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  • 27314-97-2 Introduction br Cancer pain is a common symptom

    2020-08-18

    1. Introduction
    Cancer pain is a common symptom of patients with terminal cancer, which is a major complication affecting the survival quality of patients with terminal cancer [1–7]. It is shown from related data that more than 70% of the patients with terminal cancer have different degree of pain symptoms, and 50% of the patients suffer from moderate and severe cancer pain and 30% of the patients suffer from extreme pain [8–15]. Therefore, increasing attention has been paid to the treatment of cancer pain in clinic [16–20].
    Anesthetic analgesic drugs play an important role in the treatment of cancer pain, which has a positive significance in relieving the pain and improving the survival quality of patients [21–23]. How-ever, continuous use of anesthetic drugs may lead to addiction and bring great harm to the patients [24]. With the development and popularization of WHO three-step analgesic principle for cancer, anesthetic pain relief is adopted for the cancer patient with pain according to this 27314-97-2 principle in clinic, which has achieved great effects [25]. In order to comply with the clinical use principles of anesthetic drugs and improve the anesthetic analgesic effect, this Study analyzes the specific situation of cancer patients with pain using anesthetic analgesic drugs in the people’s hospital of a certain city.
    2. Materials and methods
    2.1. General information
    102 patients with cancer pain hospitalizing in a certain domes-tic people’s hospital from 2014–2016 (male: 63 cases; female: 39 cases), aged 20 to 78, with an average age of (46.8 ± 11.2) years, were selected; there is no related contraindication of drugs and systemic infection.  Theorem 1. The posterior density vector in the analysis data param-eter silk for the use of anesthetic analgesic drugs in cancer patients also satisfies the Gaussian distribution characteristics, and its Gaussian matrix is in the form ofp silk yqi; σwi , γii ∼ N µsil , Ci , the
    of Gaussian matrix have the following form:
    2.2. Research methods
    The use of anesthetic analgesic drugs in 102 patients with cancer pain was analyzed retrospectively. The name, age and sex of the patients, name, specification, medication time and total dosage of anesthetic analgesic drugs were summarized. The defined drug dosage system (DDDs), drug utilization index (DUI) and the defined daily dose (DDD) were calculated for data processing. The specific use condition of anesthetic analgesic drugs was determined ac-cording to DUI. DDDs = total drug dosage/DDD. DUI = DDDs/actual days of medication. The higher the DDD, the more frequently the drug was used. If DUI < 1.0, it shows that the use of the drug is reasonable.
    3. Bayesian analysis model for the use of anesthetic analgesic drugs in cancer patients based on geometry reconstruction
    The basis to implement such algorithm is that: (1) the in-formation contained in the network model can be reallocated and re-executed through the geometric reconstruction process of Bayesian network to centralize the distributed information so that a small number of model nodes can contain most of the fault data;
    (2) the analysis signals of anesthetic analgesic drugs in cancer patients can be described in the multivariable form of Gaussian function so as to construct vector expression of analysis data for the use of anesthetic analgesic drugs in cancer patients with sparse characteristics. It is assumed that there is a right-angle projection in the subnet area of anesthetic analgesic drugs analysis for cancer patients, that is sle = U Tle vle , the vector is represented as follows:
    sile = [silk , 0nle −k] , silk = [sil1 , sil2 , . . . , silk ] (1) In such sector representation: the number of zero element is x, y, z, that of nonzero element is k, and k < nle , the parameter silk is the non-zero block in the analysis data for the use of anesthetic analgesic drugs in cancer patients, and its data scale is k. The multivariate representation model can be constructed by using Gaussian distribution. p(silk ) ∼ N(0, Ci0 ) ∑ Ci0 = γi i (2)
    In the formula, γi represents the scalar function in the analysis model for the use of anesthetic analgesic drugs in cancer patients and ∑i ∈ Rk×k is the positive definite parameter matrix in the analysis model for the use of anesthetic analgesic drugs in cancer patients. According to the defined Bayesian rule, it is defined that the noise interference existing in the analysis data for the use of anesthetic analgesic drugs in cancer patients is wqi and the dimensionality of such noise sector is M ×1 and the independently identically distributed data characteristics in random process are ( ) met, in the form of wqi ∼ N 0, σwi IM . The following theorem forms can be obtained:  where, the dimensionality of matrix Aulk = Aul(:,1:k) is M ×k, indicating that the data in first k column in matrix Aul are captured.