Word Sense Disambiguation for clinical abbreviations

Abbreviations are extensively used in electronic health records (EHR) of patients as well as medical documentation, reaching 30-50% of the words in clinical narrative. There are more than 197,000 unique medical abbreviations found in the clinical text and their meanings vary depending on the context in which they are used. Since data in electronic health records could be shareable across health information systems (hospitals, primary care centers, etc.) as well as others such as insurance companies information systems, it is essential determining the correct meaning of the abbreviations to avoid misunderstandings. Clinical abbreviations have specific characteristic that do not follow any standard rules for creating them. This makes it complicated to find said abbreviations and corresponding meanings. Furthermore, there is an added difficulty to working with clinical data due to privacy reasons, since it is essential to have them in order to develop and test algorithms. Word sense disambiguation (WSD) is an essential task in natural language processing (NLP) applications such as information extraction, chatbots and summarization systems among others. WSD aims to identify the correct meaning of the ambiguous word which has more than one meaning. Disambiguating clinical abbreviations is a type of lexical sample WSD task. Previous research works adopted supervised, unsupervised and Knowledge-based (KB) approaches to disambiguate clinical abbreviations. This thesis aims to propose a classification model that apart from disambiguating well known abbreviations also disambiguates rare and unseen abbreviations using the most recent deep neural network architectures for language modeling. In clinical abbreviation disambiguation several resources and disambiguation models were encountered. Different classification approaches used to disambiguate the clinical abbreviations were investigated in this thesis. Considering that computers do not directly understand texts, different data representations were implemented to capture the meaning of the words. Since it is also necessary to measure the performance of algorithms, the evaluation measurements used are discussed. As the different solutions proposed to clinical WSD we have explored static word embeddings data representation on 13 English clinical abbreviations of the UMN data set (from University of Minnesota) by testing traditional supervised machine learning algorithms separately for each abbreviation. Moreover, we have utilized a transformer-base pretrained model that was fine-tuned as a multi-classification classifier for the whole data set (75 abbreviations of the UMN data set). The aim of implementing just one multi-class classifier is to predict rare and unseen abbreviations that are most common in clinical narrative. Additionally, other experiments were conducted for a different type of abbreviations (scientific abbreviations and acronyms) by defining a hybrid approach composed of supervised and knowledge-based approaches. Most previous works tend to build a separated classifier for each clinical abbreviation, tending to leverage different data resources to overcome the data acquisition bottleneck. However, those models were restricted to disambiguate terms that have been seen in trained data. Meanwhile, based on our results, transfer learning by fine-tuning a transformer-based model could predict rare and unseen abbreviations. A remaining challenge for future work is to improve the model to automate the disambiguation of clinical abbreviations on run-time systems by implementing self-supervised learning models.Las abreviaturas se utilizan ampliamente en las historias clínicas electrónicas de los pacientes y en mucha documentación médica, llegando a ser un 30-50% de las palabras empleadas en narrativa clínica. Existen más de 197.000 abreviaturas únicas usadas en textos clínicos siendo términos altamente ambiguos El significado de las abreviaturas varía en función del contexto en el que se utilicen. Dado que los datos de las historias clínicas electrónicas pueden compartirse entre servicios, hospitales, centros de atención primaria así como otras organizaciones como por ejemplo, las compañías de seguros es fundamental determinar el significado correcto de las abreviaturas para evitar además eventos adversos relacionados con la seguridad del paciente. Nuevas abreviaturas clínicas aparecen constantemente y tienen la característica específica de que no siguen ningún estándar para su creación. Esto hace que sea muy difícil disponer de un recurso con todas las abreviaturas y todos sus significados. A todo esto hay que añadir la dificultad para trabajar con datos clínicos por cuestiones de privacidad cuando es esencial disponer de ellos para poder desarrollar algoritmos para su tratamiento. La desambiguación del sentido de las palabras (WSD, en inglés) es una tarea esencial en tareas de procesamiento del lenguaje natural (PLN) como extracción de información, chatbots o generadores de resúmenes, entre otros. WSD tiene como objetivo identificar el significado correcto de una palabra ambigua (que tiene más de un significado). Esta tarea se ha abordado previamente utilizando tanto enfoques supervisados, no supervisados así como basados en conocimiento. Esta tesis tiene como objetivo definir un modelo de clasificación que además de desambiguar abreviaturas conocidas desambigüe también abreviaturas menos frecuentes que no han aparecido previamente en los conjuntos de entrenaminto utilizando las arquitecturas de redes neuronales profundas más recientes relacionadas ocn los modelos del lenguaje. En la desambiguación de abreviaturas clínicas se emplean diversos recursos y modelos de desambiguación. Se han investigado los diferentes enfoques de clasificación utilizados para desambiguar las abreviaturas clínicas. Dado que un ordenador no comprende directamente los textos, se han implementado diferentes representaciones de textos para capturar el significado de las palabras. Puesto que también es necesario medir el desempeño de cualquier algoritmo, se describen también las medidas de evaluación utilizadas. La mayoría de los trabajos previos se han basado en la construcción de un clasificador separado para cada abreviatura clínica. De este modo, tienden a aprovechar diferentes recursos de datos para superar el cuello de botella de la adquisición de datos. Sin embargo, estos modelos se limitaban a desambiguar con los datos para los que el sistema había sido entrenado. Se han explorado además representaciones basadas vectores de palabras (word embeddings) estáticos para 13 abreviaturas clínicas en el corpus UMN en inglés (de la University of Minnesota) utilizando algoritmos de clasificación tradicionales de aprendizaje automático supervisados (un clasificador por cada abreviatura). Se ha llevado a cabo un segundo experimento utilizando un modelo multi-clasificador sobre todo el conjunto de las 75 abreviaturas del corpus UMN basado en un modelo Transformer pre-entrenado. El objetivo ha sido implementar un clasificador multiclase para predecir también abreviaturas raras y no vistas. Se realizó un experimento adicional para siglas científicas en documentos de dominio abierto mediante la aplicación de un enfoque híbrido compuesto por enfoques supervisados y basados en el conocimiento. Así, basándonos en los resultados de esta tesis, el aprendizaje por transferencia (transfer learning) mediante el ajuste (fine-tuning) de un modelo de lenguaje preentrenado podría predecir abreviaturas raras y no vistas sin necesidad de entrenarlas previamente. Un reto pendiente para el trabajo futuro es mejorar el modelo para automatizar la desambiguación de las abreviaturas clínicas en tiempo de ejecución mediante la implementación de modelos de aprendizaje autosupervisados.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Israel González Carrasco.- Secretario: Leonardo Campillos Llanos.- Vocal: Ana María García Serran

