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Article ; Online: Detection of Adverse Drug Reactions using Medical Named Entities on Twitter.

MacKinlay, Andrew / Aamer, Hafsah / Yepes, Antonio Jimeno

AMIA ... Annual Symposium proceedings. AMIA Symposium

2018 Volume 2017, Page(s) 1215–1224

Abstract: Adverse Drug Reactions (ADRs) are unintentional reactions caused by a drug or combination of drugs taken by a patient. The current ADR reporting systems inevitably have delays in reporting such events. The broad scope of social media conversations on ... ...

Abstract	Adverse Drug Reactions (ADRs) are unintentional reactions caused by a drug or combination of drugs taken by a patient. The current ADR reporting systems inevitably have delays in reporting such events. The broad scope of social media conversations on sites such as Twitter means that inevitably health-related topics will be covered. This means that these sites could then be used to detect potentially novel ADRs with less latency for subsequent further investigation. In this work, we investigate ADR surveillance using a large corpus of Twitter data, containing around 50 billion tweets spanning 3 years (2012-2014), and evaluate against over 3000 drugs reported in the FAERS database. This is both a larger corpus and broader selection of drugs than previous work in the domain. We compare the ADRs identified using our method to the FDA Adverse Event Reporting System (FAERS) database of ADRs reported using more traditional techniques, and find that Twitter is a useful resource for ADR detection up to 72% micro-averaged precision. Micro-averaged recall of 6% is achievable using only 10% of Twitter, indicating that with a higher-volume or targeted feed it would be possible to detect a large percentage of ADRs.
MeSH term(s)	Adverse Drug Reaction Reporting Systems ; Databases, Factual ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Product Surveillance, Postmarketing/methods ; Social Media ; United States ; United States Food and Drug Administration
Language	English
Publishing date	2018-04-16
Publishing country	United States
Document type	Comparative Study ; Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Construction of fetal inferior facial angle and hemi-mandible length reference ranges at 18-21 weeks' gestation in an Australian population.

Qiu, Robert / MacKinlay, Katie / Suen, Melissa / McLennan, Andrew

Australasian journal of ultrasound in medicine

2017 Volume 20, Issue 4, Page(s) 168–173

Abstract: Objective: The aim of this pilot study was to provide modern reference intervals for both inferior facial angle and hemi-mandible length in fetuses of 18-21 weeks' gestation.: Methods: Prospectively, 296 apparently normal fetuses were sonographically ...

Abstract	Objective: The aim of this pilot study was to provide modern reference intervals for both inferior facial angle and hemi-mandible length in fetuses of 18-21 weeks' gestation. Methods: Prospectively, 296 apparently normal fetuses were sonographically assessed at 18-21 weeks' gestation. Inferior facial angle and hemi-mandible length were measured and parametrically analysed with respect to gestational age. Regression models were derived for each parameter and compared with models of previous studies. Results: The mean inferior facial angle remained constant over the studied gestational age range at 63.9°, with 5th and 95th percentiles of 56.6° and 73.4°, respectively. Hemi-mandible length was found to be positively correlated with gestational age over the studied range, and the mean value is described by the equation 40.89 mm - (6327.495 × GA Conclusion: Modern reference intervals for inferior facial angle and hemi-mandible length were defined within this pilot study. These reference intervals will aid in improving accuracy diagnosing micrognathia and our ability to differentiate true micrognathia from retrognathia.
Language	English
Publishing date	2017-10-07
Publishing country	Australia
Document type	Journal Article
ZDB-ID	2843953-3
ISSN	2205-0140 ; 1836-6864
ISSN (online)	2205-0140
ISSN	1836-6864
DOI	10.1002/ajum.12066
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires

Dabrowski, Joel Janek / Pagendam, Daniel Edward / Hilton, James / Sanderson, Conrad / MacKinlay, Daniel / Huston, Carolyn / Bolt, Andrew / Kuhnert, Petra

2022

Abstract: We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level- ... ...

Abstract	We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level-set function. The result is a PINN that simulates a fire-front as it propagates through the spatio-temporal domain. We show that popular optimisation cost functions used in the literature can result in PINNs that fail to maintain temporal continuity in modelled fire-fronts when there are extreme changes in exogenous forcing variables such as wind direction. We thus propose novel additions to the optimisation cost function that improves temporal continuity under these extreme changes. Furthermore, we develop an approach to perform data assimilation within the PINN such that the PINN predictions are drawn towards observations of the fire-front. Finally, we incorporate our novel approaches into a Bayesian PINN (B-PINN) to provide uncertainty quantification in the fire-front predictions. This is significant as the standard solver, the level-set method, does not naturally offer the capability for data assimilation and uncertainty quantification. Our results show that, with our novel approaches, the B-PINN can produce accurate predictions with high quality uncertainty quantification on real-world data. Comment: Accepted for publication in Spatial Statistics
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2022-12-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Identifying Diseases, Drugs, and Symptoms in Twitter.

