Fuzzy logic assisted COVID 19 safety assessment of dental care

  1. Andrio Adwibowo

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Abstract

Uncertainty is significant when assessing a risk of certain health care facility conditions especially the facility that prone to the COVID 19 risk. One solution to deal with an uncertainty in health situation assessment is through fuzzy inference system. For that reason, this study aims to develop fuzzy assisted system to assess the safety of dental care related to the sets of patient and environmental conditions. The fuzzy system allows assessment based on the patient’s body temperature, travel history, dental care ventilation rate, and disinfection frequency. The fuzzy system incorporates several steps including fuzzification, fuzzy regulation, and defuzzification. As a result of this study, the fuzzy system is able to assess and identify the risk of dental care according to the patient’s health status and hygiene conditions of dental care as well. To conclude, fuzzy system used in this study has offered the advantage of assessing at any situation as for patient and environmental factor predicts the safety of dental care.

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    SciScore for 10.1101/2020.06.18.20134841: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.06.18.20134841v1 on medRxiv