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A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs

Overview of attention for article published in Computer Methods & Programs in Biomedicine, February 2017
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Title
A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs
Published in
Computer Methods & Programs in Biomedicine, February 2017
DOI 10.1016/j.cmpb.2016.10.018
Pubmed ID
Authors

Abdur Rahim Mohammad Forkan, Ibrahim Khalil

Abstract

In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making.

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 31%
Researcher 12 18%
Student > Master 11 16%
Student > Bachelor 5 7%
Student > Doctoral Student 4 6%
Other 9 13%
Unknown 5 7%
Readers by discipline Count As %
Computer Science 19 28%
Engineering 12 18%
Medicine and Dentistry 9 13%
Nursing and Health Professions 6 9%
Social Sciences 2 3%
Other 7 10%
Unknown 12 18%

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 16 March 2017.
All research outputs
#10,880,706
of 12,277,248 outputs
Outputs from Computer Methods & Programs in Biomedicine
#667
of 861 outputs
Outputs of similar age
#244,327
of 289,016 outputs
Outputs of similar age from Computer Methods & Programs in Biomedicine
#18
of 34 outputs
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We're also able to compare this research output to 34 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.