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@article{146308,
        author = {Suraj Bawankar and Dheerajkumar Pandey and Prince Rathore and Rajkush and Prof. A.A. Bamanikar},
        title = {A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {851-854},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146308},
        abstract = {Effective patient queue management to chop back patient wait delays and patient overcrowding is one altogether the foremost challenges featured by hospitals. In essential and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient at intervals the queue, the complete treatment time of all the patients before him is that the time that he ought to wait. would possibly it’d be convenient and fascinating if the patients might receive the foremost economical treatment organize and perceive the expected waiting time through a mobile application that updates in real time. Therefore, we've an inclination to propose a Patient Treatment Time Prediction (PTTP) recursive to predict the waiting time for every treatment task for a patient. We’ve an inclination to use realistic patient information from varied hospitals to urge a patient treatment time model for each task. Supported this large-scale, realistic data-set, the treatment time for every patient at intervals the current queue of each task is foreseen. Sustained the expected waiting time, a Hospital Queuing Recommendation (HQR) system is developed. HQR calculates Associate in Nursing predicts a cost-effective and convenient treatment established steered for the patient.},
        keywords = {Patient Treatment Time Prediction (PTTP), Hospital Queuing Recommendation (HQR), Random Forest (RF)},
        month = {},
        }