A machine learning solution for bed occupancy issue for smart healthcare sector

The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, out...

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Autores:: Gochhait, Dr Saikat
De-La-Hoz-Franco, Emiro
Shaheen, Qaisar
Diaz Martinez, Jorge Luis
Piñeres Espitia, Gabriel Dario
MERCADO POLO, DARWIN

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.eng.fl_str_mv	A machine learning solution for bed occupancy issue for smart healthcare sector
title	A machine learning solution for bed occupancy issue for smart healthcare sector
spellingShingle	A machine learning solution for bed occupancy issue for smart healthcare sector Algorithms Bed occupancy rate Healthcare Machine learning
title_short	A machine learning solution for bed occupancy issue for smart healthcare sector
title_full	A machine learning solution for bed occupancy issue for smart healthcare sector
title_fullStr	A machine learning solution for bed occupancy issue for smart healthcare sector
title_full_unstemmed	A machine learning solution for bed occupancy issue for smart healthcare sector
title_sort	A machine learning solution for bed occupancy issue for smart healthcare sector
dc.creator.fl_str_mv	Gochhait, Dr Saikat De-La-Hoz-Franco, Emiro Shaheen, Qaisar Diaz Martinez, Jorge Luis Piñeres Espitia, Gabriel Dario MERCADO POLO, DARWIN
dc.contributor.author.spa.fl_str_mv	Gochhait, Dr Saikat De-La-Hoz-Franco, Emiro Shaheen, Qaisar Diaz Martinez, Jorge Luis Piñeres Espitia, Gabriel Dario MERCADO POLO, DARWIN
dc.subject.proposal.eng.fl_str_mv	Algorithms Bed occupancy rate Healthcare Machine learning
topic	Algorithms Bed occupancy rate Healthcare Machine learning
description	The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and tele-medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services systems: (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a machine learning solution for patient admission scheduling autonomously. The proposed framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021
dc.date.accessioned.none.fl_str_mv	2022-08-25T00:01:36Z
dc.date.available.none.fl_str_mv	2022 2022-08-25T00:01:36Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.type.content.spa.fl_str_mv	Text
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dc.identifier.citation.spa.fl_str_mv	Gochhait, S., Butt, S.A., De-La-Hoz-Franco, E. et al. A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector. Aut. Control Comp. Sci. 55, 546–556 (2021). https://doi.org/10.3103/S0146411621060043
dc.identifier.issn.spa.fl_str_mv	0146-4116
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/9475
dc.identifier.url.spa.fl_str_mv	https://doi.org/10.3103/S0146411621060043
dc.identifier.doi.spa.fl_str_mv	10.3103/S0146411621060043
dc.identifier.eissn.spa.fl_str_mv	1558-108X
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv	REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.cuc.edu.co/
identifier_str_mv	Gochhait, S., Butt, S.A., De-La-Hoz-Franco, E. et al. A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector. Aut. Control Comp. Sci. 55, 546–556 (2021). https://doi.org/10.3103/S0146411621060043 0146-4116 10.3103/S0146411621060043 1558-108X Corporación Universidad de la Costa REDICUC - Repositorio CUC
url	https://hdl.handle.net/11323/9475 https://doi.org/10.3103/S0146411621060043 https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.ispartofjournal.spa.fl_str_mv	Automatic Control and Computer Sciences
dc.relation.references.spa.fl_str_mv	1 Ndurukia, Z., Njeru, A.W., Waiganjo, E. Factors influencing demand for micro insurance services in the insurance industry in Kenya, Int (2017) J. Acad. Res. Bus. Soc. Sci., 7, pp. 232-259. Cited 2 times. 2 Healthcare, and Pharmaceuticals (2017) The Economic Intelligence Unit 3 Roughley, S.D. Five years of the knime vernalis cheminformatics community contribution (Open Access) (2020) Current Medicinal Chemistry, 27 (38), pp. 6495-6522. Cited 6 times. https://www.eurekaselect.com/165102/article doi: 10.2174/0929867325666180904113616 4 Hildebrandt, H. Crossing the boundaries from individual medical care to regional public health outcomes: The triple aim of “Gesundes Kinzigtal” – better health + improved care + affordable costs (2014) Int. J. Integr. Care, 14 (5). Cited 3 times. 