Disambiguating Clinical Abbreviations using Pre-trained Word Embeddings

Thanks to Palestine Technical University-Kadoorie and Deep EMR project(TIN2017-87548-C2-1-R)for partially funding this work

Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques

Abbreviations are considered an essential part of the clinical narrative; they are used not only to save time and space but also to hide serious or incurable illnesses. Misreckoning interpretation of the clinical abbreviations could affect different aspects concerning patients themselves or other services like clinical support systems. There is no consensus in the scientific community to create new abbreviations, making it difficult to understand them. Disambiguate clinical abbreviations aim to predict the exact meaning of the abbreviation based on context, a crucial step in understanding clinical notesThis work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation) and Palestine Technical University - Kadoorie (Palestine). The work was also supported by the PID2020-116527RB-I00 project

INTEGRATION OF ICT IN EFL/ESL TEACHERS' TRAINING AND SELF-EFFICACY BELIEFS AS PERCEIVED BY THE TRAINERS

Purpose of Study: The current study aimed at revealing the integration of information and communications technology (ICT) in EFL/ESL teachers' training and self-efficacy beliefs as perceived by trainers. A group of (64) trainers from different countries (Palestine, UK, USA, Iran, Lebanon, Yemen, Iraq) completed the two instruments of the study. Methodology: The first instrument was a survey of the actuality of ICT integration in the training; it comprises (47) items distributed into nine domains, i.e.  PowerPoint, Facebook, Wiki, YouTube, Blogs, Email, Google, Mobile, and Platform\E-course. The second instrument was a self-efficacy scale which consists of (14) items. The results of trainers' responses revealed that Emails, Mobile, and Google are often used with relative weights (%78.59, %68.13, and %68.02) respectively, whereas Wikis were never used i.e. relative weight (%28.96). The differences in integrating ICTs between male and female trainers were statistically insignificant. Furthermore, there were no statistically significant differences due to the respondents' period of experience. Results: The results also showed that there were no statistically significant differences in the respondents' integration of ICT due to country of origin. The trainers' self-efficacy wobbles between 66.88 and 58.13 with a total of 61.70, which is moderate. Based on the study findings, the researchers recommend arousing trainers and trainees' awareness regarding integrating more ICTs in their training courses and encouraging them to try the different ICTs which make it easier for trainees to grasp the training material

The Novelty in Terms of Jurisdiction and Procedural System for the Enforcement of Foreign Arbitration Awards In the United Arab Emirates

