A medical specialty outpatient clinics recommendation system based on text mining

2021 ◽  
Vol 12 (4) ◽  
pp. 450
Author(s):  
Qing Chang Li ◽  
Xiao Qi Ling ◽  
Hsiu Sen Chiang ◽  
Kai Jui Yang
Keyword(s):  
Text Mining ◽  
Recommendation System ◽  
Medical Specialty ◽  
Outpatient Clinics

A medical specialty outpatient clinics recommendation system based on text mining

2021 ◽  
Vol 12 (4) ◽  
pp. 450
Author(s):  
Kai Jui Yang ◽  
Hsiu Sen Chiang ◽  
Qing Chang Li ◽  
Xiao Qi Ling
Keyword(s):  
Text Mining ◽  
Recommendation System ◽  
Medical Specialty ◽  
Outpatient Clinics

The Liminal and the Parallax

2016 ◽  
Vol 27 (5) ◽  
pp. 623-633 ◽  
Author(s):  
John I. MacArtney ◽  
Alex Broom ◽  
Emma Kirby ◽  
Phillip Good ◽  
Julia Wootton
Keyword(s):  
Palliative Care ◽  
End Of Life ◽  
Point Of Care ◽  
Medical Specialty ◽  
The Self ◽  
Outpatient Clinics ◽  
Semistructured Interviews

Transitions to palliative care can involve a shift in philosophy from life-prolonging to life-enhancing care. People living with a life-limiting illness will often receive palliative care through specialist outpatient clinics, while also being cared for by another medical specialty. Experiences of this point of care have been described as being liminal in character, that is, somewhere between living and dying. Drawing on experiences of illness and care taken from semistructured interviews with 30 palliative care outpatients in Australia, we found that this phase was frequently understood as concurrently living and dying. We suggest that this is a “parallax experience” involving narratives of a coherent linear self that is able to understand both realities, in a way that acknowledges the benefits of being multiple. These findings have significant implications for the ways in which palliative care is understood and how the self and subjectivity might be conceptualized at the end of life.

A semantic recommendation system for cancer-related articles

2018 ◽  
Vol 02 (02) ◽  
pp. 1850013
Author(s):  
Charles C. N. Wang ◽  
Yun-Lung Chung ◽  
I-Seng Chang ◽  
Jeffrey J. P. Tsai
Keyword(s):  
Cancer Research ◽  
Text Mining ◽  
Recommendation System ◽  
Cancer Diagnostics ◽  
User Needs ◽  
Treatment And Prevention ◽  
Recommendation Accuracy ◽  
Number Of Publications ◽  
Enormous Number ◽  
Semantic Computing

There have been an enormous number of publications on cancer research. These unstructured cancer-related articles are of great value for cancer diagnostics, treatment, and prevention. The aim of this study is to introduce a recommendation system. It combines text mining (LDA) and semantic computing (GloVe) to understand the meaning of user needs and to increase the recommendation accuracy.

scholarly journals SuperOrder: Provider order recommendation system for outpatient clinics

2019 ◽  
Vol 26 (2) ◽  
pp. 999-1016 ◽  
Author(s):  
Yi-Shan Sung ◽  
Ronald W Dravenstott ◽  
Jonathan D Darer ◽  
Priyantha D Devapriya ◽  
Soundar Kumara
Keyword(s):  
Recommendation System ◽  
Care Facility ◽  
Health Care Facility ◽  
Daily Basis ◽  
Outpatient Clinics ◽  
Electronic Health Record Data ◽  
Machine Learning Methods ◽  
Base Level ◽  
Meta Level ◽  
Record Data

This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A two-level prediction framework is proposed. At the base-level, the predictions are produced by aggregating three machine learning methods. The meta-level predictions are generated by integrating the base-level predictions with the order co-occurrence network. We used the retrospective data between 1 April 2014 and 31 March 2015 in pulmonary clinics from five hospital sites within a large rural health care facility in Pennsylvania to test the feasibility. With a decrease of 6 per cent in the precision, the improvement of the recall at the meta-level is approximately 20 per cent from the base-level. This demonstrates that the proposed order co-occurrence network helps in increasing the performance of order predictions. The implementation will bring a more effective and efficient way to place outpatient orders.

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