ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Efficient Patient Inflow Prediction Model for Hospital Resource Management


Affiliations
1 Department of Information Technology, AMET University, India
     

   Subscribe/Renew Journal


There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

Keywords

Artificial Inteligence, Forecasting, Optimization, Prediction.
Subscription Login to verify subscription
User
Notifications
Font Size

  • S. Ivatts and P. Millard, “Health Care Modelling: Why Should We Try?”, British Journal of Health Care Management, Vol. 8, No. 6, pp. 218-222, 2002.

  • Victorian Public Hospitals, “Performance Monitoring Framework, Available at: https://www2.health.vic.gov.au/hospitals-and-health-services/funding-performance-accountability/performancemonitoring.

  • D.M. Fatovich, Y. Nagree and P. Sprivulis, “Access Block causes Emergency Department Overcrowding and Ambulance Diversion in Perth, Western Australia”, Emergency Medicine Journal, Vol. 22, No. 5, pp. 351-354, 2005.

  • K. Peleg and J.S. Pliskin, “A Geographic Information System Simulation Model of EMS: Reducing Ambulance Response Time”, The American Journal of Emergency Medicine, Vol. 22, No. 3, pp. 164-170, 2004.

  • F.L. Henriksen, P. Schorling, B. Hansen, H. Schakow and M.L. Larsen, “FirstAED Emergency Dispatch, Global Positioning of First Responders with Distinct Roles - A Solution to Reduce the Response Times and Ensuring Early Defibrillation in the Rural Area Langeland”, Proceedings of International Conference on Well-Being in the Information Society, pp. 36-45, 2014.

  • J. Fitch, “Response Times: Myths, Measurement and Management”, Journal of Emergency Medical Services, Vol. 30, No. 9, pp. 47-56, 2005.

  • S.A. Simonsen, M. Andresen, L. Michelsen, S. Viereck, F.K. Lippert and H.K. Iversen, “Evaluation of Pre-Hospital Transport Time of Stroke Patients to Thrombolytic Treatment”, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, Vol. 22, No. 65, pp. 1-5, 2014.

  • A. Timma, M. Maegelea, R. Leferingb, K. Wendtc and H.Wyend, “Pre-Hospital Rescue Times and Actions in Severe Trauma. A Comparison between Two Trauma Systems: Germany and the Netherlands”, Injury, Vol. 45, No. 3, pp.43-52, 2014.

  • Shane G. Henderson and Andrew J. Mason, “Ambulance Service Planning: Simulation and Data Visualisation”, International Series in Operations Research and Management Science, Vol. 70, pp. 77-102, 2004.

  • Cheng Siong Lim, Rosbi Mamat and Thomas Braunl, “Impact of Ambulance Dispatch Policies on Performance of Emergency Medical Services”, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 2, pp. 624632, 2011.

  • N. Geroliminis, M.G. Karlaftis and A. Skabardonis, “A spatial queuing model for the emergency vehicle districting and location problem”, Transportation Research Part B: Methodological, Vol. 43, No. 7, pp. 798-811, 2009.

  • H.K. Rajagopalan, “Ambulance Deployment and Shift Scheduling: An Integrated Approach”, Journal of Service Science and Management, Vol. 4, No. 1, pp. 66-78, 2011.

  • Elif Akcali, Murray J. Cote and Chin Lin, “A Network Flow Approach to Optimizing Hospital Bed Capacity Decisions”, Health Care Management Science, Vol. 9, No. 4, pp. 391404, 2006.

  • R. Zongwei, L. Chuanqing and G. Haini, “Strategy on Doctor Resource Sharing among Hospitals composed Regional Medical Association based on Game Theory”, Proceedings of 3rd International Conference on Information Management, pp. 274-278, 2017.

  • H. Xie T.J. Chaussalet and P.H. Millard, “A Model-based Approach to the Analysis of Patterns of Length of Stay in Institutional Long-Term Care”, IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 3, pp.512-518, 2006.

  • Xiao Chen, Liangmin Wang, Jie Ding and Nigel Thomas, “Patient Flow Scheduling and Capacity Planning in a Smart Hospital Environment”, IEEE Access, Vol. 4, pp. 135-148, 2016.

  • H. Xu, W. Wu, S. Nemati and H. Zha, “Patient Flow Prediction via Discriminative Learning of MutuallyCorrecting Processes”, Proceedings of 33rd International Conference on Data Engineering, pp. 37-38, 2017.

  • N. Channouf, P. L’Ecuyer, A. Ingolfsson and A.N.Avramidis, “The Application of Forecasting Techniques to Modeling Emergency Medical System Calls in Calgary, Alberta”, Health Care Management Science, Vol. 10, No. 1, pp. 25-45, 2007.

