Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning

Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable/proven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7-22) was associated with high empiric liposomal amphotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable

تعلم اللغة العربية بمساعدة الحاسب عن طريق الكلمات المتشابهة = Computer assisted language learning for Arabic using cognates

يتحدث الناس حول العالم بالعديد من اللغات المختلفة والتي تمتد بعض منها إلى مئات أو الآف السنين، ونشأت هذه اللغات بطرق مختلفة وانحدر بعض منها من لغات أخرى، ومع مرور الوقت توسع استخدام بعض هذه اللغات لتصبح لغات عالمية بينما تعرضت لغات أخرى إلى الاندثار. كما أن العديد من اللغات قد تعرضت للتحوير الذي يطرأ بشكل طبيعي على أي لغة مع مرور الوقت وبنفس الوقت تمت ولادة لغات جديدة من أعقاب لغات أخرى ولا شك ان هناك تشابهًا كبيرًا بين هذه اللغات الطبيعية، وتختلف نسبة التشابه ليشكل في بعض الأحيان ما يزيد عن نصف كلمات بعض اللغات والتي تعتبر مستلة من لغات أخرى. توسع استخدامات الحاسب الآلي في السنوات الماضية بشكل كبير حتى شملت استخداماته تعلم وتعليم اللغات الحية، ومن هذه اللغات حظيت اللغة الانجليزية بجانب كبير من هذا الاهتمام بينما لم تحظ اللغة العربية إلا على جانب قليل منه، وقد طورت في السنوات القليلة الماضية العديد من البرنامج والتطبيقات المستخدمة لتعليم اللغات ومنها اللغة العربية عن طريق الدروس والأمثلة والمسابقات والأسئلة والأجوبة والألعاب الحيوية التعليمية، بينما لا يوجد أي برنامج يقوم باكتشاف الكلمات المتشابهة في اللفظ والمعنى بين اللغة العربية واللغات الأخرىبشكل تلقائي ويستفيد من ذلك في تعليم اللغة، والتي تساعد هذه العملية على تسريع التعلم بشكل سهل وفعال وممتع، حيث ان المستخدم سيسهل عليه تذكر الكلمات ذات اللفظ المتشابه مع لغته الأم. يهدف هذا البحث إلى استخدام الحاسب وتقنية المعلومات لتسريع تعلم وتعليم اللغة العربية للمتحدثين بغيرها (اللغة الانجليزية أنموذجا في هذا البحث) وذلك عن طريق التركيز على الكلمات المتشابهة بين اللغة العربية واللغة الانجليزية، سيتمكن المتعلم عن طريق البرنامج من إنشاء وإضافة الدروس وسيتم استخراج الكلمات المتشابهة آلياً عن طريق الحاسب، سيتم استخدام المنهج التحليلي لدراسة وتحليل الطرق والخوارزميات المستخدمة لإيجاد التشابه بين اللغات المختلفة ومن ثم تطوير نظام لإيجاد التشابه بين اللغة العربية والانجليزية. وتعد هذه الأنظمة إحدى طرائق معالجة اللغات الطبيعية. ***** People around the world speak many different languages, some of which extend back to hundreds or thousands of years, and these languages arose in different ways and some of them descended from other languages. Many languages have undergone modification that occurs naturally in any language with time. At the same time, new languages were born from other languages, and for sure there are large similarities between these languages, and the similarity rate varies. Therefore, it sometimes can be more than half of the language’s words, which are considered derived from other languages. The use of computers in the past years has expanded greatly to be used in various aspects of human life including learning and teaching living languages. Among these languages, English language received a large part of this attention, while the Arabic language received only a small part of it. In the past few years, many programs and applications used to teach Languages, including the Arabic language, through lessons, examples, competitions, questions and answers in addition to educational games. While there is no program that detects words that are similar in pronunciation and meaning between Arabic and other languages, this process helps to accelerate learning in an easy, effective and enjoyable way. Thus, the learner will find it easier for him to remember words with pronunciation similar to his mother tongue. This research aims to use the computer and information technology to accelerate the learning and teaching of the Arabic language to non-native speakers (the English language is a model in this research) by focusing on the similar words (cognates) between Arabic and English, through the program, the learner will be able to create and add lessons, then the cognates will be extracted automatically by computer. The analytical approach will be used to study the methods and algorithms used to find similarities between different languages, and then developing a new algorithm to find similarities between Arabic and English languages, and this method is considered one of the Natural Language Processing techniques

Natural language processing for similar languages, varieties, and dialects: A survey

There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.Non peer reviewe

Histories of Australian Rock Art Research

Australia has one of the largest inventories of rock art in the world with pictographs and petroglyphs found almost anywhere that has suitable rock surfaces – in rock shelters and caves, on boulders and rock platforms. First Nations people have been marking these places with figurative imagery, abstract designs, stencils and prints for tens of thousands of years, often engaging with earlier rock markings. The art reflects and expresses changing experiences within landscapes over time, spirituality, history, law and lore, as well as relationships between individuals and groups of people, plants, animals, land and Ancestral Beings that are said to have created the world, including some rock art. Since the late 1700s, people arriving in Australia have been fascinated with the rock art they encountered, with detailed studies commencing in the late 1800s. Through the 1900s an impressive body of research on Australian rock art was undertaken, with dedicated academic study using archaeological methods employed since the late 1940s. Since then, Australian rock art has been researched from various perspectives, including that of Traditional Owners, custodians and other community members. Through the 1900s, there was also growing interest in Australian rock art from researchers across the globe, leading many to visit or migrate to Australia to undertake rock art research. In this volume, the varied histories of Australian rock art research from different parts of the country are explored not only in terms of key researchers, developments and changes over time, but also the crucial role of First Nations people themselves in investigations of this key component of their living heritage

Sequential sentence classification in research papers using cross-domain multi-task learning

The automatic semantic structuring of scientific text allows for more efficient reading of research articles and is an important indexing step for academic search engines. Sequential sentence classification is an essential structuring task and targets the categorisation of sentences based on their content and context. However, the potential of transfer learning for sentence classification across different scientific domains and text types, such as full papers and abstracts, has not yet been explored in prior work. In this paper, we present a systematic analysis of transfer learning for scientific sequential sentence classification. For this purpose, we derive seven research questions and present several contributions to address them: (1) We suggest a novel uniform deep learning architecture and multi-task learning for cross-domain sequential sentence classification in scientific text. (2) We tailor two transfer learning methods to deal with the given task, namely sequential transfer learning and multi-task learning. (3) We compare the results of the two best models using qualitative examples in a case study. (4) We provide an approach for the semi-automatic identification of semantically related classes across annotation schemes and analyse the results for four annotation schemes. The clusters and underlying semantic vectors are validated using k-means clustering. (5) Our comprehensive experimental results indicate that when using the proposed multi-task learning architecture, models trained on datasets from different scientific domains benefit from one another. Our approach significantly outperforms state of the art on full paper datasets while being on par for datasets consisting of abstracts

The Nature of attachment:An Australian experience

Throughout the world, protected area management regimes typically separate cultural and natural heritage in legislation, policy, administrative structures, disciplinary expertise, and on-ground practice. Within settler colonial nations, including Australia, cultural heritage is itself habitually separated into indigenous heritage and 'historic' (or non-indigenous) heritage. A consequence of these multiple binaries and disconnected regimes is that they work across rather than with one another. In this chapter, I use the frame of place-attachment to consider issues arising from the separation of natural and cultural heritage in the management of protected areas. The case examples are homestead gardens within protected areas, and my concern is for the recognition of Anglo-Australian place-attachment to domestic gardens.</p

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available