José María Vargas (1786–1854): medical doctor and the first dean of the central univeristy of Venezuela

José María Vargas (1786–1854) was a multifaceted personality: Venezuelan politician, medical doctor and scientist. In 1827 he became the first dean of the Central University of Caracas. As a professional doctor, he made significant contribution to the teaching of medicine, his educational work encompassed many fields such as human anatomy, surgery, chemistry, etc. In 1827 he founded the Medical Society of Caracas. He served as president of Venezuela from 1835 to 1836. From 1839 to 1852 he was the president of the Directorate General of Education and created its first Code of Public Instruction for Universities and Academies. He carried out plenty of different medical researches and wrote many important books and papers.

Effective Strain Image Sequence Selection by Using Semi-Automated Image Processing Technique

One fourth of the cancer detected in women worldwide is breast cancer which leads this as a major threat for women. There are many methods of detecting cancer among which ultra-sound strain imaging is one of the promising techniques. However, in strain sequence, not all the frames show clear tumor visibility. Consequently, in this paper we tested some well-defined algorithms to find only those frames where the tumor is comparatively clearly visible. We have used Mean Pixel Difference (MPD) and Gray- Level Co-occurrence Matrix (GLCM) to find the frames with better tumor visibility. We have tested our methods in several real-life cases and the results have been examined by a professional doctor. The MPD has an accuracy of 96.2% and the GLCM. Contrast has that of 55.55%. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 8-13

Developing Medical Virtual Teaching Assistant Based on Speech Recognition Technology

This paper proposes to present the results of the development of the Virtual Teaching Assistant (VTA). This system is an e-learning module as a learning aid for medical students currently pursuing professional medical doctor in hospitals. In Indonesia, students of the medical doctor profession education must study and work in hospitals like an experienced doctor. They interact directly with patients and provide the same services as doctors. Every student has a professional doctor at the hospital as a mentor or companion. However, student meetings with accompanying doctors are minimal. It is not uncommon for students to encounter difficulties when dealing with patients, but they do not immediately receive guidance. As students, it is natural that they sometimes forget the theory. These students need a theoretical learning source that is fast and practical, which students can use between activities. We developed VTA to meet the needs of information and fast learning resources. VTA can run on computer, laptop, or smartphones by utilizing speech recognition technology. Students only need to ask questions in the form of speech using their everyday language, and VTA will provide answers. Although the VTA answer is still not satisfactory, it potentially to support Question and Answer-based mobile learning for particularly learning subject.

Deconstruction of Binaries and Role Reversal in Kalanithi’s When Breath Becomes Air

Physicians’ stories of their illness attempt to bridge the divide between a professional doctor and a patient’s narrative by combining both the versions. This research paper undertakes a narratological analysis of latest illness narrative written by a physician-turned-patient Paul Kalanithi in his When Breath Becomes Air. The present study also finds out the role reversal happening between a clinician, patient and writer. It further aims to analyze Paul Kalanithi’s autobiographical memoir as a literary narrative of his last stage fatal lung cancer. The paper highlights the link between literature and the medical world and in this way generates a better understanding of the present interdisciplinary relation of both the disciplines i.e. literature and medicine. This research is qualitative and descriptive while textual analysis has been used as a research method. This study ends with the findings and recommendations for further research.

Formation of language and speech skills for conducting the “doctor − patient” dialogue at lessons on Ukrainian as a foreign language

The author clarifies the importance of mastering the skills of professional doctor − patient dialogue for foreign students of medical universities as one of the main types of speech activity in their future profession. The article also presents a list of results expected after the completion of the “Professional speech” course. The author analyzes the methodological literature dedicated to the above problem, defines such concepts as “professional language”, “special language”, “professional readiness”, “medical spoken language” needed to understand the requirements for linguistic knowledge, speech skills of future doctors. The structure, content, and tasks of the professional dialogue “doctor − patient” are also determined. The professional dialogue has introductory, main and final parts, each with its peculiarities that concern grammar and vocabulary aspects. All three parts have different purposes. The task of the introductory part is to establish contact with the patient; that of the basic one is to get the information you need from the patient for successful further treatment; and the final one is to provide clear and coherent recommendations and to reach an agreement on the next steps. The author outlines the basic educational material which should be mastered by foreign students during the “Professional speech” course and suggests appropriate types of work for the development of professional speech culture. Formation of communicative tasks, understanding ways of their implementation through communicative strategies and tactics are found to be essential skills for future healthcare professionals. Each of the structural parts of the professional dialogue contains label units, and therefore students are required to know such traditional tokens to be able to use them correctly, and to understand the impact of these units on the interlocutor’s consciousness. The paper mentions basic principles of semantization of the new vocabulary, i.e. translation, clarity, suggestion of synonyms, antonyms or common root words, presentation of the context, etc. An emphasis is placed on the importance of students’ acquisition of certain speech genres for their successful future practice in medical institutions: invitation, order, request, recommendation, remarks, etc.

Evaluation of Feature Learning Methods for Voice Disorder Detection

Voice disorder is a frequently encountered health issue. Many people, however, either cannot afford to visit a professional doctor or neglect to take good care of their voice. In order to give a patient a preliminary diagnosis without using professional medical devices, previous research has shown that the detection of voice disorders can be carried out by utilizing machine learning and acoustic features extracted from voice recordings. Considering the increasing popularity of deep learning, feature learning and transfer learning, this study explores the possibilities of using these methods to assign voice recordings into one of two classes—Normal and Pathological. While the results show the general viability of deep learning and feature learning for the automatic recognition of voice disorders, they also lead to discussions on how to choose a pre-trained model when using transfer learning for this task. Furthermore, the results demonstrate the shortcomings of the existing datasets for voice disorder detection such as insufficient dataset size and lack of generality.

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.