↓ Skip to main content

A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*

Overview of attention for article published in Methods of Information in Medicine, January 2018
Altmetric Badge

Mentioned by

facebook
1 Facebook page

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
14 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression*
Published in
Methods of Information in Medicine, January 2018
DOI 10.3414/me16-01-0112
Pubmed ID
Authors

Adel Alaeddini, Seung Hee Hong

Abstract

Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics. An extension of L1 / L2 regularization is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm. A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics. The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be effectively applied to medical centers with multiple clinics, especially those suffering from information scarcity on some type of disruptions and/or clinics.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 21%
Student > Ph. D. Student 3 21%
Student > Bachelor 2 14%
Student > Master 2 14%
Professor > Associate Professor 1 7%
Other 0 0%
Unknown 3 21%
Readers by discipline Count As %
Medicine and Dentistry 3 21%
Engineering 2 14%
Computer Science 1 7%
Business, Management and Accounting 1 7%
Psychology 1 7%
Other 1 7%
Unknown 5 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2017.
All research outputs
#20,444,703
of 22,999,744 outputs
Outputs from Methods of Information in Medicine
#610
of 662 outputs
Outputs of similar age
#378,207
of 441,131 outputs
Outputs of similar age from Methods of Information in Medicine
#399
of 432 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 662 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 441,131 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 432 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.