5 Kathpalia, B.S.K. (2015) WITHDRAWN: Disabilities found during services selection board medical examination – An overview 6 Dam, L. Comparative analysis of life insurance sector in India with BRIC nations, Anveshak – Int (2017) J. Manage., 6, pp. 66-75. 7 Gassmann, O., Schuhmacher, A., von Zedtwitz, M., Reepmeyer, G. Leading pharmaceutical innovation: How to win the life science race: Third edition (2018) Leading Pharmaceutical Innovation: How to Win the Life Science Race: Third Edition, pp. 1-179. Cited 4 times. https://www.springer.com/in/book/9783319668321 ISBN: 978-331966833-8; 978-331966832-1 doi: 10.1007/978-3-319-66833-8 8 Yoon, E., Lim, Y. A study on green building certification criteria for healthcare facilities–Focused on system and contents for healthcare in BREEAM, LEED, CASBEE (2016) J. Korea Inst. Healthcare Archit., 22, pp. 17-26. 9 Pujar, S.M., Kamat, R.K., Bansode, S.Y., Kamat, R.R., Katigennavar, S.H. Identifying and exploiting human needs for a people centric evolving knowledge society: A case study of Indian ICT Emergence (2008) International Information and Library Review, 40 (3), pp. 165-170. Cited 5 times. doi: 10.1080/10572317.2008.10762777 10 De-Loyde, K.J., Harrison, J.D., Durcinoska, I., Shepherd, H.L., Solomon, M.J., Young, J.M. Which information source is best? Concordance between patient report, clinician report and medical records of patient co-morbidity and adjuvant therapy health information (2015) Journal of Evaluation in Clinical Practice, 21 (2), pp. 339-346. Cited 20 times. http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2753 doi: 10.1111/jep.12327 11 Butt, S.A., Tariq, M.I. (2020) Big Data with Green Internet of Things (G-Iot) for the Development of Smart Cities and Technologies. Cited 2 times. 12 Beck, C., Georgiou, J. A wearable, multimodal, vitals acquisition unit for intelligent field triage (2016) Proceedings - IEEE International Symposium on Circuits and Systems, 2016-July, art. no. 7538853, pp. 1530-1533. Cited 7 times. ISBN: 978-147995340-0 doi: 10.1109/ISCAS.2016.7538853 13 Wang, J., Qiu, M., Guo, B Enabling real-time information service on telehealth system over cloud-based big data platform (Open Access) (2017) Journal of Systems Architecture, 72, pp. 69-79. Cited 69 times. https://www.journals.elsevier.com/journal-of-systems-architecture doi: 10.1016/j.sysarc.2016.05.003 14 Butt, S.A. Analysis of unfair means cases in computer-based examination systems (2016) Pac. Sci. Rev. B: Humanit. Soc. Sci., 2, pp. 75-79. Cited 7 times. 15 Hill, J., Nielsen, M., Fox, M.H. Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill. (Open Access) (2013) The Permanente journal, 17 (2), pp. 67-72. Cited 95 times. doi: 10.7812/TPP/12-099 16 Goodwin, N. How should integrated care address the challenge of people with complex health and social care needs? Emerging lessons from international case studies (Open Access) (2015) International Journal of Integrated Care, 15 (JULY), art. no. A006. Cited 15 times. http://www.ijic.org/index.php/ijic/article/download/2254/3038 doi: 10.5334/ijic.2254 17 Marmot, Sir, Friel, S., Bell, R., Houweling, T.A., Taylor, S. Closing the gap in a generation: health equity through action on the social determinants of health (Open Access) (2008) The Lancet, 372 (9650), pp. 1661-1669. Cited 1975 times. http://www.journals.elsevier.com/the-lancet/ doi: 10.1016/S0140-6736(08)61690-6 18 Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., (...), Forouzanfar, M.H. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015 (Open Access) (2017) The Lancet, 389 (10082), pp. 1907-1918. Cited 2827 times. http://www.journals.elsevier.com/the-lancet/ doi: 10.1016/S0140-6736(17)30505-6 19 Shaheen, Q., Shiraz, M., Khan, S., Majeed, R., Guizani, M., Khan, N., Aseere, A.M. Towards Energy Saving in Computational Clouds: Taxonomy, Review, and Open Challenges (Open Access) (2018) IEEE Access, 6, pp. 29407-29418. Cited 15 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2018.2833551 20 Tripathi, V.V.R., Tripathi, A., Jaiswal, S. Health welfare system in modern India revitalizing Indian healthcare–Its potential and challenges (2019) ZENITH Int. J. Multidiscip. Res., 9, pp. 178-193. Cited 2 times. 