposed system could aid pathologists by speeding up the screening process and, thus, contribute to the fight against PCa., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

MiRNAs are emerging as key molecules to study neuropsychiatric diseases. However, despite the large number of methodologies and software for miRNA-seq analyses, there is little supporting literature for researchers in this area. This review focuses on evaluating how miRNA-seq has been used to study neuropsychiatric diseases to date, analyzing both the main findings discovered and the bioinformatics workflows and tools used from a methodological perspective. The objective of this review is two-fold: first, to evaluate current miRNA-seq procedures used in neuropsychiatry; and second, to offer comprehensive information that can serve as a guide to new researchers in bioinformatics. After conducting a systematic search (from 2016 to June 30, 2020) of articles using miRNA-seq in neuropsychiatry, we have seen that it has already been used for different types of studies in three main categories: diagnosis, prognosis, and mechanism. We carefully analyzed the bioinformatics workflows of each study, observing a high degree of variability with respect to the tools and methods used and several methodological complexities that are identified and discussed in this review., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Measuring the level of analgesia to adapt the opioids infusion during anesthesia to the real needs of the patient is still a challenge. This is a consequence of the absence of a specific measure capable of quantifying the nociception level of the patients. Unlike existing proposals, this paper aims to evaluate the suitability of the Analgesia Nociception Index (ANI) as a guidance variable to replicate the decisions made by the experts when a modification of the opioid infusion rate is required. To this end, different machine learning classifiers were trained with several sets of clinical features. Data for training were captured from 17 patients undergoing cholecystectomy surgery. Satisfactory results were obtained when including information about minimum values of ANI for predicting a change of dose. Specifically, a higher efficiency of the Support Vector Machine (SVM) classifier was observed compared with the situation in which the ANI index was not included: accuracy: 86.21% (83.62%-87.93%), precision: 86.11% (83.78%-88.57%), recall: 91.18% (88.24%-91.18%), specificity: 79.17% (75%-83.33%), AUC: 0.89 (0.87-0.90) and kappa index: 0.71 (0.66-0.75). The results of this research evidenced that including information about the minimum values of ANI together with the hemodynamic information outperformed the decisions made regarding only non-specific traditional signs such as heart rate and blood pressure. In addition, the analysis of the results showed that including the ANI monitor in the decision making process may anticipate a dose change to prevent hemodynamic events. Finally, the SVM was able to perform accurate predictions when making different decisions commonly observed in the clinical practice., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills., Methods: Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results., Results: DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis., Conclusions: The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Biclustering has emerged as an important method for analyzing gene expression data from microarray technology. It allows to identify groups of genes which behave similarly under a subset of conditions. As a gene may play more than one biological role in conjunction with distinct groups of genes, non-exclusive biclustering algorithms are required. In this paper we propose a new method to obtain potentially-overlapping biclusters, the Possibilistic Spectral Biclustering algorithm (PSB), based on Fuzzy Technology and Spectral Clustering. We tested our method on S. cerevisiae cell cycle expression data and on a human cancer dataset, validating the obtained biclusters using known classifications of conditions and GO Term Finder for functional annotations of genes. Results are available at http://decsai.ugr.es/ approximately ccano/psb.