Jimeno-Yepes, Antonio / MacKinlay, Andrew / Han, Bo / Chen, Qiang

Studies in health technology and informatics

2015 Volume 216, Page(s) 643–647

Abstract: Social media sites, such as Twitter, are a rich source of many kinds of information, including health-related information. Accurate detection of entities such as diseases, drugs, and symptoms could be used for biosurveillance (e.g. monitoring of flu) and ...

Abstract	Social media sites, such as Twitter, are a rich source of many kinds of information, including health-related information. Accurate detection of entities such as diseases, drugs, and symptoms could be used for biosurveillance (e.g. monitoring of flu) and identification of adverse drug events. However, a critical assessment of performance of current text mining technology on Twitter has not been done yet in the medical domain. Here, we study the development of a Twitter data set annotated with relevant medical entities which we have publicly released. The manual annotation results show that it is possible to perform high-quality annotation despite of the complexity of medical terminology and the lack of context in a tweet. Furthermore, we have evaluated the capability of state-of-the-art approaches to reproduce the annotations in the data set. The best methods achieve F-scores of 55-66%. The data analysis and the preliminary results provide valuable insights on identifying medical entities in Twitter for various applications.
MeSH term(s)	Data Mining/methods ; Disease/classification ; Natural Language Processing ; Pharmaceutical Preparations/classification ; Population Surveillance/methods ; Social Media/classification ; Symptom Assessment/classification ; Terminology as Topic ; Vocabulary, Controlled
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2015
Publishing country	Netherlands
Document type	Journal Article
ISSN	0926-9630
ISSN	0926-9630
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.

Yepes, Antonio Jimeno / MacKinlay, Andrew / Gunn, Natalie / Schieber, Christine / Faux, Noel / Downton, Matthew / Goudey, Benjamin / Martin, Richard L

AMIA ... Annual Symposium proceedings. AMIA Symposium

2018 Volume 2018, Page(s) 616–623

Abstract: As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in ... ...

Abstract	As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available in the scientific literature, and is then later manually curated into more standardized genomic databases. This curation process is expensive, time-consuming and many variants do not end up being fully curated, if at all. Detecting mutations in the literature is the first key step towards automating this process. However, most of the current methods have focused on identifying mutations that follow existing nomenclatures. In this work, we show that there is a large number of mutations that are missed by using this standard approach. Furthermore, we implement the first mutation annotator to cover an extended mutation landscape, and we show that its F1 performance is the same performance as human annotation (F1 78.29 for manual annotation vs F1 79.56 for automatic annotation).
MeSH term(s)	DNA Mutational Analysis ; Data Mining/methods ; Databases, Genetic ; Deep Learning ; Humans ; Machine Learning ; Mutation
Language	English
Publishing date	2018-12-05
Publishing country	United States
Document type	Comparative Study ; Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Detecting modification of biomedical events using a deep parsing approach.

Mackinlay, Andrew / Martinez, David / Baldwin, Timothy

BMC medical informatics and decision making

2012 Volume 12 Suppl 1, Page(s) S4

Abstract: Background: This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not ... ...

Abstract	Background: This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. Method: To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. Results: Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. Conclusions: Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.
MeSH term(s)	Abstracting and Indexing ; Algorithms ; Biomedical Research ; Humans ; I-kappa B Proteins/analysis ; Information Storage and Retrieval/methods ; Linear Models ; Natural Language Processing ; Pattern Recognition, Automated ; Phosphorylation ; Principal Component Analysis ; Semantics
Chemical Substances	I-kappa B Proteins
Language	English
Publishing date	2012-04-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1472-6947
ISSN (online)	1472-6947
DOI	10.1186/1472-6947-12-S1-S4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Cross-hospital portability of information extraction of cancer staging information.

Martinez, David / Pitson, Graham / MacKinlay, Andrew / Cavedon, Lawrence

Artificial intelligence in medicine

2014 Volume 62, Issue 1, Page(s) 11–21

Abstract: Objective: We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems. Staging information is ... ...

Abstract	Objective: We address the task of extracting information from free-text pathology reports, focusing on staging information encoded by the TNM (tumour-node-metastases) and ACPS (Australian clinico-pathological stage) systems. Staging information is critical for diagnosing the extent of cancer in a patient and for planning individualised treatment. Extracting such information into more structured form saves time, improves reporting, and underpins the potential for automated decision support. Methods and material: We investigate the portability of a text mining model constructed from records from one health centre, by applying it directly to the extraction task over a set of records from a different health centre, with different reporting narrative characteristics. Other than a simple normalisation step on features associated with target labels, we apply the models from one system directly to the other. Results: The best F-scores for in-hospital experiments are 81%, 85%, and 94% (for staging T, N, and M respectively), while best cross-hospital F-scores reach 84%, 81%, and 91% for the same respective categories. Conclusions: Our performance results compare favourably to the best levels reported in the literature, and--most relevant to our aim here--the cross-corpus results demonstrate the portability of the models we developed.
MeSH term(s)	Algorithms ; Colorectal Neoplasms/pathology ; Data Mining ; Hospital Information Systems ; Humans ; Medical Records ; Natural Language Processing ; Neoplasm Staging
Language	English
Publishing date	2014-09
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2014.06.002
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Detection of protein catalytic sites in the biomedical literature.