21 Prinja, S., Bahuguna, P., Gupta, I., Chowdhury, S., Trivedi, M. Role of insurance in determining utilization of healthcare and financial risk protection in India (Open Access) (2019) PLoS ONE, 14 (2), art. no. e0211793. Cited 29 times. https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0211793&type=printable doi: 10.1371/journal.pone.0211793 22 Jones, R. Links between bed occupancy, deaths and costs (2015) British Journal of Health Care Management, 21 (11), pp. 544-545. Cited 7 times. http://www.magonlinelibrary.com/doi/pdf/10.12968/bjhc.2015.21.11.544 doi: 10.12968/bjhc.2015.21.11.544 23 (2020) Guidelines on Core Components of Infection Prevention and Control Programmes at the National and Acute Health Care Facility Level. Cited 3 times. 24 Earnest, A., Chen, M.I., Ng, D., Leo, Y.S. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore (Open Access) (2005) BMC Health Services Research, 5, art. no. 36. Cited 101 times. http://www.biomedcentral.com/content/pdf/1472-6963-5-36.pdf doi: 10.1186/1472-6963-5-36 25 Kutafina, E., Bechtold, I., Kabino, K., Jonas, S.M. Recursive neural networks in hospital bed occupancy forecasting (Open Access) (2019) BMC Medical Informatics and Decision Making, 19 (1), art. no. 39. Cited 11 times. http://www.biomedcentral.com/bmcmedinformdecismak/ doi: 10.1186/s12911-019-0776-1 26 Awan, I.A., Shiraz, M., Hashmi, M.U., Shaheen, Q., Akhtar, R., Ditta, A. Secure Framework Enhancing AES Algorithm in Cloud Computing (Open Access) (2020) Security and Communication Networks, 2020, art. no. 8863345. Cited 12 times. https://www.hindawi.com/journals/scn/ doi: 10.1155/2020/8863345 27 Zhecheng, Z. An online short-term bed occupancy rate prediction procedure based on discrete event simulation (2014) J. Hosp. Adm., 3, pp. 37-42. Cited 4 times. 28 Butt, S.A., Gochhait, S., Andleeb, S., Adeel, M. (2021) Games features for health disciplines for patient learning as entertainment, Digital Entertainment. Cited 5 times. Palgrave Macmillan, Singapore 29 Butt, S.A., Anjum, M.W., Hassan, S.A., Garai, A., Onyema, E.M. 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spelling	Gochhait, Dr SaikatDe-La-Hoz-Franco, EmiroShaheen, QaisarDiaz Martinez, Jorge LuisPiñeres Espitia, Gabriel DarioMERCADO POLO, DARWIN2022-08-25T00:01:36Z20222022-08-25T00:01:36Z2021Gochhait, S., Butt, S.A., De-La-Hoz-Franco, E. et al. A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector. Aut. Control Comp. Sci. 55, 546–556 (2021). https://doi.org/10.3103/S01464116210600430146-4116https://hdl.handle.net/11323/9475https://doi.org/10.3103/S014641162106004310.3103/S01464116210600431558-108XCorporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and tele-medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services systems: (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a machine learning solution for patient admission scheduling autonomously. The proposed framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.12 páginasapplication/pdfengPleiades PublishingUnited StatesAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2022 Springer Nature Switzerland AG. Part of Springer Nature.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfA machine learning solution for bed occupancy issue for smart healthcare sectorArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://link.springer.com/article/10.3103/S0146411621060043Automatic Control and Computer Sciences1 Ndurukia, Z., Njeru, A.W., Waiganjo, E. Factors influencing demand for micro insurance services in the insurance industry in Kenya, Int (2017) J. Acad. Res. Bus. Soc. Sci., 7, pp. 232-259. Cited 2 times.2 Healthcare, and Pharmaceuticals (2017) The Economic Intelligence Unit3 Roughley, S.D. Five years of the knime vernalis cheminformatics community contribution (Open Access) (2020) Current Medicinal Chemistry, 27 (38), pp. 6495-6522. Cited 6 times. https://www.eurekaselect.com/165102/article doi: 10.2174/09298673256661809041136164 Hildebrandt, H. Crossing the boundaries from individual medical care to regional public