Mining non-redundant time-gap sequential patterns

Yen, Show‐Jane; Lee, Yue‐Shi

doi:10.1007/s10489-013-0426-8

Cited by 24 publications

(5 citation statements)

References 12 publications

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“…However, this could be an important information in this type of modeling because time-lapse between successive hospitalizations in the patient trajectory can vary from days to months and even years. An extension to this work is possible using survival models [46], with the process described in the article by using mining time-gap sequential patterns models [47]. Other approaches were developed based on machine learning methods.…”

Section: Time Gaps In Sequentialmentioning

confidence: 99%

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Pinaire

Chabert

Azé

et al. 2021

Journal of Healthcare Engineering

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Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events’ prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients’ patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

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Section: Time Gaps In Sequentialmentioning

confidence: 99%

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Pinaire

Chabert

Azé

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…There is a unique µ per multiset. Its principle is first to build a timegap table [19] from all occurrences of a multiset and, second, to learn a temporal model from the time-gap table. Each time-gap occurrence is labeled by the label of its sequence and any standard machine learning algorithm can learn the µ function.…”

Section: Learning Generalized Discriminant Chronicles Classifiersmentioning

confidence: 99%

Generalized Chronicles for Temporal Sequence Classification

Dauxais

Guyet

2020

Advanced Analytics and Learning on Temporal Data

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Discriminant chronicle mining (DCM) [6] tackles temporal sequence classification by combining machine learning and chronicle mining algorithms. A chronicle is a set of events related by temporal boundaries on the delay between event occurrences. Such temporal constraints are poorly expressive and discriminant chronicles may lack of accuracy. This article generalizes discriminant chronicle mining by modeling complex temporal constraints. We present the generalized model and we instantiate different generalized chronicle models. The accuracy of these models are compared with each other on simulated and real datasets.

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“…The length of time interval is considered as an important factor in the prediction task [20] and the pattern discovery task [21]. [22] point out a problem with vanilla sequential pattern mining that does not take time gaps into account: if most of the customers buy B after A, and C after B, the manager can use this pattern to promote B when a customer purchases A and promote C when a customer buys B. However, if the time intervals between the purchases are not known, improper product recommendation would occur.…”

Section: Research On Pattern Miningmentioning

confidence: 99%

A Pattern-Mining Driven Study on Differences of Newspapers in expressing Temporal Information

Fu¹,

Dhonnchadha²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper studies the differences between different types of newspapers in expressing temporal information, which is a topic that has not received much attention. Techniques from the fields of temporal processing and pattern mining are employed to investigate this topic. First, a corpus annotated with temporal information is created by the author. Then, sequences of temporal information tags mixed with part-of-speech tags are extracted from the corpus. The TKS algorithm is used to mine skip-gram patterns from the sequences. With these patterns, the signatures of the four newspapers are obtained. In order to make the signatures uniquely characterize the newspapers, we revise the signatures by removing reference patterns. Through examining the number of patterns in the signatures and revised signatures, the proportion of patterns containing temporal information tags and the specific patterns containing temporal information tags, it is found that newspapers differ in ways of expressing temporal information.

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Mining non-redundant time-gap sequential patterns

Cited by 24 publications

References 12 publications

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome

Generalized Chronicles for Temporal Sequence Classification

A Pattern-Mining Driven Study on Differences of Newspapers in expressing Temporal Information

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