Mining and exploring care pathways from electronic medical records with visual analytics

Perer, Adam; Wang, Fei; Hu, Jianying

doi:10.1016/j.jbi.2015.06.020

Cited by 103 publications

(62 citation statements)

References 36 publications

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“…In [16], a segmentation method was proposed to discover the frequent behavior patterns with different time intervals. CareExplorer [17] was a novel CP management tool which combined frequent sequence mining techniques with advanced visualization supports. Manually defining a set of concerned and important clinical activities can significantly improve the interpretability of the process models [18–20].…”

Section: Related Workmentioning

confidence: 99%

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

Xiao

Jin

Wei

et al. 2017

Journal of Healthcare Engineering

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Clinical pathways are widely used around the world for providing quality medical treatment and controlling healthcare cost. However, the expert-designed clinical pathways can hardly deal with the variances among hospitals and patients. It calls for more dynamic and adaptive process, which is derived from various clinical data. Topic-based clinical pathway mining is an effective approach to discover a concise process model. Through this approach, the latent topics found by latent Dirichlet allocation (LDA) represent the clinical goals. And process mining methods are used to extract the temporal relations between these topics. However, the topic quality is usually not desirable due to the low performance of the LDA in clinical data. In this paper, we incorporate topic assignment constraint and topic correlation limitation into the LDA to enhance the ability of discovering high-quality topics. Two real-world datasets are used to evaluate the proposed method. The results show that the topics discovered by our method are with higher coherence, informativeness, and coverage than the original LDA. These quality topics are suitable to represent the clinical goals. Also, we illustrate that our method is effective in generating a comprehensive topic-based clinical pathway model.

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Section: Related Workmentioning

confidence: 99%

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

Xiao

Jin

Wei

et al. 2017

Journal of Healthcare Engineering

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“…As stated in the Care Pathway Explorer project , an interactive hierarchical information exploration system is useful for analyzing longitudinal medical data. By providing an overview of relevant patterns visually mined from reimbursement patient traces in the SNDS database, the ePEPS toolbox supports interactive exploration for researchers.…”

Section: Resultsmentioning

confidence: 99%

A visual approach of care pathways from the French nationwide SNDS database – from population to individual records: the ePEPS toolbox

Happe

Drézen

2017

Fundamemntal Clinical Pharma

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International audienceSecondary use of medical and administrative databases has become a powerful tool for epidemiological studies. In that respect, the recent access opening of French nationwide health record database or Système National des Données de Santé is a great opportunity to carry out comprehensive health studies at the country level. However, using this database is far from being straightforward for nonexpert data scientists; so, dedicated tools needed to be developed. Our contribution is focused on data management and visualization tools able to query and cope with the complexity of care pathways of patients. The scope of the toolbox presented below goes from interactive tools for building groups of patients to timeline representations of individual patient healthcare trajectories

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“…revealing the frequent patterns of disease progression by summarizing EMR/EHR data into flow-based representations [35,37]. Furthermore, to clearly display a sequence pattern, Guo et al [12,13] segmented sequences into latent stages to infer the disease progression.…”

Section: Related Workmentioning

confidence: 99%

Comparative visual analytics for assessing medical records with sequence embedding

Guo

Fujiwara

et al. 2020

Visual Informatics

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Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

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Mining and exploring care pathways from electronic medical records with visual analytics

Abstract: Care Pathway Explorer, which combines frequent sequence mining techniques with advanced visualizations supports the integration of data-driven insights into care pathway discovery.

Cited by 103 publications

References 36 publications

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

Incorporating Topic Assignment Constraint and Topic Correlation Limitation into Clinical Goal Discovering for Clinical Pathway Mining

A visual approach of care pathways from the French nationwide SNDS database – from population to individual records: the ePEPS toolbox

Comparative visual analytics for assessing medical records with sequence embedding

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