health outcomes: The triple aim of “Gesundes Kinzigtal” – better health + improved care + affordable costs (2014) Int. J. Integr. Care, 14 (5). Cited 3 times.5 Kathpalia, B.S.K. (2015) WITHDRAWN: Disabilities found during services selection board medical examination – An overview6 Dam, L. Comparative analysis of life insurance sector in India with BRIC nations, Anveshak – Int (2017) J. Manage., 6, pp. 66-75.7 Gassmann, O., Schuhmacher, A., von Zedtwitz, M., Reepmeyer, G. Leading pharmaceutical innovation: How to win the life science race: Third edition (2018) Leading Pharmaceutical Innovation: How to Win the Life Science Race: Third Edition, pp. 1-179. Cited 4 times. https://www.springer.com/in/book/9783319668321 ISBN: 978-331966833-8; 978-331966832-1 doi: 10.1007/978-3-319-66833-88 Yoon, E., Lim, Y. A study on green building certification criteria for healthcare facilities–Focused on system and contents for healthcare in BREEAM, LEED, CASBEE (2016) J. Korea Inst. Healthcare Archit., 22, pp. 17-26.9 Pujar, S.M., Kamat, R.K., Bansode, S.Y., Kamat, R.R., Katigennavar, S.H. Identifying and exploiting human needs for a people centric evolving knowledge society: A case study of Indian ICT Emergence (2008) International Information and Library Review, 40 (3), pp. 165-170. Cited 5 times. doi: 10.1080/10572317.2008.1076277710 De-Loyde, K.J., Harrison, J.D., Durcinoska, I., Shepherd, H.L., Solomon, M.J., Young, J.M. Which information source is best? Concordance between patient report, clinician report and medical records of patient co-morbidity and adjuvant therapy health information (2015) Journal of Evaluation in Clinical Practice, 21 (2), pp. 339-346. Cited 20 times. http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2753 doi: 10.1111/jep.1232711 Butt, S.A., Tariq, M.I. (2020) Big Data with Green Internet of Things (G-Iot) for the Development of Smart Cities and Technologies. Cited 2 times.12 Beck, C., Georgiou, J. A wearable, multimodal, vitals acquisition unit for intelligent field triage (2016) Proceedings - IEEE International Symposium on Circuits and Systems, 2016-July, art. no. 7538853, pp. 1530-1533. Cited 7 times. ISBN: 978-147995340-0 doi: 10.1109/ISCAS.2016.753885313 Wang, J., Qiu, M., Guo, B Enabling real-time information service on telehealth system over cloud-based big data platform (Open Access) (2017) Journal of Systems Architecture, 72, pp. 69-79. Cited 69 times. https://www.journals.elsevier.com/journal-of-systems-architecture doi: 10.1016/j.sysarc.2016.05.00314 Butt, S.A. Analysis of unfair means cases in computer-based examination systems (2016) Pac. Sci. Rev. B: Humanit. Soc. Sci., 2, pp. 75-79. Cited 7 times.15 Hill, J., Nielsen, M., Fox, M.H. Understanding the social factors that contribute to diabetes: a means to informing health care and social policies for the chronically ill. (Open Access) (2013) The Permanente journal, 17 (2), pp. 67-72. Cited 95 times. doi: 10.7812/TPP/12-09916 Goodwin, N. How should integrated care address the challenge of people with complex health and social care needs? 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Cited 13 times. www.elsevier.com/locate/artmed doi: 10.1016/j.artmed.2016.03.001556546655AlgorithmsBed occupancy rateHealthcareMachine learningPublicationORIGINALA Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector.pdfA Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector.pdfapplication/pdf864574https://repositorio.cuc.edu.co/bitstreams/a0fb17bd-d9a1-4df7-a7a7-7f5eba092597/download824ce275ec123c38b726dea16cea448fMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/49aca493-fc92-4ab1-a548-cea45e981ddd/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTA Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector.pdf.txtA Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector.pdf.txttext/plain48544https://repositorio.cuc.edu.co/bitstreams/af2df642-6445-44c3-8c9b-0f873b808777/downloadbb7d0e59e55ee5f48309d9e3990187a7MD53THUMBNAILA Machine Learning 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A machine learning solution for bed occupancy issue for smart healthcare sector

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