يجسد التنفيذ الاختياري والجبري غاية طلب التحكيم لكل من يحصل على حكم يحقق المراد من الدعوى التحكيمية. وفي نطاق أحكام التحكيم الأجنبية، فإن الترخيص بدون شروط أو قيود وطنية لتنفيذ أحكام التحكيم الأجنبية يؤدي إلى المساس بسيادة الدولة على إقليمها، لذا أصبح الأمر بالتنفيذ هو الأداة الرقابية القضائية التي يفرضها المشرع الوطني على إرادة الأطراف، فهي رقابة لاحقة على صدور حكم التحكيم – الوطني أو الدولي أو الأجنبي -حين يراد تنفيذه. ونظراً لتباين التشريعات الدولية والوطنية في شروط وإجراءات تنفيذ الاحكام التحكيمية الأجنبية فإن مشكلة الدراسة تكمن في معرفة أي النصوص الواجبة التطبيق لتقدير مدى توافر اختصاص وإجراءات استصدار الأمر بتنفيذ هذه الأحكام من ناحية، وشروط استصدارها من ناحية أخرى. وذلك كله باتباع منهج تحليلي وخطة تستند إلى مبحثين، هما: جهة الاختصاص بطلب الأمر بالتنفيذ (مبحث أول)، ونظامه الإجرائي (مبحث ثان)

Responsibility for Others' Actions in Electronic Transactions

إن المسؤولية كإحدى مصادر الالتزام، جاءت لتقرير مسؤولية كل من يخلّ بقواعد المشروعية، ما يلحق ضرراً بالآخرين، وبهذا فهي أضمن لإبقاء تصرفات الموردين والتجار ضمن إطار المشروعية، وينبغي لتطبيق أحكامها إخلال أحد أطراف العقد بتنفيذ التزاماته، أو صدور خطأ من المورد ما يلحق ضررًا غير مشروع بالمستهلك، حيث يتم إلزام المورد بالتعويض الذي تقدره المحكمة، وتشمل المسؤولية التقصيرية في التعاملات الألكترونية, المسؤولية عن الفعل الشخصي، والتي يقصد بها مسؤولية الشخص عن كل فعل ارتكبه هو وأحدث ضررًا بالغير، وتشمل أيضًا المسؤولية عن فعل الغير، التي يقصد بها كل فعل يصدر من قبل الغير الذي يكون تابعًا للمورد كأن يكون عاملًا لديه أو أحد موظفيه، ويحدث هذا الفعل ضرراً حيث إن معظم المعلومات الموجودة عبر شبكة الإنترنت تمر بمجموعة من المراحل، المرحلة الابتدائية تشمل الإنتاج، أما المرحلة النهائية فتقتصر على الاستخدام، إضافة إلى هذا أن هناك مجموعة من الأشخاص يتعاملون بهذه المراحل ومدى وجود تبعية فيما بينهم، وهذا ما دفعنا إلى دراسة مسؤولية هؤلاء الأشخاص، إذ سنسلط الضوء على هذه الجزئية من خلال بحثنا هذا.Responsibility, as one of the sources of commitment, came to determine the responsibility of anyone who violates the rules of legality, which causes harm to others, and thus it is the guarantee to keep the actions of suppliers and traders within the framework of legality, and its provisions should be applied during the breach of one of the parties to the contract with the implementation of his obligations, or the occurrence of an error from the supplier that causes unreasonable harm. A project by the consumer, where the supplier is obliged to pay compensation estimated by the court, and tort liability in electronic transactions includes responsibility for the personal act, Which means the responsibility of a person for every act he committed and caused harm to others, and also includes liability for the act of others, which means every act issued by a third party who is affiliated with the supplier, such as being a worker for him or one of his employees, and this act causes harm as most of the information The Internet goes through a set of stages, the primary stage includes production, and the final stage is limited to use, in addition to this, there is a group of people who deal with these stages and the extent to which there is a dependency among them, and this is what prompted us to study the responsibility of these people, which we will highlight On this part through our research this

The Effectiveness of Online Course Intervention to Improve Knowledge of Antimicrobial Resistance among Dental Students, in Comparison to Reference Group Using a Randomized Controlled Trial

AIM: This study aimed to assess the effectiveness of a recognised antimicrobial resistance (AMR) online module on knowledge and perception among dental students, using a randomised controlled trial study design. METHODS: Dental students (n = 64, aged 21-25 years) in clinical years agreed to participate in this triple-blinded, parallel, randomised controlled trial. There were 34 students in the study group and 30 students in the control group. The study group participated in an online course covering information about AMR, while students in the control group received another online course about microorganisms in dentistry. Both groups were assessed three times using online questionnaires: before the intervention (T1), after the intervention (T2), and two months later (T3). Each one of T1, T2 and T3 had 22 questions. The questions were repeated each time in T1, T2, and T3 asking about AMR but with different question format, to avoid the possibility of students to memorise the answers. RESULTS: The mean (m) of correct answers for all students on T1 was 12.56, with standard deviation (SD) of 3.2. On T2, m = 14.03 and SD = 3.85, and on T3, m = 14.36 and SD = 3.71. Scores ranged from 0 to 22. The participants in the study and control groups showed significant score improvements from T1 to T2, immediately after the intervention, but there was no significant difference between T2 and T3. The study group students’ scores did not improve significantly from T1 to T3, in contrast to the control group students’ scores. More importantly, there was no significant difference in improvement from T1 to T2 when comparing the study and control groups. CONCLUSION: Online courses might not be reliable learning methods for ensuring the optimal levels of AMR knowledge that are needed by dental practitioners

