Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil.

The efficient recognition of symptoms in viral infections holds promise for swift and precise diagnosis, thus mitigating health implications and the potential recurrence of infections. COVID-19 presents unique challenges due to various factors influencing diagnosis, especially regarding disease symptoms that closely resemble those of other viral diseases, including other strains of SARS, thus impacting the identification of useful and meaningful symptom patterns as they emerge in infections. Therefore, this study proposes an association rule mining approach, utilising the Apriori algorithm to analyse the similarities between individuals with confirmed SARS-CoV-2 diagnosis and those with unspecified SARS diagnosis. The objective is to investigate, through symptom rules, the presence of COVID-19 patterns among individuals initially not diagnosed with the disease. Experiments were conducted using cases from Brazilian SARS datasets for São Paulo State. Initially, reporting percentage similarities of symptoms in both groups were analysed. Subsequently, the top ten rules from each group were compared. Finally, a search for the top five most frequently occurring positive rules among the unspecified ones, and vice versa, was conducted to identify identical rules, with a particular focus on the presence of positive rules among the rules of individuals initially diagnosed with unspecified SARS.

Evaluation of pollution prevention related deficiencies of ships using association rule mining.

Since marine and environmental pollution is a major problem for the maritime industry, preventive implementations are constantly being developed. In this context, this research aimed to determine the dominant factors in ships detected to have pollution prevention deficiencies in port state control (PSC). A total of 12,530 PSC reports carried out by Paris Memorandum of Understanding (MoU) region between 2017 and 2023 were analyzed with the association rule mining. The Apriori algorithm was performed to reveal hidden and meaningful relationships in the inspections. The dominant variables for inspections that detected pollution prevention deficiencies were ship flag, classification society, number of deficiencies, and inspection type. Association rules revealed that pollution prevention deficiency areas differed interestingly according to geographical region, classification society, and ship age. The findings may be a guide for stakeholders for pollution prevention during ship inspections, and contribute to the achievement of maritime-related Sustainable Development Goals (SDGs).

Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.

Suicide research is directed at understanding social, economic, and biological causes of suicide thoughts and behaviors. (1) Background: Worldwide, certain countries have high suicide mortality rates (SMRs) compared to others. Age-standardized suicide mortality rates (SMRs) published by the World Health Organization (WHO) plus numerous bibliographic records of the Web of Science (WoS) database provide resources to understand these disparities between countries and regions. (2) Methods: Hierarchical clustering was applied to age-standardized suicide mortality rates per 100,000 population from 2000-2019. Keywords of country-specific suicide-related publications collected from WoS were analyzed by network and association rule mining. Keyword embedding was carried out using a recurrent neural network. (3) Results: Countries with similar SMR trends formed naturally distinct groups of high, medium, and low suicide mortality rates. Major themes in suicide research worldwide are depression, mental disorders, youth suicide, euthanasia, hopelessness, loneliness, unemployment, and drugs. Prominent themes differentiating countries and regions include: alcohol in post-Soviet countries; HIV/AIDS in Sub-Saharan Africa, war veterans and PTSD in the Middle East, students in East Asia, and many others. (4) Conclusion: Countries naturally group into high, medium, and low SMR categories characterized by different keyword-informed themes. The compiled dataset and presented methodology enable enrichment of analytical results by bibliographic data where observed results are difficult to interpret.

Interactions between patterns of multimorbidity and functional status among hospitalized older patients: a novel approach using cluster analysis and association rule mining.

BACKGROUND: Multimorbidity (MM) is generally defined as the presence of 2 or more chronic diseases in the same patient and seems to be frequently associated with frailty and poor quality of life. However, the complex interplay between MM and functional status in hospitalized older patients has not been fully elucidated so far. Here, we implemented a 2-step approach, combining cluster analysis and association rule mining to explore how patterns of MM and disease associations change as a function of disability. METHODS: This retrospective cohort study included 3366 hospitalized older patients discharged from acute care units of Ancona and Cosenza sites of Italian National Institute on Aging (INRCA-IRCCS) between 2011 and 2017. Cluster analysis and association rule mining (ARM) were used to explore patterns of MM and disease associations in the whole population and after stratifying by dependency in activities of daily living (ADL) at discharge. Sensitivity analyses in men and women were conducted to test for robustness of study findings. RESULTS: Out of 3366 included patients, 78% were multimorbid. According to functional status, 22.2% of patients had no disability in ADL (functionally independent group), 22.7% had 1 ADL dependency (mildly dependent group), and 57.4% 2 or more ADL impaired (moderately-severely dependent group). Two main MM clusters were identified in the whole general population and in single ADL groups. ARM revealed interesting within-cluster disease associations, characterized by high lift and confidence. Specifically, in the functionally independent group, the most significant ones involved atrial fibrillation (AF)-anemia and chronic kidney disease (CKD) (lift = 2.32), followed by coronary artery disease (CAD)-AF and heart failure (HF) (lift = 2.29); in patients with moderate-severe ADL disability, the most significant ARM involved CAD-HF and AF (lift = 1.97), thyroid dysfunction and AF (lift = 1.75), cerebrovascular disease (CVD)-CAD and AF (lift = 1.55), and hypertension-anemia and CKD (lift = 1.43). CONCLUSIONS: Hospitalized older patients have high rates of MM and functional impairment. Combining cluster analysis to ARM may assist physicians in discovering unexpected disease associations in patients with different ADL status. This could be relevant in the view of individuating personalized diagnostic and therapeutic approaches, according to the modern principles of precision medicine.

Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus.

BACKGROUND: It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care. AIM: To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery. METHODS: This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value. RESULTS: The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the "CAD-with-diabetes" group, offering valuable insights into health indicators and influencing factors on disease outcomes. CONCLUSION: The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques.

BACKGROUND: Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction. METHODS: The study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS). We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability. RESULTS: Based on our findings, about 28.59% (95% CI: 28.36, 28.83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88.0%. The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region. CONCLUSION: The region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies risky groups, including first-time mothers, women with short or long birth intervals, and those with unwanted pregnancies. To address the needs of these high-risk women, the researchers recommend immediately providing iron supplements, scheduling comprehensive prenatal care, and strongly encouraging facility-based deliveries or skilled birth attendance.

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

PURPOSE: Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. PATIENTS AN METHODS: We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. RESULTS: Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. CONCLUSIONS: Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.

Learning debiased graph representations from the OMOP common data model for synthetic data generation.

BACKGROUND: Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention. METHODS: Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts. RESULTS: The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand. CONCLUSION: Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable.

The diabetes mellitus multimorbidity network in hospitalized patients over 50 years of age in China: data mining of medical records.

OBJECTIVE: Many diabetes mellitus (DM) patients suffer from multimorbidity. Understanding the DM multimorbidity network should be given priority. The purpose of this study is characterize the DM multimorbidity network in people over 50 years. METHODS: Data on 75 non-communicable diseases (NCDs) were extracted from electronic medical records of 309,843 hospitalized patients older than 50 years who had at least one NCD. The association rules analysis was used as a novel classification method and combined with the Chi-square tests to identify associations between NCDs and DM. RESULT: A total of 12 NCDs were closely related to DM, {cholelithiasis, DM} was an unexpected combination. {dyslipidemia, DM} and {gout, DM} had the largest lift in the male and female groups, respectively. The negative related group included 7 NCDs. There were 9 NCDs included in the strong association rules. Most combinations were different by age and sex. In males, the strongest rule was {peripheral vascular disease (PVD), dyslipidemia, DM}, while {hypertension, dyslipidemia, chronic liver disease (CLD), DM} was the strongest in females. In patients younger than 70 years, hypertension, CLD, and dyslipidemia were the most dominant NCDs in the DM multimorbidity network. In patients 70 years or older, chronic kidney disease (CKD), CVD, CHD, and heart disease (HD) frequently co-occurred with DM. CONCLUSION: Future primary healthcare policies for DM should be formulated based on age and sex. In patients younger than 70 years, more attention to hypertension, CLD, and dyslipidemia is required, while attention to CKD, CVD, CHD and HD is needed in patients older than 70 years.

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task's performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests.

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.

Unlocking new potential of clinical diagnosis with artificial intelligence: Finding new patterns of clinical and lab data.

Recent advancements in science and technology, coupled with the proliferation of data, have also urged laboratory medicine to integrate with the era of artificial intelligence (AI) and machine learning (ML). In the current practices of evidence-based medicine, the laboratory tests analysing disease patterns through the association rule mining (ARM) have emerged as a modern tool for the risk assessment and the disease stratification, with the potential to reduce cardio-vascular disease (CVD) mortality. CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension, diabetes, and lifestyle choices. AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages. Personalized medicine, well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome. These technological advancements enhance the computational analyses for both research and clinical practice. ARM plays a pivotal role by uncovering meaningful relationships within databases, aiding in patient survival prediction and risk factor identification. AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical, legal, and pri-vacy concerns. Thus, an AI ethics framework is essential to guide its responsible use. Aligning AI algorithms with existing lab practices, promoting education among healthcare professionals, and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.

Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach.

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the "Behavior of Young Novice Drivers Scale" (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting.

PURPOSE: Data mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method. METHODS: We utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python. RESULTS: The results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules. CONCLUSIONS: This study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.

Risk and Protective Factors of Depression in Family and School Domains for Chinese Early Adolescents: An Association Rule Mining Approach.

