Association rules method and big data: Evaluating frequent medication combinations associated with fractures in older adults

Nishtala, Prasad S.; Chyou, Te‐yuan; Held, Fabian; Couteur, David G. Le; Gnjidic, Danijela

doi:10.1002/pds.4432

Cited by 11 publications

(9 citation statements)

References 39 publications

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“…The National Minimum Data Set (NMDS) is a national collection of public and private hospital discharge information, including coded clinical data for inpatients and day patients. We have provided a detailed description of both the datasets previously 7,8 . We used unique encrypted National Health Index (NHI) identifiers to cross‐match medication exposure data with hospital events data from NMDS.…”

Section: Methodsmentioning

confidence: 99%

“…The utility of the AR method has also been successfully extended to the field of bioinformatics to identify factors that control gene transcription 6 . We previously have demonstrated the AR method's utility to investigate medication combinations associated with fracture and acute kidney injury in older adults 7,8 . Our previous analyses were restricted to examining transient medication exposures during the time at risk of an acute event in line with the recommendation for implementing a case‐crossover design.…”

Section: Introductionmentioning

confidence: 99%

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

Chyou

Nishtala

2021

Pharmacoepidemiology and Drug

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chyou

Nishtala

2021

Pharmacoepidemiology and Drug

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“…As for the topics of research interest, nearly 70% of the inclusions (24/35) concentrate on gerontology, namely, biomarkers (3) [33][34][35], frailty index (1) [36], epigenetics of aging (1) [37], aging of brain functional connectivity networks (1) [38], ARDs (16) that contain dementia (6) [39][40][41][42][43][44], stroke (2) [45,46], Parkinson's Disease (1 ) [47], fracture (1) [48], hypertension (1) [49], mild cognitive impairment (1) [50], depression (1) [51], pressure ulcers (1) [52], polypharmacy side-effects (1) [53], and mortality related to sarcopenia and frailty (1) [54]. The rest are devoted to technical support and decision support like cloud-based healthcare platforms focusing on dementia (1) [55], memory recall training (1) [56]), fraud detection in medicare (2) [57,58], prediction of readmission risk (2) [59,60], well-being (2) [61,62], population portraits (1) [63], built environment and health outcomes (1) [64], patterns of living activities (1) [65], trajectories (1) [66], and geospatial patterns of points of interest (1) [67].…”

Section: Research Topicsmentioning

confidence: 99%

The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework

Zhang

Gao

et al. 2021

Sustainability

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Big data has been prominent in studying aging and older people’s health. It has promoted modeling and analyses in biological and geriatric research (like cellular senescence), developed health management platforms, and supported decision-making in public healthcare and social security. However, current studies are still limited within a single subject, rather than flourished as interdisciplinary research in the context of big data. The research perspectives have not changed, nor has big data brought itself out of the role as a modeling tool. When embedding big data as a data product, analysis tool, and resolution service into different spatial, temporal, and organizational scales of aging processes, it would present as a connection, integration, and interaction simultaneously in conducting interdisciplinary research. Therefore, this paper attempts to propose an ecological framework for big data based on aging and older people’s health research. Following the scoping process of PRISMA, 35 studies were reviewed to validate our ecological framework. Although restricted by issues like digital divides and privacy security, we encourage researchers to capture various elements and their interactions in the human-environment system from a macro and dynamic perspective rather than simply pursuing accuracy.

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“…Association rules in the data mining method are used to determine the directed association of each item in the dataset to characterize the correlation or relationship between various items and other items. In short, the suitability of the characterization formed on each item from the dataset will be combined through association rules [8]. In determining the results in the suitability of the characterization formed for each item, the dataset will be processed using one of several association rules algorithms, namely fp-growth.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Hasan

Aziz

Nofendri

2023

matrik

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Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.

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Association rules method and big data: Evaluating frequent medication combinations associated with fractures in older adults

Cited by 11 publications

References 39 publications

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

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

The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

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