Verspoor, Karin / Mackinlay, Andrew / Cohn, Judith D / Wall, Michael E

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

2013 , Page(s) 433–444

Abstract: This paper explores the application of text mining to the problem of detecting protein functional sites in the biomedical literature, and specifically considers the task of identifying catalytic sites in that literature. We provide strong evidence for ... ...

Abstract	This paper explores the application of text mining to the problem of detecting protein functional sites in the biomedical literature, and specifically considers the task of identifying catalytic sites in that literature. We provide strong evidence for the need for text mining techniques that address residue-level protein function annotation through an analysis of two corpora in terms of their coverage of curated data sources. We also explore the viability of building a text-based classifier for identifying protein functional sites, identifying the low coverage of curated data sources and the potential ambiguity of information about protein functional sites as challenges that must be addressed. Nevertheless we produce a simple classifier that achieves a reasonable ∼69% F-score on our full text silver corpus on the first attempt to address this classification task. The work has application in computational prediction of the functional significance of protein sites as well as in curation workflows for databases that capture this information.
MeSH term(s)	Amino Acids/chemistry ; Artificial Intelligence ; Binding Sites ; Catalytic Domain ; Computational Biology ; Data Mining/statistics & numerical data ; Databases, Protein/statistics & numerical data ; Ligands ; Natural Language Processing ; Proteins/chemistry ; Proteins/classification ; Proteins/metabolism
Chemical Substances	Amino Acids ; Ligands ; Proteins
Language	English
Publishing date	2013-02-19
Publishing country	United States
Document type	Journal Article
ISSN	2335-6936
ISSN (online)	2335-6936
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Optimizing graph-based patterns to extract biomedical events from the literature.

Liu, Haibin / Verspoor, Karin / Comeau, Donald C / MacKinlay, Andrew D / Wilbur, W

BMC bioinformatics

2015 Volume 16 Suppl 16, Page(s) S2

Abstract: IN BIONLP-ST 2013: We participated in the BioNLP 2013 shared tasks on event extraction. Our extraction method is based on the search for an approximate subgraph isomorphism between key context dependencies of events and graphs of input sentences. Our ... ...

Abstract	IN BIONLP-ST 2013: We participated in the BioNLP 2013 shared tasks on event extraction. Our extraction method is based on the search for an approximate subgraph isomorphism between key context dependencies of events and graphs of input sentences. Our system was able to address both the GENIA (GE) task focusing on 13 molecular biology related event types and the Cancer Genetics (CG) task targeting a challenging group of 40 cancer biology related event types with varying arguments concerning 18 kinds of biological entities. In addition to adapting our system to the two tasks, we also attempted to integrate semantics into the graph matching scheme using a distributional similarity model for more events, and evaluated the event extraction impact of using paths of all possible lengths as key context dependencies beyond using only the shortest paths in our system. We achieved a 46.38% F-score in the CG task (ranking 3rd) and a 48.93% F-score in the GE task (ranking 4th). AFTER BIONLP-ST 2013: We explored three ways to further extend our event extraction system in our previously published work: (1) We allow non-essential nodes to be skipped, and incorporated a node skipping penalty into the subgraph distance function of our approximate subgraph matching algorithm. (2) Instead of assigning a unified subgraph distance threshold to all patterns of an event type, we learned a customized threshold for each pattern. (3) We implemented the well-known Empirical Risk Minimization (ERM) principle to optimize the event pattern set by balancing prediction errors on training data against regularization. When evaluated on the official GE task test data, these extensions help to improve the extraction precision from 62% to 65%. However, the overall F-score stays equivalent to the previous performance due to a 1% drop in recall.
MeSH term(s)	Algorithms ; Databases as Topic ; Information Storage and Retrieval ; Natural Language Processing ; Publications ; Statistics as Topic
Language	English
Publishing date	2015
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/1471-2105-16-S16-S2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book: The econometrics of financial markets

Campbell, John Y / Lo, Andrew W / MacKinlay, Archie Craig

2012

Author's details	John Y. Campbell; Andrew W. Lo; A. Craig MacKinley
Language	English
Size	632 S.
Edition	Neuausg.
Publisher	Princeton Univ. Press
Publishing place	Princeton, NJ
Document type	Book
Database	Former special subject collection: coastal and deep sea fishing

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