scispace - formally typeset
Search or ask a question
Author

Sarvnaz Karimi

Other affiliations: University of Melbourne, RMIT University, NICTA  ...read more
Bio: Sarvnaz Karimi is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Computer science & Transliteration. The author has an hindex of 22, co-authored 94 publications receiving 1842 citations. Previous affiliations of Sarvnaz Karimi include University of Melbourne & RMIT University.


Papers
More filters
01 Nov 2014
TL;DR: The topic of the 2014 ALTA shared task was to identify location information in tweets and Kaggle in Class was used as the framework for submission, evaluation and communication with the participants.
Abstract: This year was the fifth in the ALTA series of shared tasks. The topic of the 2014 ALTA shared task was to identify location information in tweets. As in past competitions, we used Kaggle in Class as the framework for submission, evaluation and communication with the participants. In this paper we describe the details of the shared task, evaluation method, and results of the participating systems.

7 citations

Journal ArticleDOI
TL;DR: A Show, Tell and Summarise model is proposed that first generates findings from chest X-rays and then summarises them to provide impression section, overcoming the limitation of previous research in radiology report generation.
Abstract: Radiology plays a vital role in health care by viewing the human body for diagnosis, monitoring, and treatment of medical problems. In radiology practice, radiologists routinely examine medical images such as chest X-rays and describe their findings in the form of radiology reports. However, this task of reading medical images and summarising its insights is time consuming, tedious, and error-prone, which often represents a bottleneck in the clinical diagnosis process. A computer-aided diagnosis system which can automatically generate radiology reports from medical images can be of great significance in reducing workload, reducing diagnostic errors, speeding up clinical workflow, and helping to alleviate any shortage of radiologists. Existing research in radiology report generation focuses on generating the concatenation of the findings and impression sections. Also, existing work ignores important differences between normal and abnormal radiology reports. The text of normal and abnormal reports differs in style and it is difficult for a single model to learn both the text style and learn to transition from findings to impression. To alleviate these challenges, we propose a Show, Tell and Summarise model that first generates findings from chest X-rays and then summarises them to provide impression section. The proposed work generates the findings and impression sections separately, overcoming the limitation of previous research. Also, we use separate models for generating normal and abnormal radiology reports which provide true insight of model’s performance. Experimental results on the publicly available IU-CXR dataset show the effectiveness of our proposed model. Finally, we highlight limitations in the radiology report generation research and present recommendations for future work.

7 citations

Proceedings ArticleDOI
01 Aug 2019
TL;DR: The authors compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification, and personal health mention classification, showing that context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology.
Abstract: Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations (word versus context) for three classification problems: influenza infection classification, drug usage classification and personal health mention classification. For statistical classifiers trained for each of these problems, context-based representations based on ELMo, Universal Sentence Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe and the two adapted using the MESH ontology. There is an improvement of 2-4% in the accuracy when these context-based representations are used instead of word-based representations.

7 citations

Proceedings Article
05 Dec 2008
TL;DR: A two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist retrieval of relevant articles, and to restrict results to higher-quality documents is demonstrated.
Abstract: Searching and selecting articles to be included in systematic reviews is a real challenge for healthcare agencies responsible for publishing these reviews. The current practice of manually reviewing all papers returned by complex hand-crafted boolean queries is human labour-intensive. We demonstrate a two-stage searching system that takes advantage of ranked queries and support-vector machine text classification to assist retrieval of relevant articles, and to restrict results to higher-quality documents. Our proposed approach shows significant work saved in the systematic review process over a baseline of a keyword-based retrieval system

7 citations

Journal ArticleDOI
TL;DR: The authors used transformers to extract disease mentions from clinical notes and used rule-based methods for extracting family member (FM) information from text and coreference resolution techniques to improve the annotation of diseases.
Abstract: Background: The prognosis, diagnosis, and treatment of many genetic disorders and familial diseases significantly improve if the family history (FH) of a patient is known. Such information is often written in the free text of clinical notes. Objective: The aim of this study is to develop automated methods that enable access to FH data through natural language processing. Methods: We performed information extraction by using transformers to extract disease mentions from notes. We also experimented with rule-based methods for extracting family member (FM) information from text and coreference resolution techniques. We evaluated different transfer learning strategies to improve the annotation of diseases. We provided a thorough error analysis of the contributing factors that affect such information extraction systems. Results: Our experiments showed that the combination of domain-adaptive pretraining and intermediate-task pretraining achieved an F1 score of 81.63% for the extraction of diseases and FMs from notes when it was tested on a public shared task data set from the National Natural Language Processing Clinical Challenges (N2C2), providing a statistically significant improvement over the baseline (P<.001). In comparison, in the 2019 N2C2/Open Health Natural Language Processing Shared Task, the median F1 score of all 17 participating teams was 76.59%. Conclusions: Our approach, which leverages a state-of-the-art named entity recognition model for disease mention detection coupled with a hybrid method for FM mention detection, achieved an effectiveness that was close to that of the top 3 systems participating in the 2019 N2C2 FH extraction challenge, with only the top system convincingly outperforming our approach in terms of precision.

6 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Proceedings ArticleDOI
23 Apr 2020
TL;DR: It is consistently found that multi-phase adaptive pretraining offers large gains in task performance, and it is shown that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable.
Abstract: Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task’s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.

1,532 citations

Journal ArticleDOI
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Abstract: Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

1,491 citations