Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

MeSH indexing based on automatically generated summaries

BACKGROUND: MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. RESULTS: We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. CONCLUSIONS: Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading

Foreword

The aim of this Workshop is to focus on building and evaluating resources used to facilitate biomedical text mining, including their design, update, delivery, quality assessment, evaluation and dissemination. Key resources of interest are lexical and knowledge repositories (controlled vocabularies, terminologies, thesauri, ontologies) and annotated corpora, including both task-specific resources and repositories reengineered from biomedical or general language resources. Of particular interest is the process of building annotated resources, including designing guidelines and annotation schemas (aiming at both syntactic and semantic interoperability) and relying on language engineering standards. Challenging aspects are updates and evolution management of resources, as well as their documentation, dissemination and evaluation

Enhancing navigation in biomedical databases by community voting and database-driven text classification

<p>Abstract</p> <p>Background</p> <p>The breadth of biological databases and their information content continues to increase exponentially. Unfortunately, our ability to query such sources is still often suboptimal. Here, we introduce and apply community voting, database-driven text classification, and visual aids as a means to incorporate distributed expert knowledge, to automatically classify database entries and to efficiently retrieve them.</p> <p>Results</p> <p>Using a previously developed peptide database as an example, we compared several machine learning algorithms in their ability to classify abstracts of published literature results into categories relevant to peptide research, such as related or not related to cancer, angiogenesis, molecular imaging, etc. Ensembles of bagged decision trees met the requirements of our application best. No other algorithm consistently performed better in comparative testing. Moreover, we show that the algorithm produces meaningful class probability estimates, which can be used to visualize the confidence of automatic classification during the retrieval process. To allow viewing long lists of search results enriched by automatic classifications, we added a dynamic heat map to the web interface. We take advantage of community knowledge by enabling users to cast votes in Web 2.0 style in order to correct automated classification errors, which triggers reclassification of all entries. We used a novel framework in which the database "drives" the entire vote aggregation and reclassification process to increase speed while conserving computational resources and keeping the method scalable. In our experiments, we simulate community voting by adding various levels of noise to nearly perfectly labelled instances, and show that, under such conditions, classification can be improved significantly.</p> <p>Conclusion</p> <p>Using PepBank as a model database, we show how to build a classification-aided retrieval system that gathers training data from the community, is completely controlled by the database, scales well with concurrent change events, and can be adapted to add text classification capability to other biomedical databases.</p> <p>The system can be accessed at <url>http://pepbank.mgh.harvard.edu</url>.</p

Deep Neural Networks for Multi-Label Text Classification: Application to Coding Electronic Medical Records

Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders. The main difficulty with developing automated EMR coding methods is the nature of the label space. The standardized vocabularies used for medical coding contain over 10 thousand codes. The label space is large, and the label distribution is extremely unbalanced - most codes occur very infrequently, with a few codes occurring several orders of magnitude more than others. A few codes never occur in training dataset at all. In this work, we present three methods to handle the large unbalanced label space. First, we study how to augment EMR training data with biomedical data (research articles indexed on PubMed) to improve the performance of standard neural networks for text classification. PubMed indexes more than 23 million citations. Many of the indexed articles contain relevant information about diagnosis and procedure codes. Therefore, we present a novel method of incorporating this unstructured data in PubMed using transfer learning. Second, we combine ideas from metric learning with recent advances in neural networks to form a novel neural architecture that better handles infrequent codes. And third, we present new methods to predict codes that have never appeared in the training dataset. Overall, our contributions constitute advances in neural multi-label text classification with potential consequences for improving EMR coding

