Use of Type-2 Fuzzy Sets for Military Value of Information Decision Support

IntroductionInnovative approaches are needed to design robust clinical decision support (CDS) to optimize hospital glycemic management. We piloted an electronic medical record (EMR), evidence-based algorithmic CDS tool in an academic center to alert clinicians in real time about gaps in care related to inpatient glucose control and insulin utilization, and to provide management recommendations.Research design and methodsThe tool was designed to identify clinical situations in need for action: (1) severe or recurrent hyperglycemia in patients with diabetes: blood glucose (BG) ≥13.88 mmol/L (250 mg/dL) at least once or BG ≥10.0 mmol/L (180 mg/dL) at least twice, respectively; (2) recurrent hyperglycemia in patients with stress hyperglycemia: BG ≥10.0 mmol/L (180 mg/dL) at least twice; (3) impending or established hypoglycemia: BG 3.9–4.4 mmol/L (70–80 mg/dL) or ≤3.9 mmol/L (70 mg/dL); and (4) inappropriate sliding scale insulin (SSI) monotherapy in recurrent hyperglycemia, or anytime in patients with type 1 diabetes. The EMR CDS was active (ON) for 6 months for all adult hospital patients and inactive (OFF) for 6 months. We prospectively identified and compared gaps in care between ON and OFF periods.ResultsWhen active, the hospital CDS tool significantly reduced events of recurrent hyperglycemia in patients with type 1 and type 2 diabetes (3342 vs 3701, OR=0.88, p=0.050) and in patients with stress hyperglycemia (288 vs 506, OR=0.60, p<0.001). Hypoglycemia or impending hypoglycemia (1548 vs 1349, OR=1.15, p=0.050) were unrelated to the CDS tool on subsequent analysis. Inappropriate use of SSI monotherapy in type 1 diabetes (10 vs 22, OR=0.36, p=0.073), inappropriate use of SSI monotherapy in type 2 diabetes (2519 vs 2748, OR=0.97, p=0.632), and in stress hyperglycemia subjects (1617 vs 1488, OR=1.30, p<0.001) were recognized.ConclusionEMR CDS was successful in reducing hyperglycemic events among hospitalized patients with dysglycemia and diabetes, and inappropriate insulin use in patients with type 1 diabetes.

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Hazard identification, risk assessment and control for dam construction safety using an integrated BWM and MARCOS approach under interval type-2 fuzzy sets environment

Automation in Construction ◽

10.1016/j.autcon.2021.103699 ◽

2021 ◽

Vol 127 ◽

pp. 103699

Author(s):

Erkan Celik ◽

Muhammet Gul

Keyword(s):

Risk Assessment ◽

Fuzzy Sets ◽

Construction Safety ◽

Hazard Identification ◽

Dam Construction ◽

Interval Type ◽

And Control

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An interval type-2 hesitant fuzzy best-worst method

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202801 ◽

2021 ◽

pp. 1-28

Author(s):

Ashraf Norouzi ◽

Hossein Razavi hajiagha

Keyword(s):

Decision Making ◽

Fuzzy Sets ◽

Group Decision Making ◽

Group Decision ◽

Fuzzy Ahp ◽

Uncertain Data ◽

Interval Type ◽

Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

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