Mining Association Rules between Abnormal Health Examination Results and Outpatient Medical Records

Huang, Yu‐Chun

doi:10.1177/183335831304200204

Cited by 8 publications

(5 citation statements)

References 11 publications

(8 reference statements)

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“…From the methodological perspective, different ARM methods have been employed in the literature. Some of those methods include data cutting and inner product (DCIP) method, 33,34 extended FP-growth methods, 35 Boolean analyzer, 29 and Tertius. 27 However, many of the research works used Apriori to discover association rules in medical data sets, 16,22,27 which makes ARM one of the popular and widely used methods without doubt.…”

Section: Related Studiesmentioning

confidence: 99%

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Sarıyer

2019

Health Informatics J

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Diagnostic tests are widely used in emergency departments to make detailed investigations on diagnosis and treat patients correctly. However, since these tests are expensive and time-consuming, ordering correct tests for patients is crucial for efficient use of hospital resources. Thus, understanding the relation between diagnosis and diagnostic test requirement becomes an important issue in emergency departments. Association rule mining was used to extract hidden patterns and relation between diagnosis and diagnostic test requirement in real-life medical data received from an emergency department. Apriori was used as an association rule mining algorithm. Diagnosis was grouped into 21 categories based on International Classification of Disease, and laboratory tests were grouped into four main categories (hemogram, biochemistry, cardiac enzyme, urine and human excrement related). Both positive and negative rules were discovered. Since the nature of the data had the dominance of negative values, higher number of negative rules with higher confidences were discovered compared to positive ones. The extracted rules were validated by emergency department experts and practitioners. It was concluded that understanding the association between patient’s diagnosis and diagnostic test requirement can improve decision-making and efficient use of resources in emergency departments. Association rules can also be used for supporting physicians to treat patients.

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Section: Related Studiesmentioning

confidence: 99%

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Sarıyer

2019

Health Informatics J

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“…The Doc2Vec used in this study is based on the same principles of Word2Vec (Le & Mikolov, 2014), which allows for the drastic reduction of computational complexity compared with other available methods (Dias et al, 2014; Yan & Zhu, 2018). Therefore, it can be used without excessively overloading the computer system even when analyzing electronic medical records, which contain vast amounts of data (Huang, 2013). In addition, aspiration pneumonia cases could be significantly differentiated even with a small amount of data, eliminating the need for the large amount of data generally used in machine learning.…”

Section: Discussionmentioning

confidence: 99%

Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records

et al. 2021

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Nursing records are an account of patient condition and treatment during their hospital stay. In this study, we developed a system that can automatically analyze nursing records to predict the occurrence of diseases and incidents (e.g., falls). Text vectorization was performed for nursing records and compared with past case data on aspiration pneumonia, to develop an onset prediction system. Nursing records for a patient group that developed aspiration pneumonia during hospitalization and a non-onset control group were randomly assigned to definitive diagnostic (for learning), preliminary survey, and test datasets. Data from the preliminary survey were used to adjust parameters and influencing factors. The final verification used the test data and revealed the highest compatibility to predict the onset of aspiration pneumonia (sensitivity = 90.9%, specificity = 60.3%) with the parameter values of size = 80 (number of dimensions of the sentence vector), window = 13 (number of words before and after the learned word), and min_count = 2 (threshold of wordcount for word to be included). This method represents the foundation for a discovery/warning system using machine-based automated monitoring to predict the onset of diseases and prevent adverse incidents such as falls.

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“…Huang et al applied Naive Bayes, IB1 classifier, and CART on 2,064 patients dataset and identify five important factors (age, insulin needs, diagnose duration, diet treatment and random blood glucose) that effect blood glucose control. Using these five attributes, system achieved 95% accuracy and 98% sensitivity [131].…”

Section: Classificationmentioning

confidence: 99%

“…ARM was applied on patients clinical examination and history data for oral cancer detection. Huang presented data cutting and sorting method (DCSM) rather than Apriori algorithm that reduces the time to scan immense sizes database, i.e., health examination and outpatient medical records [131].…”

Section: Association Analysismentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

163

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Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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Mining Association Rules between Abnormal Health Examination Results and Outpatient Medical Records

Cited by 8 publications

References 11 publications

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Highlighting the rules between diagnosis types and laboratory diagnostic tests for patients of an emergency department: Use of association rule mining

Supporting the Early Detection of Disease Onset and Change Using Document Vector Analysis of Nursing Observation Records

Big data analytics for preventive medicine

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