The regional disparities in liver disease comorbidity among elderly Chinese based on a health ecological model: the China Health and Retirement Longitudinal Study.

PURPOSE: This study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model. METHOD: Basic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes. RESULTS: Among the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities. CONCLUSION: The comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Healthcare insurance fraud detection using data mining.

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Factors influencing illegal dumping of hazardous waste in China.

In recent years, illegal dumping of hazardous waste (IDHW) in China has become a recurring problem. Effective identification and exploration of the factors influencing illegal dumping are crucial for incident prevention and hazardous waste management, but its analysis has rarely been reported. Thus, this study focused on 568 cases of IDHW officially reported by the government. Through regular expressions, the categories of dumped wastes and the provinces where the incidents occurred were extracted. Furthermore, a comprehensive set of influencing factors was constructed by text mining for the case content and by the integration from the existing literature. On this basis, the unstructured and structured data were integrated using a Boolean dataset to respectively explore the association rules of influencing factors for the overall IDHW and for major waste categories, in conjunction with the extracted province information. Subsequently, a Bayesian network was constructed by utilizing the results of association rules mining and the key factors were identified through corresponding analysis. The findings of this study reveal a close connection between various influencing factors, with distinct key factors identified for different categories of hazardous waste. Among them, law-enforcement emerges as a crucial factor in most IDHW cases, while the factor of public monitoring for metallic hazardous waste and the factor of government supervision for distillation residue waste and other waste play a key role in their respective cases of illegal dumping. These findings offer a fresh research perspective for investigating the factors influencing IDHW and present helpful insights for developing effective strategies to prevent and control such incidents.

Influence of emotions on the aggressive driving behavior of online -car-hailing drivers based on association rule mining.

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers' emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers.

Based on the association rule mining method, we found a close connection between drivers' emotional states and the manifestation of aggressive driving behaviours. The findings indicate that the combination of negative emotions and various contributing factors significantly amplifies the likelihood of aggressive driving.

Phytochemistry, data mining, pharmacology, toxicology and the analytical methods of Cyperus rotundus L. (Cyperaceae): a comprehensive review.

Cyperus rotundus L. has been widely used in the treatment and prevention of numerous diseases in traditional systems of medicine around the world, such as nervous, gastrointestinal systems diseases and inflammation. In traditional Chinese medicine (TCM), its rhizomes are frequently used to treat liver disease, stomach pain, breast tenderness, dysmenorrheal and menstrual irregularities. The review is conducted to summarize comprehensively the plant's vernacular names, distribution, phytochemistry, pharmacology, toxicology and analytical methods, along with the data mining for TCM prescriptions containing C. rotundus. Herein, 552 compounds isolated or identified from C. rotundus were systematically collated and classified, concerning monoterpenoids, sesquiterpenoids, flavonoids, phenylpropanoids, phenolics and phenolic glycosides, triterpenoids and steroids, diterpenoids, quinonoids, alkaloids, saccharides and others. Their pharmacological effects on the digestive system, nervous system, gynecological diseases, and other bioactivities like antioxidant, anti-inflammatory, anti-cancer, insect repellent, anti-microbial activity, etc. were summarized accordingly. Moreover, except for the data mining on the compatibility of C. rotundus in TCM, the separation, identification and analytical methods of C. rotundus compositions were also systematically summarized, and constituents of the essential oils from different regions were re-analyzed using multivariate statistical analysis. In addition, the toxicological study progresses on C. rotundus revealed the safety property of this herb. This review is designed to serve as a scientific basis and theoretical reference for further exploration into the clinical use and scientific research of C. rotundus. Supplementary Information: The online version contains supplementary materials available at 10.1007/s11101-023-09870-3.

Fuzzy association analysis for identifying climatic and socio-demographic factors impacting the spread of COVID-19.

Recently, the whole world witnessed the fatal outbreak of COVID-19 epidemic originating at Wuhan, Hubei province, China, during a mass gathering in a film festival. World Health Organization (WHO) has declared this COVID-19 as a pandemic due to its rapid spread across different countries within a few days. Several research works are being performed to understand the various influential factors responsible for spreading COVID. However, limited studies have been performed on how climatic and socio-demographic conditions may impact the spread of the virus. In this work, we aim to find the relationship of socio-demographic conditions, such as temperature, humidity, and population density of the regions, with the spread of COVID-19. The COVID data for different countries along with the social data are collected. For the experimental purpose, Fuzzy association rule mining is employed to infer the various relationships from the data. Moreover, to examine the seasonal effect, a streaming setting is also considered. The experimental results demonstrate various interesting insights to understand the impact of different factors on spreading COVID-19.

