【摘要】：伴隨著醫(yī)療衛(wèi)生事業(yè)及醫(yī)藥科技的快速發(fā)展,臨床醫(yī)療活動(dòng)中的藥物使用問題也逐步成為公眾關(guān)注的焦點(diǎn)。而隨著藥物的廣泛使用,藥源性疾病對(duì)人類健康的危害也越來越大,合理使用藥物是降低藥物不良反應(yīng)危害的重要前提。合理用藥需要根據(jù)疾病類型、病人情況和藥理學(xué)理論選擇最佳的藥物及其制劑,制定或調(diào)整給藥方案,以及有效、安全、經(jīng)濟(jì)地防治和治愈疾病的措施。 本文主要針對(duì)合理用藥問題,著重對(duì)系統(tǒng)的標(biāo)準(zhǔn)化診斷數(shù)據(jù)庫和過程記錄數(shù)據(jù)庫進(jìn)行分析研究,通過采用基于實(shí)例學(xué)習(xí)方法及粗糙集理論相關(guān)算法,最終使合理用藥系統(tǒng)具有自學(xué)習(xí)的功能,促進(jìn)臨床合理用藥。 粗糙集理論是一種用來處理不確定、不一致和不精確數(shù)據(jù)的數(shù)學(xué)理論。在本課題中,使用相應(yīng)的粗糙集算法,計(jì)算出決策屬性對(duì)條件屬性的依賴程度,確定了各特征屬性的權(quán)重值。另一方面,使用基于正區(qū)域的約簡(jiǎn)算法來處理藥品組合與治療效果數(shù)據(jù)表,將處理后所得的知識(shí)規(guī)則作為專家組制定新處方用藥方案的依據(jù)。 本文是將基于實(shí)例學(xué)習(xí)與粗糙集屬性權(quán)值化算法進(jìn)行融合,實(shí)現(xiàn)病例檢索,使醫(yī)生可將系統(tǒng)提供的醫(yī)囑方案作為給藥的參考。針對(duì)藥品組合與治療效果數(shù)據(jù)表,采用粗糙集約簡(jiǎn)算法,得到對(duì)治療起關(guān)鍵作用的藥品組合,該藥品組合為專家組提供了有效的支持。如上設(shè)計(jì)流程不但使系統(tǒng)實(shí)現(xiàn)自學(xué)習(xí),而且使合理用藥系統(tǒng)學(xué)習(xí)效率得到了提高。而本課題的研究成果,將對(duì)合理用藥的發(fā)展起著重要的作用,同時(shí)為臨床醫(yī)生給患者在治療過程中藥物治療方案的制定提供了支持。
[Abstract]:With the rapid development of medical and medical science and technology, the problem of drug use in clinical medical activities has gradually become the focus of public attention. With the wide use of drugs, drug-induced diseases are more and more harmful to human health. Rational use of drugs is an important prerequisite to reduce adverse drug reactions. Rational drug use needs to choose the best drug and its preparation according to the disease type, patient condition and pharmacology theory, to formulate or adjust the drug administration plan, and to prevent and cure the disease effectively, safely and economically. Aiming at the problem of rational use of drugs, this paper focuses on the analysis and research of the standardized diagnosis database and process record database of the system, and adopts the case-based learning method and rough set theory related algorithms. Finally, the rational drug use system has the function of self-learning and promotes the rational use of drugs in clinic. Rough set theory is a mathematical theory used to deal with uncertain, inconsistent and imprecise data. In this paper, the corresponding rough set algorithm is used to calculate the degree of dependence of decision attributes on conditional attributes, and the weights of each feature attribute are determined. On the other hand, a reduction algorithm based on positive region is used to deal with the data table of drug combination and therapeutic effect, and the rules of knowledge obtained from the treatment are taken as the basis for the expert group to formulate a new prescription drug use scheme. In this paper, case-based learning and rough set attribute weighted algorithm are fused to realize case retrieval, so that doctors can use the medical advice scheme provided by the system as the reference for drug delivery. According to the data table of drug combination and therapeutic effect, rough set reduction algorithm is used to obtain the drug combination which plays a key role in the treatment. The combination provides effective support for the expert group. The above design process not only makes the system realize self-learning, but also improves the learning efficiency of rational drug use system. The research results of this topic will play an important role in the development of rational drug use, and provide support for the clinicians to make the drug treatment plan.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP311.13;R318.6

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