The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.

Additional file 1. Spark application of the e-learning recommender system.

Generated datasets for frequent itemset mining algorithms Apriori, Eclat, and FP-Growth.

Mined rules by FP-Growth algorithm.

This dataset presents a collection of co-existence features extracted from the original CICMalDroid 2020 using the FP-Growth algorithm. The co-existence features are combinations of two features that frequently occur together within the dataset.

Additional file 1. Table 1. Disease generalization in ICD-10 codes. Table 2. Comparison among OMOP ID, Concept Code and the generlization ICD-10 codes. Table 3. The rules verified by literatures. Table 4. The rules discoveried by FP-growth algorithm.

BackgroundConcerns about the role of chronically used medications in the clinical outcomes of the coronavirus disease 2019 (COVID-19) have remarkable potential for the breakdown of non-communicable diseases (NCDs) management by imposing ambivalence toward medication continuation. This study aimed to investigate the association of single or combinations of chronically used medications in NCDs with clinical outcomes of COVID-19.MethodsThis retrospective study was conducted on the intersection of two databases, the Iranian COVID-19 registry and Iran Health Insurance Organization. The primary outcome was death due to COVID-19 hospitalization, and secondary outcomes included length of hospital stay, Intensive Care Unit (ICU) admission, and ventilation therapy. The Anatomical Therapeutic Chemical (ATC) classification system was used for medication grouping. The frequent pattern growth algorithm was utilized to investigate the effect of medication combinations on COVID-19 outcomes.FindingsAspirin with chronic use in 10.8% of hospitalized COVID-19 patients was the most frequently used medication, followed by Atorvastatin (9.2%) and Losartan (8.0%). Adrenergics in combination with corticosteroids inhalants (ACIs) with an odds ratio (OR) of 0.79 (95% confidence interval: 0.68–0.92) were the most associated medications with less chance of ventilation therapy. Oxicams had the least OR of 0.80 (0.73–0.87) for COVID-19 death, followed by ACIs [0.85 (0.77–0.95)] and Biguanides [0.86 (0.82–0.91)].ConclusionThe chronic use of most frequently used medications for NCDs management was not associated with poor COVID-19 outcomes. Thus, when indicated, physicians need to discourage patients with NCDs from discontinuing their medications for fear of possible adverse effects on COVID-19 prognosis.

Introduction: Motilin (MLN) is a gastrointestinal (GI) hormone produced in the upper small intestine. Its most well understood function is to participate in Phase III of the migrating myoelectric complex component of GI motility. Changes in MLN availability are associated with GI diseases such as gastroesophageal reflux disease and functional dyspepsia. Furthermore, herbal medicines have been used for several years to treat various GI disorders. We systematically reviewed clinical and animal studies on how herbal medicine affects the modulation of MLN and subsequently brings the therapeutic effects mainly focused on GI function.Methods: We searched the PubMed, Embase, Cochrane, and Web of Science databases to collect all articles published until 30 July 2023, that reported the measurement of plasma MLN levels in human randomized controlled trials and in vivo herbal medicine studies. The collected characteristics of the articles included the name and ingredients of the herbal medicine, physiological and symptomatic changes after administering the herbal medicine, changes in plasma MLN levels, key findings, and mechanisms of action. The frequency patterns (FPs) of botanical drug use and their correlations were investigated using an FP growth algorithm.Results: Nine clinical studies with 1,308 participants and 20 animal studies were included in the final analyses. Herbal medicines in clinical studies have shown therapeutic effects in association with increased levels of MLN, including GI motility regulation and symptom improvement. Herbal medicines have also shown anti-stress, anti-tumor, and anti-inflammatory effects in vivo. Various biochemical markers may correlate with MLN levels. Markers may have a positive correlation with plasma MLN levels included ghrelin, acetylcholine, and secretin, whereas a negative correlation included triglycerides and prostaglandin E2. Markers, such as gastrin and somatostatin, did not show any correlation with plasma MLN levels. Based on the FP growth algorithm, Glycyrrhiza uralensis and Paeonia japonica were the most frequently used species.Conclusion: Herbal medicine may have therapeutic effects mainly on GI symptoms with involvement of MLN regulation and may be considered as an alternative option for the treatment of GI diseases. Further studies with more solid evidence are needed to confirm the efficacy and mechanisms of action of herbal medicines.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443244, identifier CRD42023443244.

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into ‘low’, ‘medium’, and ‘high’ attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence ‘high yield’ were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability’s impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.