Unsupervised neural networks for clustering emergent patient flows

Resta, Marina; Sonnessa, Michele; Tànfani, Elena; Testi, Angela

doi:10.1016/j.orhc.2017.08.002

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…2 Thus, use of data mining becomes more of an issue in EDs to make effective decisions. In the literature, there have been many studies which used different functions of data mining such as for clustering the patients, [3][4][5] classifying them, 6 or generating predictions. [7][8][9] However, to the best of the knowledge, use of association analysis or association rule mining (ARM) is very rare in ED context.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Most existing machine learning applications in health care – including those that create electronic phenotyping algorithms – use supervised learning (17). In this section, we provide a brief overview of several machine learning techniques that have been commonly used to identify health outcomes from electronic health data, including supervised methods such as support vector machines and random forests, as well as neural networks and deep learning models that may be either supervised or unsupervised (17–19). …”

Section: Overview Of Common Machine Learning Techniques Used To Identmentioning

confidence: 99%

Using Machine Learning to Identify Health Outcomes from Electronic Health Record Data

et al. 2018

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Purpose of review: Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with some of these challenges. In this review, we discuss four common scenarios that researchers may find helpful for thinking critically about when and for what tasks machine learning may be used to identify health outcomes from EHR data. Recent findings: We first consider the conditions in which machine learning may be especially useful with respect to two dimensions of a health outcome: 1) the characteristics of its diagnostic criteria, and 2) the format in which its diagnostic data are usually stored within EHR systems. In the first dimension, we propose that for health outcomes with diagnostic criteria involving many clinical factors, vague definitions, or subjective interpretations, machine learning may be useful for modeling the complex diagnostic decision-making process from a vector of clinical inputs to identify individuals with the health outcome. In the second dimension, we propose that for health outcomes where diagnostic information is largely stored in unstructured formats such as free text or images, machine learning may be useful for extracting and structuring this information as part of a natural language processing system or an image recognition task. We then consider these two dimensions jointly to define four common scenarios of health outcomes. For each scenario, we discuss the potential uses for machine learning – first assuming accurate and complete EHR data and then relaxing these assumptions to accommodate the limitations of real-world EHR systems. We illustrate these four scenarios using concrete examples and describe how recent studies have used machine learning to identify these health outcomes from EHR data. Summary: Machine learning has great potential to improve the accuracy and efficiency of health outcome identification from EHR systems, especially under certain conditions. To promote the use of machine learning in EHR-based phenotyping tasks, future work should prioritize efforts to increase the transportability of machine learning algorithms for use in multi-site settings.

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“…The use of data mining techniques in health systems has received significant attention recently. Rather than the commonly used techniques of clustering (Lin et al, 2011;Resta et al, 2018) and association rule mining (Huang, 2013;Lee et al, 2013;Nahar et al, 2013), most research utilises classification techniques. There have been various approaches.…”

Section: Introductionmentioning

confidence: 99%

The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression

Sarıyer

Ataman²

2020

HIM J

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Background: Emergency departments (EDs) play an important role in health systems since they are the front line for patients with emergency medical conditions who frequently require diagnostic tests and timely treatment. Objective: To improve decision-making and accelerate processes in EDs, this study proposes predictive models for classifying patients according to whether or not they are likely to require a diagnostic test based on referral diagnosis, age, gender, triage category and type of arrival. Method: Retrospective data were categorised into four output patient groups: not requiring any diagnostic test (group A); requiring a radiology test (group B); requiring a laboratory test (group C); requiring both tests (group D). Multivariable logistic regression models were used, with the outcome classifications represented as a series of binary variables: test (1) or no test (0); in the case of group A, no test (1) or test (0). Results: For all models, age, triage category, type of arrival and referral diagnosis were significant predictors whereas gender was not. The main referral diagnosis with high model coefficients varied by designed output groups (groups A, B, C and D). The overall accuracies of the logistic regression models for groups A, B, C and D were, respectively, 74.11%, 73.07%, 82.47% and 85.79%. Specificity metrics were higher than the sensitivities for groups B, C and D, meaning that these models were better able to predict negative outcomes. Implications: These results provide guidance for ED triage staff, researchers and practitioners in making rapid decisions regarding patients’ diagnostic test requirements based on specified variables in the predictive models. This is critical in ED operations planning as it potentially decreases waiting times, while increasing patient satisfaction and operational performance.

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Unsupervised neural networks for clustering emergent patient flows

Cited by 16 publications

References 25 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

Using Machine Learning to Identify Health Outcomes from Electronic Health Record Data

The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression

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