Comorbidities in congenital heart disease: different patterns in childhood and adulthood.

BACKGROUND: Existing studies were no exploration of the association between congenital heart disease (CHD) in children and comorbidities. This study was to assess the prevalence and number of comorbidities in CHD among children and adults, and to compare the comorbidity patterns by children and adults using association rule analysis. METHODS: Patients identified by the International Classification of Diseases, Ninth Revision (ICD-9) code in the Medical Information Mart for Intensive Care III (MIMIC-III) 2001-2012 and MIMIC-IV 2008-2018 were included in this cross-sectional study. Association rule analysis was used to explore associations between CHD and comorbidities in children and adults using values of support (%), confidence (%), and lift. RESULTS: Among 60,400 eligible patients, 1.54% of adults had CHD and 0.83% of adults had CHD with at least one comorbidity, 13.79% had CHD and 12.37% had CHD with at least one comorbidity in children. The most common comorbidities were circulatory system diseases (53.78%), endocrine diseases (35.76%), and respiratory system diseases (23.46%) in adults with CHD, and the most common comorbidities were perinatal diseases (87.50%) in children with CHD. The comorbidity rate was 90.19% and 56.68% in children and adults, respectively. In children, perinatal diseases, circulatory system diseases, and endocrine diseases had the highest prevalence. The incidence of circulatory system diseases, perinatal diseases and endocrine diseases in CHD adults was confidence = 31.56%, 36.11%, and 23.23%, respectively. Perinatal diseases were common comorbidities among all CHD severity groups in children and adults. CONCLUSION: The prevalence of comorbidities in children with CHD was higher than that in adults with CHD. The most common comorbidities were perinatal diseases and endocrine diseases among children and adults with CHD, respectively. Our study provided insights into comorbidity patterns in children and adults with CHD.

Analysis of multiple chronic disease characteristics in middle-aged and elderly South Koreans by exercise habits based on association rules mining algorithm.

BACKGROUND: The term, "multiple chronic diseases" (MCD), describes a patient with two or more chronic conditions simultaneously at the same time. Compared with general chronic diseases, it is linked to poorer health outcomes, more difficult clinical management, and higher medical expenses. Several existing MCD guidelines support a healthy lifestyle including regular physical activities but do not include specific exercise therapy recommendations. This study aimed to understand the prevalence and model of MCD in middle-aged and elderly South Koreans by comparing MCD characteristics with exercise habits, to provide a theoretical basis for the implementation of exercise therapy in these patients. METHODS: The data of 8477 participants aged > 45 years from the "2020 Korean Health Panel Survey" were used to analyze the current status of MCD in the middle-aged and elderly. The Chi-square test for categorical variables and the t-test for continuous variables. the used software was IBM SPSS Statistics 26.0 and IBM SPSS Modeler 18.0. RESULTS: In this study, the morbidity rate of MCD was 39.1%. Those with MCD were more likely to be female (p < 0.001), seniors over 65 years of age (p < 0.001), with low education level, no regular exercise behavior (p < 0.01). Chronic renal failure (93.9%), depression (90.4%), and cerebrovascular disease (89.6%) were the top three diseases identified in patients with MCD. A total of 37 association rules were identified for the group of individuals who did not engage in regular exercise. This equated to 61% more than that of the regular exercise group, who showed only 23 association rules. In the extra association rules, cardiovascular diseases (150%), spondylosis (143%), and diabetes (125%) are the three chronic diseases with the highest frequency increase. CONCLUSIONS: Association rule analysis is effective in studying the relationship between various chronic diseases in patients with MCD. It also effectively helps with the identification of chronic diseases that are more sensitive to regular exercise behaviors. The findings from this study may be used to formulate more appropriate and scientific exercise therapy for patients with MCD.

Acquisition of temporal patterns from electronic health records: an application to multimorbid patients.

