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      Retracted: Comparison of Clinical Efficacy of Sodium Nitroprusside and Urapidil in the Treatment of Acute Hypertensive Cerebral Hemorrhage

      retraction
      Journal of Healthcare Engineering
      Hindawi

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          Abstract

          This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation The presence of these indicators undermines our confidence in the integrity of the article's content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process. In addition, our investigation has also shown that one or more of the following human-subject reporting requirements has not been met in this article: ethical approval by an Institutional Review Board (IRB) committee or equivalent, patient/participant consent to participate, and/or agreement to publish patient/participant details (where relevant). Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

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          • Record: found
          • Abstract: found
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          Comparison of Clinical Efficacy of Sodium Nitroprusside and Urapidil in the Treatment of Acute Hypertensive Cerebral Hemorrhage

          This paper mainly studies the clinical efficacy of sodium nitroprusside and urapidil in the treatment of acute hypertensive intracerebral hemorrhage and analyzes the brain CT image detection based on a deep learning algorithm. A total of 132 cases of acute hypertension admitted to XXX hospital from XX 2019 to XX 2020 were retrospectively analyzed. The diseases of all patients were clinically confirmed, and patients were divided into groups according to the differences in treatment methods. Urapidil was used for group 1; sodium nitroprusside was used for group 2; and urapidil combined with sodium nitroprusside was used for group 3. A convolutional neural network in deep learning is used to construct intelligent processing to classify brain CT images of patients. The network performance of AlexNet, GoogLeNet, and CNN3 is predicted. The results show that GoogLeNet has the highest prediction accuracy of 0.83, followed by AlexNet with 0.80 and CNN3 with 0.74. The results of the performance parameter curve show that the GoogLeNet has the highest performance parameter of 0.89, followed by AlexNet and CNN3 network. The performance parameter curve of machine learning is above 0.80. After five weeks of drug treatment, the hematoma volume was (3.8 ± 2.6) mL in group1, (7.6 ± 2.8) mL in group 2, and (2.8 ± 1.5) mL in group 3. After 5 days of treatment, the patients' heart rate changed compared with before treatment. Compared with group 2, there were significant differences between groups 1 and 3 (P < 0.01), indicating that the therapeutic effect of the combination group was significantly better than that of the other groups alone. In summary, the combination of sodium nitroprusside and urapidil has a significantly better effect than that of urapidil alone. A convolutional neural network based on deep learning improves the recognition accuracy of medical images.
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            Author and article information

            Contributors
            Journal
            J Healthc Eng
            J Healthc Eng
            JHE
            Journal of Healthcare Engineering
            Hindawi
            2040-2295
            2040-2309
            2023
            20 September 2023
            20 September 2023
            : 2023
            : 9807650
            Affiliations
            Article
            10.1155/2023/9807650
            10533249
            37772023
            28adc953-ae00-4758-b8b2-56985bdb9320
            Copyright © 2023 Journal of Healthcare Engineering.

            This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            History
            : 19 September 2023
            : 19 September 2023
            Categories
            Retraction

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