China 
Africa 
Explore chapters and articles related to this topic
Extraction of Medical Entities Using a Matrix-Based Pattern-Matching Method
Published in Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey, Applied Intelligent Decision Making in Machine Learning, 2020
A single word entity is easy to find and classify, but boundary detection of the sequence of entities is still a vital issue in clinical text processing. For example, “oxycodone–acetaminophen,” “saphaneous vein graft → posterior descending artery,” and “a permanent dual chamber rate responsive pacemaker.” In the proposed work the problem of correct boundary identification of clinical concepts is explored. The proposed system uses Part of Speech (POS) as a feature for training the model. The system is based on a matrix model and performs multi-pattern matching. These matched patterns are converted to their corresponding words and mapped with a unified modeling language system (UMLS) for entity classification. The rest of this chapter is organized as follows. Section 8.2 illustrates the background of clinical named-entity recognition, Section 8.3 describes the proposed method and dataset, Section 8.4 presents system evaluation, Section 8.5 provides the experimental results and a discussion, and Section 8.6 concludes and indicates some new directions for further research.
Chest imaging
Published in Sarah McWilliams, Practical Radiological Anatomy, 2011
o The vessel that supplies the posterior descending artery to the inferior surface of the heart is called dominant. There is usually right dominance, i.e. the right coronary artery supplies the inferior heart/posterior descending artery. The right coronary may be small when the left cir-cumflex supplies the inferior heart, called left dominance (Fig. 3.56).
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Published in Automatika, 2023
K. Kavipriya, Manjunatha Hiremath
Figure 7 shows the final result of the computational method; 7(a) is a left artery image of paitent_1 in that the stenosis is detected in the left anterior descending (LAD). Figure 7(b) is the right artery of the patient_2 and the stenosis is in the proximal right coronary artery (RCA). Figure 7(c) is the right artery of the patient_3 and the detected stenosis is in the posterior descending artery (PDA). Figure 7(d) is the right artery of patient_4 and stenosis is in Mid RCA. This proposed stenosis detection method locates the stenosis in the angiogram image effectively.