The q-rung orthopair fuzzy sets (q-ROFSs), originated by Yager, are good tools to describe fuzziness in human cognitive processes. The basic elements of q-ROFSs are q-rung orthopair fuzzy numbers (q-ROFNs), which are constructed by membership and nonmembership degrees. As realistic decision-making is very complicated, decision makers (DMs) may be hesitant among several values when determining membership and nonmembership degrees. By incorporating dual hesitant fuzzy sets (DHFSs) into q-ROFSs, we propose a new technique to deal with uncertainty, called q-rung dual hesitant fuzzy sets (q-RDHFSs). Subsequently, we propose a family of q-rung dual hesitant fuzzy Heronian mean operators for q-RDHFSs. Further, the newly developed aggregation operators are utilized in multiple attribute group decision-making (MAGDM). We used the proposed method to solve a most suitable supplier selection problem to demonstrate its effectiveness and usefulness. The merits and advantages of the proposed method are highlighted via comparison with existing MAGDM methods. The main contribution of this paper is that a new method for MAGDM is proposed.
As an extension of the intuitionistic fuzzy set (IFS), the recently proposed picture fuzzy set (PFS) is more suitable to describe decision-makers’ evaluation information in decision-making problems. Picture fuzzy aggregation operators are of high importance in multi-attribute decision-making (MADM) within a picture fuzzy decision-making environment. Hence, in this paper our main work is to introduce novel picture fuzzy aggregation operators. Firstly, we propose new picture fuzzy operational rules based on Dombi t-conorm and t-norm (DTT). Secondly, considering the existence of a broad and widespread correlation between attributes, we use Heronian mean (HM) information aggregation technology to fuse picture fuzzy numbers (PFNs) and propose new picture fuzzy aggregation operators. The proposed operators not only fuse individual attribute values, but also have a good ability to model the widespread correlation among attributes, making them more suitable for effectively solving increasingly complicated MADM problems. Hence, we introduce a new algorithm to handle MADM based on the proposed operators. Finally, we apply the newly developed method and algorithm in a supplier selection issue. The main novelties of this work are three-fold. Firstly, new operational laws for PFSs are proposed. Secondly, novel picture fuzzy aggregation operators are developed. Thirdly, a new approach for picture fuzzy MADM is proposed.
Retinoblastoma is a curable intraocular malignancy in children. However, in clinical practice, retinoblastoma can sometimes be misdiagnosed and mismanaged, leading to extraocular extension and even death. In this report, a series of 3 cases are related that emphasize the conditions and consequences resulting from misdiagnosis and mismanagement of retinoblastoma. The clinical features, imaging findings, histopatholigical examination, and management in 3 case reports of children with misdiagnosed retinoblastoma are presented. Two of the cases received pars plana vitrectomy after being misdiagnosed with Coats disease or ocular blunt trauma, whereas the third case received evisceration after being misdiagnosed with suppurative endophthalmitis. When the diagnosis of retinoblastoma had been confirmed after a second surgery was performed in our hospital, only 2 of the cases received adjuvant orbital radiotherapy. All 3 cases died of systemic tumor metastases. Intraocular surgical procedures should be avoided in any equivocal case until the possibility of latent retinoblastoma is eliminated.We strongly recommend that early enucleation be executed as soon as possible followed by postoperative adjuvant therapy under conditions wherein an intraocular surgery was inadvertently performed in an eye with retinoblastoma.
Objective This study aims to develop an insulin dosage adjustment model using machine learning of high quality electronic health records (EHRs) notes and then to form an artificial intelligence-based insulin clinical decision support workflow (iNCDSS) implemented in the HIS system to give a real-time recommendation of insulin dosage titration. The efficacy and safety in clinical practice is evaluated in this proof-of-concept study. Research design and methods We extracted patient-specific and time-varying features from the original EHRs data and performed machine learning analysis through 5-fold cross validation. In the patient-blind, single-arm interventional study, insulin dosage was titrated according to iNCDSS in type 2 diabetic inpatients for up to 7 d or until hospital discharge. The primary end point of the trial was the difference in glycemic control as measured by mean daily blood glucose concentration during the intervention period. Results A total of 3275 type 2 diabetic patients with 38,406 insulin counts were included for the model analysis. The XGBoost model presented the best performance with root mean square error (RMSE) of 1.06 unit and mean absolute relative difference (MARD) of 6.0% in the training dataset, and RMSE of 1.30 unit and MARD of 6.9% in the testing dataset. Twenty-three patients with T2DM (male 14, 60.9%; age 58.8 ± 10.7 years; duration of diabetes 11.8 ± 8.8 years, HbA1c 9.1 ± 1.1%) were enrolled in the proof of concept trial. The duration of iNCDSS intervention was 7.0 ± 0.1 d. The insulin dose recommended by iNCDSS was accepted by physicians in 97.8%. The mean daily capillary blood glucose was markedly improved during the intervention period, with a reduction of mean daily capillary BG from 11.3(8.0, 13.9) mmol/L in the first 24 h to 7.9(6.5,8.9) mmol/L in the last 24 h of the trial (P < 0.001). In addition, the time range below 3.9 mmol/L was decreased from 1.1% to 0.5%. Conclusions The clinical decision support system of insulin dosage titration developed using a machine learning algorithm based on the EHRs data was effective and safe in glycemic control in in type 2 diabetic inpatients. Trial registrations ClinicalTrials.gov Identifier: NCT04053959.
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