Extracting biomedical relations from biomedical literature

Tese de mestrado em Bioinformática e Biologia Computacional, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018A ciência, e em especial o ramo biomédico, testemunham hoje um crescimento de conhecimento a uma taxa que clínicos, cientistas e investigadores têm dificuldade em acompanhar. Factos científicos espalhados por diferentes tipos de publicações, a riqueza de menções etiológicas, mecanismos moleculares, pontos anatómicos e outras terminologias biomédicas que não se encontram uniformes ao longo das várias publicações, para além de outros constrangimentos, encorajaram a aplicação de métodos de text mining ao processo de revisão sistemática. Este trabalho pretende testar o impacto positivo que as ferramentas de text mining juntamente com vocabulários controlados (enquanto forma de organização de conhecimento, para auxílio num posterior momento de recolha de informação) têm no processo de revisão sistemática, através de um sistema capaz de criar um modelo de classificação cujo treino é baseado num vocabulário controlado (MeSH), que pode ser aplicado a uma panóplia de literatura biomédica. Para esse propósito, este projeto divide-se em duas tarefas distintas: a criação de um sistema, constituído por uma ferramenta que pesquisa a base de dados PubMed por artigos científicos e os grava de acordo com etiquetas pré-definidas, e outra ferramenta que classifica um conjunto de artigos; e a análise dos resultados obtidos pelo sistema criado, quando aplicado a dois casos práticos diferentes. O sistema foi avaliado através de uma série de testes, com recurso a datasets cuja classificação era conhecida, permitindo a confirmação dos resultados obtidos. Posteriormente, o sistema foi testado com recurso a dois datasets independentes, manualmente curados por investigadores cuja área de investigação se relaciona com os dados. Esta forma de avaliação atingiu, por exemplo, resultados de precisão cujos valores oscilam entre os 68% e os 81%. Os resultados obtidos dão ênfase ao uso das tecnologias e ferramentas de text mining em conjunto com vocabulários controlados, como é o caso do MeSH, como forma de criação de pesquisas mais complexas e dinâmicas que permitam melhorar os resultados de problemas de classificação, como são aqueles que este trabalho retrata.Science, and the biomedical field especially, is witnessing a growth in knowledge at a rate at which clinicians and researchers struggle to keep up with. Scientific evidence spread across multiple types of scientific publications, the richness of mentions of etiology, molecular mechanisms, anatomical sites, as well as other biomedical terminology that is not uniform across different writings, among other constraints, have encouraged the application of text mining methods in the systematic reviewing process. This work aims to test the positive impact that text mining tools together with controlled vocabularies (as a way of organizing knowledge to aid, at a later time, to collect information) have on the systematic reviewing process, through a system capable of creating a classification model which training is based on a controlled vocabulary (MeSH) that can be applied to a variety of biomedical literature. For that purpose, this project was divided into two distinct tasks: the creation a system, consisting of a tool that searches the PubMed search engine for scientific articles and saves them according to pre-defined labels, and another tool that classifies a set of articles; and the analysis of the results obtained by the created system when applied to two different practical cases. The system was evaluated through a series of tests, using datasets whose classification results were previously known, allowing the confirmation of the obtained results. Afterwards, the system was tested by using two independently-created datasets which were manually curated by researchers working in the field of study. This last form of evaluation achieved, for example, precision scores as low as 68%, and as high as 81%. The results obtained emphasize the use of text mining tools, along with controlled vocabularies, such as MeSH, as a way to create more complex and comprehensive queries to improve the performance scores of classification problems, with which the theme of this work relates

Performance analysis of text classification algorithms for PubMed articles

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary developed by the US National Library of Medicine (NLM) for indexing articles in Pubmed Central (PMC) archive. The annotation process is a complex and time-consuming task relying on subjective manual assignment of MeSH concepts. Automating such tasks with machine learning may provide a more efficient way of organizing biomedical literature in a less ambiguous way. This research provides a case study which compares the performance of several different machine learning algorithms (Topic Modelling, Random Forest, Logistic Regression, Support Vector Classifiers, Multinomial Naive Bayes, Convolutional Neural Network and Long Short-Term Memory (LSTM)) in reproducing manually assigned MeSH annotations. Records for this study were retrieved from Pubmed using the E-utilities API to the Entrez system of databases at NCBI (National Centre for Biotechnology Information). The MeSH vocabulary is organised in a hierarchical structure and article abstracts labelled with a single MeSH term from the top second two layers were selected for training the machine learning models. Various strategies for text multiclass classification were considered. One was a Chi-square test for feature selection which identified words relevant to each MeSH label. The second approach used Named Entity Recognition (NER) to extract entities from the unstructured text and another approach relied on word embeddings able to capture latent knowledge from literature. At the start of the study text was tokenised using the Term Frequency Inverse Document Frequency (Tf-idf) technique and topic modelling performed with the objective to ascertain the correlation between assigned topics (unsupervised learning task) and MeSH terms in PubMed. Findings revealed the degree of coupling was low although significant. Of all of the classifier models trained, logistic regression on Tf-idf vectorised entities achieved highest accuracy. Performance varied across the different MeSH categories. In conclusion automated curation of articles by abstract may be possible for those target classes classified reliably and reproducibly

Computer Vision Problems in 3D Plant Phenotyping

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis. First, we present a novel system for automated and non-invasive/non-contact plant growth measurement. We exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement. We have set up a gantry robot system having 7 degrees of freedom hanging from the roof of a growth chamber. The payload is a range scanner, which can measure dense depth maps (raw 3D coordinate points in mm) on the surface of an object (the plant). The scanner can be moved around the plant to scan from different viewpoints by programming the robot with a specific trajectory. The sequence of overlapping images can be aligned to obtain a full 3D structure of the plant in raw point cloud format, which can be triangulated to obtain a smooth surface (triangular mesh), enclosing the original plant. We show the capability of the system to capture the well known diurnal pattern of plant growth computed from the surface area and volume of the plant meshes for a number of plant species. Second, we propose a technique to detect branch junctions in plant point cloud data. We demonstrate that using these junctions as feature points, the correspondence estimation can be formulated as a subgraph matching problem, and better matching results than state-of-the-art can be achieved. Also, this idea removes the requirement of a priori knowledge about rotational angles between adjacent scanning viewpoints imposed by the original registration algorithm for complex plant data. Before, this angle information had to be approximately known. Third, we present an algorithm to classify partially occluded leaves by their contours. In general, partial contour matching is a NP-hard problem. We propose a suboptimal matching solution and show that our method outperforms state-of-the-art on 3 public leaf datasets. We anticipate using this algorithm to track growing segmented leaves in our plant range data, even when a leaf becomes partially occluded by other plant matter over time. Finally, we perform some experiments to demonstrate the capability and limitations of the system and highlight the future research directions for Computer Vision based plant phenotyping

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org