Conceptual Metaphor of Window and Frame in The Poem Collection: “What the Barefoot Heart Sees in the Time of Shoes”

نهضت الاستعارة الإدراكية أو المعرفية كما جاءت عند لايكوف على نموذج معرفي يتبلور حول قدرة اللغة وكفاءتها على التوسع الانزياحي، والمجازي، والاستعاري، التي يمكن أن تمثل أقانيم وقوالب تحتوي أطرا مفاهيمية تختزل الواقع في اللغة، فالعالم الواقعي استعارة كبيرة وكل شيء يتداول فيه يعدّ لغة استعارية من حيث تحديد الجهات المكانية، والزمانية، والتعبيرات الحياتية، ومصطلحاها التداولية، فاللغة المرآة المعرفية العاكسة لمتطلبات الإدراك والفهم، فالشاعر عياش يحياوي في (ديوانه ما يراه القلب الحافي في زمن الأحذية)، اختزل الواقع في تشكيل استعاري معرفي يكشف عن النافذة والإطار، فاستعارة النافذة هي في الحقيقة مجالات معرفية متنوعة: البيت، والوطن، والفقر، والصعلكة، والمراقبة، والسلطة، والسرقة، وحدود الوطن وجغرافيته، واستعارة الإطار هي الحافة، والهجرة، والابتعاد، والناصية، والشرفة، والغياب، والتهميش، والقيد، والاحتواء، والحصار وكلها معان تختزل الواقع وتكشف عن ماهية التعرف على الواقع الجزائري، والاحتراق النفسي أو الداخلي والغربة والاغتراب، وما تعانيه الذات المنكسرة والمتشظية بين طموحات بناء المجتمع وتقدمه ومحاربة كل أنواع الظلم الاجتماعي والسياسي، والبحث عن الحرية والعدالة الاجتماعية, التي اختزلت في حكاية شحاذ أو متسول تمثّل وطنه في حدود، أو أطر ضيقة بمساحة الكرتون .Perceptual or epistemological metaphor, as it came to Lakoff, arose on a cognitive model that crystallized around the ability and competence of language to expand shifting, metaphor, and metaphor, which could represent persons and templates that contain conceptual frameworks that summarize reality in language, so the real world is a great metaphor and everything circulating in it is considered a language An allegorical one in terms of defining the spatial and temporal entities, and life expressions, and their deliberative terminology. Language is the epistemological mirror reflecting the requirements of perception and understanding. The poet Ayyash lives in (his poetry what the barefoot heart sees in the time of shoes) reduced reality to an allegorical cognitive formation that reveals the window and frame, so the metaphor of the window They are, in fact, various fields of knowledge: the home, the homeland, poverty, the traumatization, the surveillance, the power, the theft, the borders and the geography of the homeland, the frame metaphor is the edge, the migration, the distance, the forelock, the balcony, the absence, the marginalization, the restriction, the containment, the siege, all of which are meanings that are reduced Reality and it reveals what is known as the Algerian reality, psychological or internal combustion, estrangement and alienation, and what the broken and fragmented self-suffers from among aspirations Building and advancing society, combating all kinds of social and political injustice, and the search for freedom and social justice that were reduced to the story of a beggar or beggar whose homeland is represented within borders, or a narrow frame the size of a carton

Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks

This study presents a prediction model for comparing the performance of six different photovoltaic (PV) modules using artificial neural networks (ANNs), with simple inputs for the model. Cell temperature (Tc), irradiance, fill factor (FF), short circuit current (Isc), open-circuit voltage (Voc), maximum power (Pm), and the product of Voc and Isc are the inputs of the neural networks’ processes. A Prova 1011 solar system analyzer was used to extract the datasets of IV curves for six different PV modules under test conditions. As for the result, the highest FF was the mono-crystalline with an average of 0.737, while the lowest was the CIGS module with an average of 0.66. As for efficiency, the most efficient was the mono-crystalline module with an average of 10.32%, while the least was the thin-film module with an average of 7.65%. It is noted that the thin-film and flexible mono-modules have similar performances. The results from the proposed model give a clear idea about the best and worst performances of the PV modules under test conditions. Comparing the prediction process with the real dataset for the PV modules, the prediction accuracy for the model has a mean absolute percentage error (MAPE) of 0.874%, with an average root mean square error (RMSE) and mean absolute deviation (MAD) of, respectively, 0.0638 A and 0.237 A. The accuracy of the proposed model proved its efficiency for predicting the performance of the six PV modules