Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi
Journal Title: Sir Syed University Reseacrh Journal of Engineering and Technology - Year 2013, Vol 3, Issue 1
Abstract
Usually elongated hospitalization is experienced by Burn patients, and the precise forecast of the placement of patient according to the healing acceleration has significant consequence on healthcare supply administration. Substantial amount of evidence suggest that sun light is essential to burns healing and could be exceptionally beneficial for burned patients and workforce in healthcare building. Satisfactory UV sunlight is fundamental for a calculated amount of burn to heal; this delicate rather complex matrix is achieved by applying pattern classification for the first time on the space syntax map of the floor plan and Browder chart of the burned patient. On the basis of the data determined from this specific healthcare learning technique, nurse can decide the location of the patient on the floor plan, hence patient safety first is the priority in the routine tasks by staff in healthcare settings. Whereas insufficient UV light and vitamin D can retard healing process, hence this experiment focuses on machine learning design in which pattern recognition and technology supports patient safety as our primary goal. In this experiment we lowered the adverse events from 2012- 2013, and nearly missed errors and prevented medical deaths up to 50% lower, as compared to the data of 2005- 2012 before this technique was incorporated. In this research paper, three distinctive phases of clinical situations are considered—primarily: admission, secondly: acute, and tertiary: post-treatment according to the burn pattern and healing rate—and be validated by capable AI- origin forecasting techniques to hypothesis placement prediction models for each clinical stage with varying percentage of burn i.e. superficial wound, partial thickness or full thickness deep burn. Conclusively we proved that the depth of burn is directly proportionate to the depth of patient’s placement in terms of window distance. Our findings support the hypothesis that the windowed wall is most healing wall, here fundamental suggestion is support vector machines: which is most advantageous hyper plane for linearly divisible patterns for the burns depth as well as the depth map is used.
Authors and Affiliations
Javaria Manzoor Shaikh, JaeSeung Park
Keywords
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Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi
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