Currently, one of the main challenges for information systems in healthcare is focused on support for health professionals regarding disease classifications. This work presents an innovative method for a recommendation system for the diagnosis of breast cancer using patient medical histories. In this proposal, techniques of natural language processing (NLP) were implemented on real datasets: one comprised 160, 560 medical histories of anonymous patients from a hospital in Chile for the following categories: breast cancer, cysts and nodules, other cancer, breast cancer surgeries and other diagnoses; and the other dataset was obtained from the MIMIC III dataset. With the application of word-embedding techniques, such as word2vec’s skip-gram and BERT, and machine learning techniques, a recommendation system as a tool to support the physician’s decision-making was implemented. The obtained results demonstrate that using word embeddings can define a good-quality recommendation system. The results of 20 experiments with 5-fold cross-validation for anamnesis written in Spanish yielded an F1 of 0.980 ± 0.0014 on the classification of ‘cancer’ versus ‘not cancer’ and 0.986 ± 0.0014 for ‘breast cancer’ versus ‘other cancer’. Similar results were obtained with the MIMIC III dataset.

Biomedical text classification algorithms, which currently support clinical decision-making processes, call for expensive training texts due to the low availability of labeled corpus and the cost of manual annotation by specialized professionals. The active learning (AL) approach to classification heavily lessens such cost by reducing the number of labeled documents required to achieve specified performance. This article introduces a query strategy and a stopping criterion that transform CREGEX, a regular-expressions-based text classification algorithm, in an AL biomedical text classifier. The query strategy samples the training dataset, trading off the greedy learning achieved by the regular expressions classification precision and the conservative learning induced by text sequence alignment classification. The sustained reduction in the variance of the query strategy scores is used as a stopping criterion. The AL classifier was compared with Support Vector Machine (SVM), Naïve Bayes (NB), and a classifier based on Bidirectional Encoder Representations from Transformers (BERT), using three datasets with biomedical information in Spanish on smoking habits, obesity, and obesity types. The learning curve results indicate that AL in CREGEX allowed to efficiently reduce the number of training examples for equal performance than the rest of the classifiers, obtaining areas under the learning curve greater than 85% in all cases. The stopping criterion applied to the AL process allowed to use, on average, approximately 32% to 50% of the total training examples with differences in performance concerning the maximum value of the learning curve not exceeding 2%. This performance demonstrates the effectiveness of using AL in a biomedical text classifier based on regular expressions, which is attributable to such expressions' ability to represent intricate sequential patterns in training texts considered most informative.

High accuracy text classifiers are used nowadays in organizing large amounts of biomedical information and supporting clinical decision-making processes. In medical informatics, regular expression-based classifiers have emerged as an alternative to traditional, discriminative classification algorithms due to their ability to model sequential patterns. This article presents CREGEX (Classifier Regular Expression), a biomedical text classifier based on an automatically generated regular-expressions-based feature space. We conceived an algorithm for automatically constructing an informative and discriminative regular-expressions-based feature space, suitable for binary and multiclass discrimination problems. Regular expressions are automatically generated from training texts using a coarse-to-fine text aligning method, which trades off the lexical variants of words, in terms of gender and grammatical number, and the generation of a feature space containing a large number of noisy features. CREGEX carries out feature selection by filtering keywords and also computes a confidence metric to classify test texts. Three de-identified datasets in Spanish, with information on smoking habits, obesity, and obesity types, were used here to assess the performance of CREGEX. For comparison, Support Vector Machine (SVM) and Naïve Bayes (NB) supervised classifiers were also trained with consecutive sequences of tokens (n-grams) as features. Results show that, in all the datasets used for evaluation, CREGEX not only outperformed both the SVM and NB classifiers in terms of accuracy and F-measure (p-value<; 0.05) but also used a fewer amount of training examples to achieve the same performance. Such a superior performance is attributed to the regular expressions' ability to represent complex text patterns.

Vegetatively propagated clones accumulate somatic mutations. The purpose of this study was to better understand the consequences of clonal propagation and involved defining the nature of somatic mutations throughout the genome. Fifteen Zinfandel winegrape clone genomes were sequenced and compared to one another using a highly contiguous genome reference produced from one of the clones, Zinfandel 03. Though most heterozygous variants were shared, somatic mutations accumulated in individual and subsets of clones. Overall, heterozygous mutations were most frequent in intergenic space and more frequent in introns than exons. A significantly larger percentage of CpG, CHG, and CHH sites in repetitive intergenic space experienced transition mutations than genic and non-repetitive intergenic spaces, likely because of higher levels of methylation in the region and the increased likelihood of methylated cytosines to spontaneously deaminate. Of the minority of mutations that occurred in exons, larger proportions of these were putatively deleterious when they occurred in relatively few clones. These data support three major conclusions. First, repetitive intergenic space is a major driver of clone genome diversification. Second, clonal propagation is associated with the accumulation of putatively deleterious mutations. Third, the data suggest selection against deleterious variants in coding regions such that mutations are less frequent in coding than noncoding regions of the genome.

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of all years per patient for each variable (123 variables). The second set of variables was obtained from the mode every three years (369 variables). The results show that the random forest trained with the second set of variables has the best performance (89% accuracy), which is better than other reported results in the literature.