ArticleJournal of diabetes science and technology2018
Careflow Mining Techniques to Explore Type 2 Diabetes Evolution.
Article in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical and operational insights from data-driven care pathway mapping: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Analytical methods for identifying sequences of utilization in health data: a scoping review.BMC medical research methodology · 2023Article
- Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.BMC medical informatics and decision making · 2023Article
- Interactive process mining of cancer treatment sequences with melanoma real-world data.Frontiers in oncology · 2023Article
- A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data.IEEE journal of biomedical and health informatics · 2021Article
- Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records.Artificial intelligence in medicine · 2020Article
- Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus.Bioinformation · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.
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