Non-homogeneous Markov models for sequential pattern mining of healthcare data

2008 ◽  
Vol 20 (4) ◽  
pp. 327-344 ◽  
Author(s):  
L. Garg ◽  
S. McClean ◽  
B. Meenan ◽  
P. Millard
Keyword(s):  
Pattern Mining ◽  
Markov Models ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Healthcare Data

scholarly journals Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

2021 ◽  
Vol 13 (16) ◽  
pp. 8900
Author(s):  
Naeem Ahmed Mahoto ◽  
Asadullah Shaikh ◽  
Mana Saleh Al Reshan ◽  
Muhammad Ali Memon ◽  
Adel Sulaiman
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Analysis ◽  
Pattern Mining ◽  
Large Data ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Electronic Records ◽  
Data Mining Techniques ◽  
Healthcare Data

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.

scholarly journals A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1525
Author(s):  
Felipe Vieira ◽  
Cristian Cechinel ◽  
Vinicius Ramos ◽  
Fabián Riquelme ◽  
Rene Noel ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Data Collection ◽  
Pattern Mining ◽  
New Technologies ◽  
Personal Information ◽  
Sequential Pattern Mining ◽  
Complex Problem ◽  
Sequential Pattern ◽  
Background Information ◽  
Professional Relationships

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

Top-k Self-Adaptive Contrast Sequential Pattern Mining

2021 ◽  
pp. 1-15
Author(s):  
Youxi Wu ◽  
Yuehua Wang ◽  
Yan Li ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Self Adaptive

Sequential Pattern Mining Algorithm Based on Interestingness

2018 ◽  
Author(s):  
Tao Li ◽  
Shuaichi Zhang ◽  
Hui Chen ◽  
Yongjun Ren ◽  
Xiang Li ◽  
...  
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Mining Algorithm
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