  • Lawrence H. Brown, E. Brooke Lerner, Baxter Larmon, Todd Le Gassick and Michael Taigman, “Are EMS Call Volume Predictions based on Demand Pattern Analysis Accurate?”, Prehospital Emergency Care, Vol. 11, No. 2, pp.199-203, 2007.

  • Ho-Ting Wong and Poh-Chin Lai, “Weather Factors in the Short-Term Forecasting of Daily Ambulance Calls”, International Journal of Biometeorology, Vol. 58, No. 5, pp.669-678, 2014.

  • H. Setzler, C. Saydam and S. Park, “EMS Call Volume Predictions: A Comparative Study”, Computers and Operations Research, Vol. 36, No. 6, pp. 1843-1851, 2009.

  • Riad Alharbey, “Predictive Analytics Dashboard for Monitoring Patients in Advanced Stages of COPD”, Proceedings of 49th Hawaii International Conference on System Sciences, pp. 3455-3461, 2016.

  • Debasish Basak, Srimanta Pal and Dipak Chandra Patranabis, “Support Vector Regression”, Neural Information Processing-Letters and Reviews, Vol. 11, No.10, pp. 203-224, 2007.

  • G. Camps Valls et al., “Prediction of Cyclosporine Dosage in Patients after kidney Transplantation using Neural Networks”, IEEE Transactions on Biomedical Engineering, Vol. 50, No. 4, pp. 442-448, 2003.

  • A.S. Weigend and N.A. Gershenfeld, “Time Series Prediction. Forecasting the Future and Understanding the Past”, Proceedings of Advanced Research Workshop on Comparative Time Series Analysis, pp. 279-313, 1992.

  • E.A. Wan, “Finite Impulse Response Neural Networks with Applications in Time Series Prediction”, Ph.D. Dissertation, Department of Electrical Engineering, Stanford University, 1993.

  • Jeffrey L. Elman, “Finding Structure in Time”, Cognitive Science, Vol. 14, No. 2, pp.179-211, 1988.

  • A.Y. Chen, T.Y. Lu, M.H.M. Ma and W.Z. Sun, “Demand Forecast using Data Analytics for the Preallocation of Ambulances”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 4, pp. 1178-1187, 2016.

  • HES Outpatient dataset, Available at: http://www.adls.ac.uk/hscic/nhs-hes/?detail.

  • H. Setzler, C. Saydam and S. Park, “EMS Call Volume Predictions: A Comparative Study”, Computers and Operations Research, Vol. 36, No. 6, pp. 1843-1851, 2009.

  • C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

  • C.D. Lewis, “Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting”, Butterworth Scientific, 1982.

  • J. Chen, K. Li, Z. Tang, K. Bilal and K. Li, “A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment”, IEEE Access, Vol. 4, pp. 1767-1783, 2016

  • A. Bagnasco, M. Saviozzi, F. Silvestro, A. Vinci, S. Grillo and E. Zennaro, “Artificial Neural Network Application to Load Forecasting in a Large Hospital Facility”, Proceedings of International Conference on Probabilistic Methods Applied to Power Systems, pp. 1-6, 2014.

  • L. Garg, S.I. McClean, M. Barton, B.J. Meenan and K. Fullerton, “Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems”, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 42, No. 6, pp. 1332-1345, 2012.

  • G. Guidi, M.C. Pettenati, P. Melillo and E. Iadanza, “A Machine Learning System to Improve Heart Failure Patient Assistance”, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 6, pp. 1750-1756, 2014.

  • X. Shi, Y. Hu, Y. Zhang, W. Li, Y. Hao, A. Alelaiwi, S.M.M. Rahman and M.S. Hossain, “Multiple Disease Risk Assessment with Uniform Model based on Medical Clinical Notes”, IEEE Access, Vol. 4, pp. 7047-7083, 2016.

  • S. Walczak, “Artificial Neural Network Medical Decision Support Tool: Predicting Transfusion Requirements of ER Patients”, IEEE Transactions on Information Technology in Biomedicine, Vol. 9, No. 3, pp. 468-474, 2005.

  • Hospital Episode Statistics (HES): Outpatient ActivityProvider-level analysis, Available at: http://data.gov.uk/dataset/hospital_outpatient_activity.


Abstract Views: 267

PDF Views: 4




  • An Efficient Patient Inflow Prediction Model for Hospital Resource Management

Abstract Views: 267  |  PDF Views: 4

Authors

Kottalanka Srikanth
Department of Information Technology, AMET University, India
D. Arivazhagan
Department of Information Technology, AMET University, India

Abstract


There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

Keywords


Artificial Inteligence, Forecasting, Optimization, Prediction.

References