BACKGROUND: The exponential growth of digital healthcare data is fueling the development of Knowledge Discovery in Databases (KDD). Extracting temporal relationships between medical events is essential to reveal hidden patterns that can help physicians find optimal treatments, diagnose illnesses, detect drug adverse reactions, and more. This paper presents an approach for the extraction of patient evolution patterns from electronic health records written in Catalan and/or Spanish. METHODS: We propose a robust formulation for extracting Temporal Association Rules (TARs) that goes beyond simple rule extraction by considering the sequence of multiple visits. Our highly configurable algorithm leverages this formulation to extract Temporal Association Rules from sequences of medical instances. We can generate rules in the desired format, content, and temporal factors while accounting for different levels of abstraction of medical instances. To demonstrate the effectiveness of our methodology, we applied it to extract patient evolution patterns from clinical histories of multimorbid patients suffering from heart disease and stroke who visited Primary Care Centers (CAP) in Catalonia. Our main objective is to uncover complex rules with multiple temporal steps, that comprise a set of medical instances. RESULTS: As we are working with real-world, error-prone data, we propose a process of validation of the results by expert practitioners in primary care. Despite our limited dataset, the high percentage of patterns deemed correct and relevant by the experts is promising. The insights gained from these patterns can inform preventive measures and help detect risk factors, ultimately leading to better treatments and outcomes for patients. CONCLUSION: Our algorithm successfully extracted a set of meaningful and relevant temporal patterns, especially for the specific type of multimorbid patients considered. These patterns were evaluated by experts and demonstrated the ability to predict risk factors that are commonly associated with certain diseases. Moreover, the average time gap between the occurrence of medical events provided critical insight into the term of these risk factors. This information holds significant value in the context of primary healthcare and preventive medicine, highlighting the potential of our method to serve as a valuable medical tool.

Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method.

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.

Short-term relation between air pollutants and hospitalizations for respiratory diseases: analysis by temporal association rules.

This study investigates the relation between exposure to critical air pollution events with multipollutant (CO, PM10, PM2.5, NO2, O3, and SO2) and hospitalizations for respiratory diseases in the metropolitan area of São Paulo (RMSP) and in the countryside and coastline, from 2017 to 2021. Data mining analysis by temporal association rules searched for frequent patterns of respiratory diseases and multipollutants associated with time intervals. In the results, pollutants PM10, PM2.5, and O3 showed high concentration values in the three regions, SO2 on the coast, and NO2 in the RMSP. Seasonality was similar between pollutants and between cities and concentrations significantly higher in winter, except for O3, which was present in warm seasons. Hospitalizations were recurrent during the transition from summer to colder periods. In approximately 35% of the total days with hospitalization greater than the annual average, one or more pollutants had a high concentration. The rules showed that PM2.5, PM10, and O3 pollutants are strongly associated with increased hospitalizations in the RMSP (PM2.5 and PM10 with 38.5% support and 77% confidence) and in Campinas (PM2.5 with 66.1% support and 94% confidence) and the pollutant O3 with maximum support of 17.5%. On the coast, SO2 was related to high hospitalizations (43.85% support and 80% confidence). The pollutants CO and NO2 were not associated with the increase in hospitalizations. The ratio delay indicates the pollutants that were associated with hospitalizations, having concentration remained above the limit for three days, oscillating in smaller hospitalizations on the 1st day and again higher on the 2nd and 3rd days of delay, in a decreasing way. In conclusion, high pollutant exposure is significantly associated with daily hospitalization for respiratory problems. The cumulative effect of air pollutants increased hospitalization in the following days, in addition to identifying the pollutants and which pollutant combinations are most harmful to health in each region.

Semi-supervised incremental learning with few examples for discovering medical association rules.

BACKGROUND: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. METHODS: In this work we propose a new semi-supervised data mining model that combines unsupervised techniques (Fisher's exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F-measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. RESULTS: The new semi-supervised ML algorithm improves the results of supervised algorithms computed using the F-measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. CONCLUSIONS: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.

Dependence Graphs Based on Association Rules to Explore Delusional Experiences.

