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Tahani Daghistani
Ministry of Naional Guard Health Affairs
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0268-2629
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Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in Spark platform. The aim of this paper is exploring factors that affect no-sow rate then can be used to formulate predictions using big data machine learning techniques.
Keywords
No-Show, Outpatient Clinics, Prediction Model, Big data, Spark
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CURRENT STATUS: Published
View the published article at Journal of Big Data.
No community comments so far
Editorial decision: Accept
On 25 Nov, 2020
Reviewer #2 agreed
On 15 Nov, 2020
Review #1 received
Received 15 Nov, 2020
Reviewer #1 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 09 Nov, 2020
Editor assigned
On 03 Nov, 2020
Submission checks complete
On 03 Nov, 2020
Editor invited
On 03 Nov, 2020
No comments provided
Editorial decision: Minor Revision
On 22 Oct, 2020
Review #3 received
Received 06 Oct, 2020
Review #2 received
Received 29 Sep, 2020
Reviewer #3 agreed
On 28 Sep, 2020
Reviewers invited
Invitations sent on 24 Sep, 2020
Reviewer #1 agreed
On 24 Sep, 2020
Reviewer #2 agreed
On 24 Sep, 2020
Review #1 received
Received 24 Sep, 2020
Editor assigned
On 23 Sep, 2020
Submission checks complete
On 22 Sep, 2020
Editor invited
On 22 Sep, 2020
No comments provided
Editorial decision: Minor Revision
On 07 Sep, 2020
Review #4 received
Received 01 Sep, 2020
Reviewer #4 agreed
On 23 Aug, 2020
Reviewer #3 agreed
On 04 Aug, 2020
Review #3 received
Received 04 Aug, 2020
Review #1 received
Received 03 Aug, 2020
Review #2 received
Received 03 Aug, 2020
Editor assigned
On 30 Jul, 2020
Reviewers invited
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Reviewer #1 agreed
On 30 Jul, 2020
Reviewer #2 agreed
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
Version 1
Posted 05 Jun, 2020
No comments provided
Editorial decision: Major Revision
On 27 Jun, 2020
Review #2 received
Received 19 Jun, 2020
Review #7 received
Received 16 Jun, 2020
Review #9 received
Received 16 Jun, 2020
Review #10 received
Received 16 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #9 agreed
On 15 Jun, 2020
Reviewer #10 agreed
On 15 Jun, 2020
Review #6 received
Received 10 Jun, 2020
Review #8 received
Received 10 Jun, 2020
Reviewer #8 agreed
On 09 Jun, 2020
Reviewer #4 agreed
On 08 Jun, 2020
Reviewer #5 agreed
On 08 Jun, 2020
Reviewer #6 agreed
On 08 Jun, 2020
Reviewer #7 agreed
On 08 Jun, 2020
Review #4 received
Received 08 Jun, 2020
Review #5 received
Received 08 Jun, 2020
Reviewer #3 agreed
On 07 Jun, 2020
Review #3 received
Received 07 Jun, 2020
Reviewer #2 agreed
On 05 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Reviewer #1 agreed
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A new preprint design is coming!
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See the published version of this preprint at Journal of Big Data.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Tahani Daghistani
Ministry of Naional Guard Health Affairs
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0268-2629
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Journal of Big Data.
No community comments so far
Editorial decision: Accept
On 25 Nov, 2020
Reviewer #2 agreed
On 15 Nov, 2020
Review #1 received
Received 15 Nov, 2020
Reviewer #1 agreed
On 12 Nov, 2020
Reviewers invited
Invitations sent on 09 Nov, 2020
Editor assigned
On 03 Nov, 2020
Submission checks complete
On 03 Nov, 2020
Editor invited
On 03 Nov, 2020
No comments provided
Editorial decision: Minor Revision
On 22 Oct, 2020
Review #3 received
Received 06 Oct, 2020
Review #2 received
Received 29 Sep, 2020
Reviewer #3 agreed
On 28 Sep, 2020
Reviewers invited
Invitations sent on 24 Sep, 2020
Reviewer #1 agreed
On 24 Sep, 2020
Reviewer #2 agreed
On 24 Sep, 2020
Review #1 received
Received 24 Sep, 2020
Editor assigned
On 23 Sep, 2020
Submission checks complete
On 22 Sep, 2020
Editor invited
On 22 Sep, 2020
No comments provided
Editorial decision: Minor Revision
On 07 Sep, 2020
Review #4 received
Received 01 Sep, 2020
Reviewer #4 agreed
On 23 Aug, 2020
Reviewer #3 agreed
On 04 Aug, 2020
Review #3 received
Received 04 Aug, 2020
Review #1 received
Received 03 Aug, 2020
Review #2 received
Received 03 Aug, 2020
Editor assigned
On 30 Jul, 2020
Reviewers invited
Invitations sent on 30 Jul, 2020
Reviewer #1 agreed
On 30 Jul, 2020
Reviewer #2 agreed
On 30 Jul, 2020
Submission checks complete
On 29 Jul, 2020
Editor invited
On 29 Jul, 2020
Version 1
Posted 05 Jun, 2020
No comments provided
Editorial decision: Major Revision
On 27 Jun, 2020
Review #2 received
Received 19 Jun, 2020
Review #7 received
Received 16 Jun, 2020
Review #9 received
Received 16 Jun, 2020
Review #10 received
Received 16 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #9 agreed
On 15 Jun, 2020
Reviewer #10 agreed
On 15 Jun, 2020
Review #6 received
Received 10 Jun, 2020
Review #8 received
Received 10 Jun, 2020
Reviewer #8 agreed
On 09 Jun, 2020
Reviewer #4 agreed
On 08 Jun, 2020
Reviewer #5 agreed
On 08 Jun, 2020
Reviewer #6 agreed
On 08 Jun, 2020
Reviewer #7 agreed
On 08 Jun, 2020
Review #4 received
Received 08 Jun, 2020
Review #5 received
Received 08 Jun, 2020
Reviewer #3 agreed
On 07 Jun, 2020
Review #3 received
Received 07 Jun, 2020
Reviewer #2 agreed
On 05 Jun, 2020
Reviewers invited
Invitations sent on 04 Jun, 2020
Reviewer #1 agreed
On 04 Jun, 2020
Editor assigned
On 03 Jun, 2020
First submitted
On 02 Jun, 2020
Submission checks complete
On 02 Jun, 2020
Editor invited
On 02 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Journal of Big Data.
Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in Spark platform. The aim of this paper is exploring factors that affect no-sow rate then can be used to formulate predictions using big data machine learning techniques.
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