SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury

Sengupta, Dipankar; Naik, Pradeep Kumar

doi:10.1186/2043-9113-3-24

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Sequential Pattern Mining: In the realm of supervised temporal pattern mining, research has extended the temporal abstraction framework by mining recent temporal patterns for monitoring and event detection problems in patients suffering from T2DM [Batal et al 2012]. Sengupta et al [Sengupta and Naik 2013] used similar techniques for detecting sequential rules associated with the early identification of brain tumors. Simon et.…”

Section: Cohort and Case-control Study Designmentioning

confidence: 99%

Mining Electronic Health Records: A Survey

Yadav¹,

Steinbach²,

Kumar³

et al. 2017

Preprint

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The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research.

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Section: Cohort and Case-control Study Designmentioning

confidence: 99%

Mining Electronic Health Records: A Survey

Yadav¹,

Steinbach²,

Kumar³

et al. 2017

Preprint

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“…In general, we have witnessed a trend towards a translational use of data mining and knowledge discovery. Great interest has been devoted also to the management of longitudinal data, with several methodologies centered on the extraction and visualization of temporal patterns [20][21][22][23][24][25][26][27][28][29][30]. These methods were exploited both for clinical applications and for tackling organizational issues [31,32].…”

Section: Data Sources and Availability In 2015 3121 Rise Of The Elmentioning

confidence: 99%

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

Sacchi¹,

Holmes

2016

Yearb Med Inform

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Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

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