Methods to estimate dependence graphs among variables, have quickly gained popularity in psychopathology research. To date, multiple methods have been proposed but recent studies report several drawbacks impacting on the validity of the conclusions as it is argued that assumptions and conditions underlying the methods commonly used and the nature of the data is lacking alignment. A particularly important issue is that underlying dynamics potentially present in heterogeneous datasets are disregarded, as the methods focus on the variables but not on individuals. This work also argues that the networks may lack relevant components as current methods ignore connections beyond pairwise interactions between individual symptoms. This study addresses these issues with a novel method for constructing dependence graphs based on applying Association Rules to binary records, which is often the type of records in the psychopathology domain. To demonstrate the benefits, we examine 12 delusional experiences in a sample of 1423 subjects with psychotic disorders. We show that by extracting Association Rules using an algorithm called apriori, in addition to facilitating an intuitive interpretation, previously unseen relevant dependencies are revealed from higher order interactions among psychotic experiences in subgroups of patients.

Spatial patterns and temporal variations of pollutants at 56 air quality monitoring stations in the state of São Paulo, Brazil.

This study applied two data mining tasks: clustering and association rules to a dataset of pollutants in the state of São Paulo. The clustering task was applied to temporal patterns and geospatial distributions of pollutants, and the association rules were used to identify prevailing meteorological conditions when there were high concentrations of pollutants from 2017 to 2019. The results indicated good adequacy of the cluster, indicating different pollution levels per group, with a silhouette coefficient from 0.26 to 0.72. In the spatial evaluation, the groups severely polluted were located in the metropolitan region, on the coast and, some inland cities, by industrial, vehicular, burning, agriculture, and other emissions. The cluster identified a strong presence of O3 and PM2.5 in 65% and 72% of the monitored stations in several areas of the state. As for the distance between the sources of pollution, the groups of PM10 and NO2 were geographically distant, while PM2.5, CO, SO2, and O3 were closer, suggesting a spatial relationship of exposure. Seasonality was similar between groups, with significantly higher concentrations in winter, except for O3, for which higher concentrations occurred in summer. Meteorological conditions contributed to critical episodes of pollution (support and confidence greater than 80%), with low temperature and humidity, low rainfall, and milder wind associated with increased pollutants. In conclusion, investigating spatial representativeness allows revealing spatial and temporal patterns of pollutants and unfavorable meteorological conditions to diffusion. Thus, ideal and effective measures can be taken to avoid critical periods of exposure based on the behavior of pollutants in different regions and related climate changes.

Selected Data Mining Tools for Data Analysis in Distributed Environment.

In this paper, we deal with distributed data represented either as a finite set T of decision tables with equal sets of attributes or a finite set I of information systems with equal sets of attributes. In the former case, we discuss a way to the study decision trees common to all tables from the set T: building a decision table in which the set of decision trees coincides with the set of decision trees common to all tables from T. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply various decision tree learning algorithms to it. We extend the considered approach to the study of test (reducts) and decision rules common to all tables from T. In the latter case, we discuss a way to study the association rules common to all information systems from the set I: building a joint information system for which the set of true association rules that are realizable for a given row ρ and have a given attribute a on the right-hand side coincides with the set of association rules that are true for all information systems from I, have the attribute a on the right-hand side, and are realizable for the row ρ. We then show how to build a joint information system in a polynomial time. When we build such an information system, we can apply various association rule learning algorithms to it.

Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case-time-control design.

BACKGROUND: Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication-induced delirium in older adults is well understood, limited population-level evidence is available, particularly on combinations of medications associated with delirium in older adults. OBJECTIVES: We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case-time-control design. METHOD: We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005-2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti-inflammatory medications, were considered drugs of interest. The first-time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case-time-control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l-day with a 35-day washout period) that are statistically associated with delirium, and the association was tested by computing a time-trend adjusted matched odds-ratio (MOR) and its 95% confidence interval (CI). RESULTS: We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case-time-control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR = 6.17; 95%CI = [2.05-18.54]), haloperidol (MOR = 4.81; 95%CI = [3.16-6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR = 3.94; 95%CI = [3.03-5.10]), and fentanyl exposure (MOR = 3.46; 95%CI [2.05-9.21]). CONCLUSION: The association rule method applied to a case-time-control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.

Using association rules mining to characterize loss of control eating in childhood.

