Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes

Background: In spite of numerous research efforts on supporting the therapy of diabetes mellitus, the subject still involves challenges and creates active interest among researchers. In this paper, a decision support tool is presented for setting insulin therapy in new-onset type 1 diabetes. Methods: The concept of differential sequential patterns (DSPs) is introduced with the aim of representing deviations in the patient's blood glucose level (BGL) and the amount of insulin injections administered. The decision support tool is created using data mining algorithms for discovering sequential patterns. Results: By using the DSPs, it is possible to support the physician's decisionmaking concerning changing the treatment (i.e., whether to increase or decrease the insulin dosage). The other contributions of the paper are an algorithm for generating DSPs and a new method for evaluating nocturnal glycaemia. The proposed qualitative evaluation of nocturnal glycaemia improves the generalization capabilities of the DSPs. Conclusions: The usefulness of the proposed approach was evident in the results of experiments in which juvenile diabetic patients actual data were used. It was confirmed that the proposed DSPs can be used to guide the therapy of numerous juvenile patients with type 1 diabetes

Caracterización espacio temporal de la ecofisiología de la "apodanthera biflora" utilizando minería de patrones secuenciales

En los últimos años, los investigadores del Laboratorio de Ecología Evolutiva de la Universidad Peruana Cayetano Heredia (UPCH) han venido estudiando especies nativas del Bosque Seco Ecuatorial del norte del Perú. Este es el caso de la Apodanthera Biflora, raíz comestible de potencial uso alimentario e industrial. Con la finalidad de desarrollar planes de sostenibilidad y preservación de la especie, los expertos requieren realizar estudios más extensos sobre los factores que afectan las características nutricionales e industriales de la especie. Para determinar estos factores se deben descubrir correlaciones temporales a partir de fuentes de datos heterogéneas. Debido a la dificultad de explotar este tipo de datos no estandarizados ni agrupados, los métodos estadísticos tradicionales no son suficientes, por lo que se requiere herramientas permitan al experto identificar qué correlaciones temporales representan patrones frecuentes relevantes. El presente trabajo evalúa el uso de las técnicas de minería de patrones secuenciales y visualización espacial, con el objetivo de determinar si su aplicación facilita la obtención de patrones frecuentes relevantes a partir de distintas fuentes de datos heterogéneos relacionados a la Apodanthera Biflora. Para lograr este objetivo, se utiliza una metodología basada en el Descubrimiento de Conocimiento a partir de Bases de Datos (KDD por sus siglas en inglés), el cuál define fases para la selección, pre procesamiento, transformación, minería y evaluación (visualización) de los datos. Los resultados obtenidos demostraron que la técnica de minería de patrones secuenciales PrefixSpan y la visualización espacial, utilizando librerías de Google Maps API y D3 Js, permitieron a los expertos la obtención de patrones frecuentes relevantes. Así mismo, la técnica de transformación GIS para datos geográficos, y la técnica de discretización por entropía y frecuencia, han permitido el pre procesamiento de datos heterogéneos. A partir de las correlaciones descubiertas, los expertos identificaron patrones frecuentes relevantes, en las localidades de Chulucanas, Cerrato, El Morante, P. Mora y El Porvenir; principalmente relacionados a las características del suelo, precipitaciones y composición química de la raíz.Tesi

The evaluation of occupational accident with sequential pattern mining

Accidents in manufacturing systems greatly affect productivity and efficiency, which are well known perfor-mance indicaters in practice. Therefore, it is very important to know the sequential patterns among the accidents to avode possible losses decrasing performance of the manufacturing systems. In order to reduce accidents, it is necessary to determine the patterns that cause the accident first. The associations among the causes of the occurrence of accidents is rarely investigated in the literature. To fill this gap, the patterns of causes among the accidents in the manufacturing system are revealed by using sequential pattern mining in this study. The most important contribution of this study is the discovery of sequential patterns formed by accident characteristics of pre-accident, moment of accident and post-accident stages unlike traditional accident investigation methods. Additionally, knowing the patterns of causes among the accidents can help decision makers to prepare a more proactive security program in real life. The CloFast algorithm is performed to go into the details of accidents in manufacturing systems. Accident records induding data between 2013 and 2019 are used to discover the sequential patterns. The results of this study showed that each accidents has its own sequential accident patterns and it is also posible to prevent possible accidents and reduce losses due to accidents considering sequential patterns in real life. Safety engineers and occupational safety specialists should take into account the sequential patterns among the accidents to avoid similar accident in the near future

Analysis of Medical Pathways by means of Frequent Closed Sequences

Analysing sequential medical data to detect hidden patterns has recently received great attention in a variety of applications. This paper addresses the analysis of patients’ exam log data to rebuild from operational data an image of the steps of the medical treatment process. The analysis is performed on the medical treatment of diabetic patients provided by a Local Sanitary Agency in Italy. The extracted knowledge allows highlighting medical pathways typically adopted for specific diseases, as well as discovering deviations with respect to them, which can indicate alternative medical treatments, medical/patient negligence or incorrect procedures for data collection. Detected medical pathways include both the sets of exams which are frequently done together, and the sequences of exam sets frequently followed by patients. The proposed approach is based on the extraction of the frequent closed sequences, which provide, in a compact form, the medical pathways of interest