SOUL: Scala Oversampling and Undersampling Library for imbalance classification

This work has been supported by the research project TIN2017-89517-P, by the UGR research contract OTRI 3940 and by a research scholarship, given to the authors Nestor Rodriguez and David Lopez by the University of Granada, Spain.The improvements in technology and computation have promoted a global adoption of Data Science. It is devoted to extracting significant knowledge from high amounts of information by means of the application of Artificial Intelligence and Machine Learning tools. Among the different tasks within Data Science, classification is probably the most widespread overall. Focusing on the classification scenario, we often face some datasets in which the number of instances for one of the classes is much lower than that of the remaining ones. This issue is known as the imbalanced classification problem, and it is mainly related to the need for boosting the recognition of the minority class examples. In spite of a large number of solutions that were proposed in the specialized literature to address imbalanced classification, there is a lack of open-source software that compiles the most relevant ones in an easy-to-use and scalable way. In this paper, we present a novel software approach named as SOUL, which stands for Scala Oversampling and Undersampling Library for imbalanced classification. The main capabilities of this new library include a large number of different data preprocessing techniques, efficient execution of these approaches, and a graphical environment to contrast the output for the different preprocessing solutions.UGR research contract OTRI 3940University of Granada, SpainTIN2017-89517-

Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms

This work has been partially supported by the Ministry of Science and Technology under project TIN2017-89517-P, the Contract UGR-AM OTRI-4260 and the Andalusian Excellence project P18-FR-4961. J. Carrasco was supported by the Spanish Ministry of Science under the FPU Programme 998758-2016. D. Garcia-Gil holds a contract co-financed by the European Social Fund and the Administration of the Junta de Andalucia, reference DOC_01137.The research in anomaly detection lacks a unified definition of what represents an anomalous instance. Discrepancies in the nature itself of an anomaly lead to multiple paradigms of algorithms design and experimentation. Predictive maintenance is a special case, where the anomaly represents a failure that must be prevented. Related time series research as outlier and novelty detection or time series classification does not apply to the concept of an anomaly in this field, because they are not single points which have not been seen previously and may not be precisely annotated. Moreover, due to the lack of annotated anomalous data, many benchmarks are adapted from supervised scenarios. To address these issues, we generalise the concept of positive and negative instances to intervals to be able to evaluate unsupervised anomaly detection algorithms. We also preserve the imbalance scheme for evaluation through the proposal of the Preceding Window ROC, a generalisation for the calculation of ROC curves for time series scenarios. We also adapt the mechanism from a established time series anomaly detection benchmark to the proposed generalisations to reward early detection. Therefore, the proposal represents a flexible evaluation framework for the different scenarios. To show the usefulness of this definition, we include a case study of Big Data algorithms with a real-world time series problem provided by the company ArcelorMittal, and compare the proposal with an evaluation method.Ministry of Science and Technology TIN2017-89517-PContract UGR-AM OTRI-4260Andalusian Excellence project P18-FR-4961Spanish Government 998758-2016European Social Fund (ESF)Junta de Andalucia DOC_0113

Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study

Anomaly detection is the task of detecting samples that behave differently from the rest of the data or that include abnormal values. Unsupervised anomaly detection is the most common scenario, which implies that the algorithms cannot train with a labeled input and do not know the anomaly behavior beforehand. Histogram-based methods are one of the most approaches in unsupervised anomaly detection, remarking a good performance and a low runtime. Despite the good performance, histogram-based anomaly detectors are not capable of processing data flows while updating their knowledge and cannot deal with a high amount of samples. In this paper, we propose a new histogram-based approach for addressing the aforementioned problems by introducing the ability to update the information inside a histogram. We have applied these strategies to design a new algorithm called Multi-step Histogram Based Outlier Scores (MHBOS), including five new histogram update mechanisms. The results have shown the performance and validity of MHBOS as well as the proposed strategies in terms of performance and computing times.Ministry of Science and Technology under project PID2020-119478 GB-I00Contract UGR-AM OTRI-426Andalusian Excellence project P18-FR-496Spanish Ministry of Science under the FPU Programme 998758-201