Childhood loss of control (LOC)-eating, the perceived inability to stop or control eating, is associated with increased risk for binge-eating disorder and obesity. However, the correlates of LOC-eating in childhood remain unclear. A secondary analysis of 177, 7-12-year-old children from five laboratory feeding studies was performed to investigate potential family (e.g., frequency of meals together, feeding practices), parental (e.g., education, weight status), and child (e.g., weight status, appetite traits) correlates of LOC-eating. Association rules mining (ARM1), a data-driven approach, was used to examine all characteristics that were common across studies to identify which were associated with LOC-eating. Results showed LOC-eating was characterized by a combination of child appetitive behaviors and parental feeding practices. In particular, LOC-eating was associated with low parental pressure to eat in combination with a high propensity to want to eat all the time and frequent refusal or dislike of novel foods. This pattern of both food approach (i.e., wanting to eat all the time) and avoidant behaviors (i.e., food fussiness) highlights the need for more research to characterize the complex patterns of appetitive traits associated with LOC-eating. In contrast, the absence of LOC-eating was associated with a low propensity to want to eat all the time, greater family income, and infrequent emotional overeating. Therefore, propensity to want to eat all the time, a single question from the Children's Eating Behavior Questionnaire, characterized both the presence and absence of LOC-eating, highlighting the need for more research to determine if this question captures clinically relevant individual differences. Future studies addressing these questions will advance our understanding of pediatric LOC-eating and may lead to interventions to reduce risk for more severe eating disorder symptomology.

TSARM-UDP: An Efficient Time Series Association Rules Mining Algorithm Based on Up-to-Date Patterns.

In many industrial domains, there is a significant interest in obtaining temporal relationships among multiple variables in time-series data, given that such relationships play an auxiliary role in decision making. However, when transactions occur frequently only for a period of time, it is difficult for a traditional time-series association rules mining algorithm (TSARM) to identify this kind of relationship. In this paper, we propose a new TSARM framework and a novel algorithm named TSARM-UDP. A TSARM mining framework is used to mine time-series association rules (TSARs) and an up-to-date pattern (UDP) is applied to discover rare patterns that only appear in a period of time. Based on the up-to-date pattern mining, the proposed TSAR-UDP method could extract temporal relationship rules with better generality. The rules can be widely used in the process industry, the stock market, etc. Experiments are then performed on the public stock data and real blast furnace data to verify the effectiveness of the proposed algorithm. We compare our algorithm with three state-of-the-art algorithms, and the experimental results show that our algorithm can provide greater efficiency and interpretability in TSARs and that it has good prospects.

COVID-19 patient diagnosis and treatment data mining algorithm based on association rules.

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID-19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID-19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre-process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID-19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision-maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID-19, CHD and hypertension, while 46.5% of people with COVID-19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID-19 disease.

[Characteristics analysis for Chinese patent medicine containing Jujubea Fructus based on data mining].

Chinese patent medicine prescriptions containing Jujubea Fructus in 2015 edition of Chinese Pharmacopoeia and the Composition Principles of Chinese Patent Drug were collected, and the characteristics of Chinese patent medicine containing Jujubea Fructus were analyzed by using data mining technology. Statistical software Excel 2019, Clementine 12.0 and SPSS 21.0 were used to conduct statistical analysis of conforming Chinese patent medicine prescriptions by means of frequency statistics, association rule analysis and cluster analysis. Finally, a total of 185 Chinese patent medicine prescriptions containing Jujubea Fructus were included in this study, involving 402 Chinese medicines and 28 kinds of high frequency Chinese medicines, with Jujubea Fructus, Poria, Zingiberis Rhizoma Recens, Glycyrrhizae Radix et Rhizoma, and Codonopsis Radix as the top five. The deficiency-nourishing drugs were in the most common efficacy classification, mainly sweet, bitter and pungent, with most medicine properties of warm and gentle, main meridians of spleen lung and stomach, dosage forms of pills, granules and tablets, and main indications of splenic diseases. Fifteen drug combinations were obtained in association rule analysis. Eleven drug combinations were obtained by association rule analysis of Chinese patent medicine containing Jujubea Fructus in the treatment of splenic diseases, and the drugs were divided into two categories by cluster analysis. According to the above analysis, it is found that the Chinese patent medicine prescriptions containing Jujubea Fructus are mainly composed of deficiency-nourishing drugs, mostly compatible with drugs of sweet, bitter and pungent flavors, warm and gentle properties, and spleen, lung, and stomach meridians in the treatment of splenic diseases, with Sijunzi Decoction as the main drug. This study provides guidance for modern clinical application and development of Jujubea Fructus.