Heise D, Kroker P, Mailänder A
Medizinische Universitätsklinik, Abt. Pneumonologie, Homburg/Saar, Federal Republic of Germany.
Lung. 1990;168 Suppl:1193-200. doi: 10.1007/BF02718261.
We simulate the interpretation process by the testing of preformed working hypotheses. A clinical syndrome, "bronchial obstruction," is described by a set of suitable parameters (FEV1, MMEF, Raw, etc.). For a given patient, this set forms a normalized vector. It has to be compared with equivalent data derived from patients which fulfilled the criteria for the clinical syndrome in question. If the patient's vector has a similar direction as the vector of the collective, the working hypothesis is accepted. The length of the vector is then used to quantify the severity of the functional disturbances in verbal terms ("slight," "moderate," "severe"). The limits used for severity grading and the typical parameter pattern for the given syndrome are adapted to the user's criteria by a built-in learning capability. On the other hand, the assembled data may be used for the training of newcomers. The use of vector algorithms allows for a high flexibility of our program with respect to all methods used in lung function testing.
我们通过对预先形成的工作假设进行检验来模拟解释过程。一种临床综合征“支气管阻塞”由一组合适的参数（第一秒用力呼气容积、最大呼气中期流速、气道阻力等）来描述。对于给定的患者，这组参数构成一个归一化向量。必须将其与来自符合所讨论临床综合征标准的患者的等效数据进行比较。如果患者的向量与总体向量方向相似，则接受该工作假设。然后，向量的长度用于用文字描述功能障碍的严重程度（“轻度”“中度”“重度”）。通过内置的学习能力，用于严重程度分级的界限以及给定综合征的典型参数模式会根据用户标准进行调整。另一方面，汇总的数据可用于新手的培训。向量算法的使用使我们的程序在肺功能测试所使用的所有方法方面具有高度灵活性。
