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Biomedical Informatics Insights

Yuan Luo, Peter Szolovits
In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies...
2016: Biomedical Informatics Insights
Vinod C Kaggal, Ravikumar Komandur Elayavilli, Saeed Mehrabi, Joshua J Pankratz, Sunghwan Sohn, Yanshan Wang, Dingcheng Li, Majid Mojarad Rastegar, Sean P Murphy, Jason L Ross, Rajeev Chaudhry, James D Buntrock, Hongfang Liu
The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery...
2016: Biomedical Informatics Insights
Manabu Torii, Sameer S Tilak, Son Doan, Daniel S Zisook, Jung-Wei Fan
In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research...
2016: Biomedical Informatics Insights
Muhammad Anshari, Mohammad Nabil Almunawar
Mobile technology enables health-care organizations to extend health-care services by providing a suitable environment to achieve mobile health (mHealth) goals, making some health-care services accessible anywhere and anytime. Introducing mHealth could change the business processes in delivering services to patients. mHealth could empower patients as it becomes necessary for them to become involved in the health-care processes related to them. This includes the ability for patients to manage their personal information and interact with health-care staff as well as among patients themselves...
2016: Biomedical Informatics Insights
Kevin Bretonnel Cohen, Benjamin Glass, Hansel M Greiner, Katherine Holland-Bouley, Shannon Standridge, Ravindra Arya, Robert Faist, Diego Morita, Francesco Mangano, Brian Connolly, Tracy Glauser, John Pestian
We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do...
2016: Biomedical Informatics Insights
Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao
Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs...
2016: Biomedical Informatics Insights
Adebowale I Ojo, Sunday O Popoola
Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. The population of study comprised 442 health information management personnel in five teaching hospitals that had implemented EHIMS in Nigeria. A self-developed questionnaire was used as an instrument for data collection. The findings revealed that there is a positive, close relationship between all the identified factors and EHIMS's success: technical factors (r = 0...
2015: Biomedical Informatics Insights
Pierre Zweigenbaum, Thomas Lavergne, Natalia Grabar, Thierry Hamon, Sophie Rosset, Cyril Grouin
Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system...
2013: Biomedical Informatics Insights
Christine Howes, Matthew Purver, Rose McCabe
Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power...
2013: Biomedical Informatics Insights
Mindy K Ross, Ko-Wei Lin, Karen Truong, Abhishek Kumar, Mike Conway
The database of Genotypes and Phenotypes (dbGaP) allows researchers to understand phenotypic contribution to genetic conditions, generate new hypotheses, confirm previous study results, and identify control populations. However, effective use of the database is hindered by suboptimal study retrieval. Our objective is to evaluate text classification techniques to improve study retrieval in the context of the dbGaP database. We utilized standard machine learning algorithms (naive Bayes, support vector machines, and the C4...
2013: Biomedical Informatics Insights
Rohit J Kate
Converting information contained in natural language clinical text into computer-amenable structured representations can automate many clinical applications. As a step towards that goal, we present a method which could help in converting novel clinical phrases into new expressions in SNOMED CT, a standard clinical terminology. Since expressions in SNOMED CT are written in terms of their relations with other SNOMED CT concepts, we formulate the important task of identifying relations between clinical phrases and SNOMED CT concepts...
2013: Biomedical Informatics Insights
Siddhartha Jonnalagadda, Trevor Cohen, Stephen Wu, Hongfang Liu, Graciela Gonzalez
Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small-annotated corpora might not have sufficient examples for learning to statistically extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and term clustering, all of which are considered distributional semantic features...
2013: Biomedical Informatics Insights
Sunghwan Sohn, Cheryl Clark, Scott R Halgrim, Sean P Murphy, Siddhartha R Jonnalagadda, Kavishwar B Wagholikar, Stephen T Wu, Christopher G Chute, Hongfang Liu
A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essential to understand the semantic patterns as well as the patterns of the context interspersed among them (ie, context patterns) to effectively extract comprehensive medication information...
2013: Biomedical Informatics Insights
Stephen Wu
No abstract text is available yet for this article.
2013: Biomedical Informatics Insights
John P Pestian
No abstract text is available yet for this article.
2013: Biomedical Informatics Insights
Sylvia Halász, Philip Brown, Cem Oktay, Arif Alper Cevik, Isa Kılıçaslan, Colin Goodall, Dennis G Cochrane, Thomas R Fowler, Guy Jacobson, Simon Tse, John R Allegra
INTRODUCTION: Syndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. The n-gram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD codes. OBJECTIVES: The objective was to create a Turkish n-gram CC classifier for the respiratory syndrome and then compare daily volumes between the n-gram CC classifier and a respiratory ICD-10 code grouping on a test set of data...
2013: Biomedical Informatics Insights
Tudor Groza, Hamed Hassanzadeh, Jane Hunter
Today's search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications...
2013: Biomedical Informatics Insights
Tudor Groza, Jane Hunter, Andreas Zankl
Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome...
2013: Biomedical Informatics Insights
John P Pestian, Pawel Matykiewicz, Michelle Linn-Gust, Brett South, Ozlem Uzuner, Jan Wiebe, K Bretonnel Cohen, John Hurdle, Christopher Brew
This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large set of labels. The number of participants was larger than in any previous biomedical challenge task...
January 30, 2012: Biomedical Informatics Insights
John P Pestian, Pawel Matykiewicz, Michelle Linn-Gust
This paper reports on the results of an initiative to create and annotate a corpus of suicide notes that can be used for machine learning. Ultimately, the corpus included 1,278 notes that were written by someone who died by suicide. Each note was reviewed by at least three annotators who mapped words or sentences to a schema of emotions. This corpus has already been used for extensive scientific research.
2012: Biomedical Informatics Insights
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