Ear, nose and throat manifestations in pemphigus vulgaris

Pemphigus vulgaris (PV) is an autoimmune disease characterized by mucocutaneous intraepithelial blisters and pathogenic autoantibodies against desmoglein 3. There are two clinical forms: mucosal (MPV) and mucocutaneous (MCPV). The frequency of ear, nose and throat (ENT) involvement in PV is not clearly defined. Only a few isolated individual cases have been reported. OBJECTIVES: The objective of our study was to determine the incidence of ENT involvement in patients with PV. PATIENTS: We studied prospectively all 16 patients diagnosed with PV and treated in the Department of Dermatology of the University Clinic of Navarra between 2001 and 2005. They were 10 cases of MPV and six cases of MCPV. All patients were evaluated for ENT manifestations by endoscopic examination. RESULTS: Of the 16 patients, 13 presented with throat symptoms (81%), 12 pharyngeal (75%) and seven laryngeal symptoms (44%). Fourteen patients (88%) had active PV lesions on endoscopic evaluation (eight patients had active lesions on both pharyngeal and laryngeal mucosa, four had PV lesions only on laryngeal mucosa and two had PV lesions on pharyngeal mucosa). Laryngeal lesions were most commonly present in MPV patients. The frequency of nasal symptoms (38%) was lower than active PV lesions (62%) found on ENT examination. Oral symptoms and oral active PV lesions were the most frequent findings (94%). Only three patients with MCPV showed erosions on the external auditory canal. CONCLUSIONS: As ENT endoscopy allows more extensive areas of mucosa to be examined than simple visual inspection, we recommend that it be included in the examination of all patients with PV. By obtaining more complete information concerning the extent of the disease, a more accurate diagnosis can be made, better choice of drug and dose may be decided and, ultimately, response to treatment may be improved

Algoritmos de detección de anomalías y mitigación de falsos positivos en entornos Big Data

Tesis Univ. Granada

Baricitinib reduces 30‐day mortality in older adults with moderate‐to‐severe COVID‐19 pneumonia

Background: Older adults are at the highest risk of severe disease and death due to COVID‐19. Randomized data have shown that baricitinib improves outcomes in these patients, but focused stratified analyses of geriatric cohorts are lacking. Our objective was to analyze the efficacy of baricitinib in older adults with COVID‐19 moderate‐to‐severe pneumonia.Methods: This is a propensity score [PS]‐matched retrospective cohort study. Patients from the COVID‐AGE and Alba‐Score cohorts, hospitalized for moderate‐to‐severe COVID‐19 pneumonia, were categorized in two age brackets of age <70 years old (86 with baricitinib and 86 PS‐matched controls) or ≥70 years old (78 on baricitinib and 78 PS‐matched controls). Thirty‐day mortality rates were analyzed with Kaplan–Meier and Cox proportional hazard models.ResultsMean age was 79.1 for those ≥70 years and 58.9 for those <70. Exactly 29.6% were female. Treatment with baricitinib resulted in a significant reduction in death from any cause by 48% in patients aged 70 or older, an 18.5% reduction in 30‐day absolute mortality risk (n/N: 16/78 [20.5%] baricitinib, 30/78 [38.5%] in PS‐matched controls, p < 0.001) and a lower 30‐day adjusted fatality rate (HR 0.21; 95% CI 0.09–0.47; p < 0.001). Beneficial effects on mortality were also observed in the age group <70 (8.1% reduction in 30‐day absolute mortality risk; HR 0.14; 95% CI 0.03–0.64; p = 0.011).Conclusions: Baricitinib is associated with an absolute mortality risk reduction of 18.5% in adults older than 70 years hospitalized with COVID‐19 pneumonia.</p