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

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

Automatic query expansion: A structural linguistic perspective

A user’s query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques ignore information about the dependencies that exist between words in natural language. However, more recent approaches have demonstrated that by explicitly modeling associations between terms significant improvements in retrieval effectiveness can be achieved over those that ignore these dependencies. State-of-the-art dependency-based approaches have been shown to primarily model syntagmatic associations. Syntagmatic associations infer a likelihood that two terms co-occur more often than by chance. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process will improve retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine

Scalable Text Mining with Sparse Generative Models

The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods. This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines. The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places

Small sugar farmer agency in the tropics 1872-1914 and the anomalous Herbert River Farmers' Association

One hundred and thirty-six years ago six immigrant small selectors formed the Herbert River Farmers’ Association (HRFA). On the Herbert a plantation mode of sugar production began in 1872. The selectors there, used the HRFA to actively participate in the transition of the tropical Australian sugar industry from plantation to small, family farms by 1914. Associations such as theirs formed the cornerstone of the institutional foundations of a globally unique and successful industry farmed by small, family farmers. Principal exponents of sugar industry organization history have consistently dismissed the small sugar cane farmers’ associations. Broader sugar industry scholarship however, identified them as having contributed to the demise of plantation production and the development of farm-based central milling. This assessment recognized that the HRFA and fellow small associations promoted small farming and that their members proved that white, small sugar farmers could farm in a tropical environment without detriment to their health and could provide a reliable supply of high-quality cane. Agricultural associations in sugar growing regions in the period 1872 to 1914 were dominated by white elite planters, practising an exploitative mode of production that used unfree or indentured coloured labour. Furthermore, land was not distributed equally to planters and small farmers alike, denying the small farmers, white or otherwise, the type of independence that came to characterise Australian white, small, sugar farmers. Land ownership and the freedom to form associations allowed the small selectors of the Herbert River Valley in tropical north Queensland in the late nineteenth century to negotiate with the planters in a way that the tenant farmers and share-croppers in other sugar growing regions could not. Accounts of the origins and nature of the sugar industry agricultural association movement focus exclusively on the planter associations while small sugar farmer associations are virtually invisible in the scholarship. Agricultural associations were vehicles both planters and farmers used to access rural extension, promote agricultural skills and innovation, and lobby with one voice. A top-down approach has made for a void in the understanding and appreciation of the development and role of small sugar industry agricultural associations in Australia. The Australian small sugar farmers’ association was unique in the global sugar industry association movement and the HRFA was the first of its kind in the plantation era in tropical Australia

Newsletter no. 45

INHIGEO produces an annual publication that includes information on the commission's activities, national reports, book reviews, interviews and occasional historical articles.N

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

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

No abstract available