Depression is one of the most common psychological problems in adolescence. Familial and school-related factors are closely related to adolescents' depression, but their combined effects need further examination. The purpose of this study was to explore the combined effects of risk/protective factors of depression in family and school domains using a sample of Chinese adolescents differing in gender, age group and left-behind status. A total of 2455 Chinese students in primary and secondary school participated in the cross-sectional survey and reported multiple risk/protective factors in family and school environments and depressive symptoms. Association rule mining, a machine learning method, was used in the data analyses to identify the correlation between risk/protective factor combinations and depression. We found that (1) Family cohesion, family conflict, peer support, and teacher support emerged as the strongest factors associated with adolescent depression; (2) The combination of these aforementioned factors further strengthened their association with depression; (3) Female gender, middle school students, and family socioeconomic disadvantages attenuated the protective effects of positive relational factors while exacerbating the deleterious effects of negative relational factors; (4) For individuals at risk, lack of mental health education resources at school intensified the negative impact; (5) The risk and protective factors of depression varied according to gender, age stage and left-behind status. In conclusion, the findings shed light on the identification of high-risk adolescents for depression and underscore the importance of tailored programs targeting specific subgroups based on gender, age, or left-behind status.

Utilizing Data Mining Techniques for Enhancing Physical Education Teaching Quality: A Sports Science Perspective / Utilización de técnicas de minería de datos para mejorar la calidad de la enseñanza de la educación física: una perspectiva de las ciencias del deporte

Quality physical education (PE) instruction plays a pivotal role in the development of talent and the overall success of educational institutions. Recent scholarly investigations have unveiled significant challenges within the realm of PE teaching. Consequently, it becomes imperative to establish a comprehensive PE teaching quality evaluation system that leverages data mining techniques. This study endeavors to design a robust model for evaluating PE teaching quality based on association rules mining. The process begins by defining evaluation indices specific to PE teaching quality. Subsequently, a meticulous analysis of the influencing factors affecting PE teaching quality is conducted. The results generated by the model presented herein closely align with established standard values, demonstrating superior evaluation quality. This model, rooted in data mining techniques, offers a promising reference point for future assessments and improvements in the domain of physical education teaching quality. In conclusion, the fusion of data mining methodologies within the sports science perspective provides a compelling framework for enhancing the quality of physical education teaching. This approach not only addresses existing challenges but also paves the way for more effective talent development and the advancement of educational institutions.(AU)

Prescription pattern analysis of Type 2 Diabetes Mellitus: a cross-sectional study in Isfahan, Iran.

BACKGROUND: Patients with Type 2 Diabetes Mellitus (T2DM) are at a higher risk of polypharmacy and more susceptible to irrational prescriptions; therefore, pharmacological therapy patterns are important to be monitored. The primary objective of this study was to highlight current prescription patterns in T2DM patients and compare them with existing Standards of Medical Care in Diabetes. The second objective was to analyze whether age and gender affect prescription patterns. METHOD: This cross-sectional study was conducted using the Iran Health Insurance Organization (IHIO) prescription database. It was mined by an Association Rule Mining (ARM) technique, FP-Growth, in order to find co-prescribed drugs with anti-diabetic medications. The algorithm was implemented at different levels of the Anatomical Therapeutic Chemical (ATC) classification system, which assigns different codes to drugs based on their anatomy, pharmacological, therapeutic, and chemical properties to provide an in-depth analysis of co-prescription patterns. RESULTS: Altogether, the prescriptions of 914,652 patients were analyzed, of whom 91,505 were found to have diabetes. According to our results, prescribing Lipid Modifying Agents (C10) (56.3%), Agents Acting on The Renin-Angiotensin System (C09) (48.9%), Antithrombotic Agents (B01) (35.7%), and Beta Blocking Agents (C07) (30.1%) were meaningfully associated with the prescription of Drugs Used in Diabetes. Our study also revealed that female diabetic patients have a higher lift for taking Thyroid Preparations, and the older the patients were, the more they were prone to take neuropathy-related medications. Additionally, the results suggest that there are gender differences in the association between aspirin and diabetes drugs, with the differences becoming less pronounced in old age. CONCLUSIONS: Almost all of the association rules found in this research were clinically meaningful, proving the potential of ARM for co-prescription pattern discovery. Moreover, implementing level-based ARM was effective in detecting difficult-to-spot rules. Additionally, the majority of drugs prescribed by physicians were consistent with the Standards of Medical Care in Diabetes.

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining.

Introduction: Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field. Methods: Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas. Results: The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene. Discussion: Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline.

Discovering associations between radiological features and COVID-19 patients' deterioration.

Background and Aims: Data mining methods are effective and well-known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID-19 by applying the rule mining method using characteristics of medical images. Methods: This retrospective study has analyzed the radiological data from 104 COVID-19 hospitalized patients diagnosed with COVID-19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results: Ten rules were extracted with only X-ray-related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan-related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion: This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID-19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.