Here, we introduce and compare two new approaches to automatically detect whether the title or abstract of a PubMed publication contains HPI data, and extract the information about organisms and proteins involved in the interaction. The first approach is a feature-based supervised learning method using support vector machines (SVMs). The SVM models are trained on the features derived from the individual sentences. These features include names of the host/pathogen organisms and corresponding proteins or genes, keywords describing HPI-specific information, more general protein-protein interaction information, experimental methods and other statistical information. The language-based method employed a link grammar parser combined with semantic patterns derived from the training examples. The approaches have been trained and tested on manually curated HPI data. When compared to a naïve approach based on the existing protein-protein interaction literature mining method, our approaches demonstrated higher accuracy and recall in the classification task. The most accurate, feature-based, approach achieved 66-73% accuracy, depending on the test protocol.

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Background Human activity and the interaction between health conditions and activity is a critical part of understanding the overall function of individuals. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) models function as all aspects of an individual’s interaction with the world, including organismal concepts such as individual body structures, functions, and pathologies, as well as the outcomes of the individual’s interaction with their environment, referred to as activity and participation. Function, particularly activity and participation outcomes, is an important indicator of health at both the level of an individual and the population level, as it is highly correlated with quality of life and a critical component of identifying resource needs. Since it reflects the cumulative impact of health conditions on individuals and is not disease specific, its use as a health indicator helps to address major barriers to holistic, patient-centered care that result from multiple, and often competing, disease specific interventions. While the need for better information on function has been widely endorsed, this has not translated into its routine incorporation into modern health systems. Purpose We present the importance of capturing information on activity as a core component of modern health systems and identify specific steps and analytic methods that can be used to make it more available to utilize in improving patient care. We identify challenges in the use of activity and participation information, such as a lack of consistent documentation and diversity of data specificity and representation across providers, health systems, and national surveys. We describe how activity and participation information can be more effectively captured, and how health informatics methodologies, including natural language processing (NLP), can enable automatically locating, extracting, and organizing this information on a large scale, supporting standardization and utilization with minimal additional provider burden. We examine the analytic requirements and potential challenges of capturing this information with informatics, and describe how data-driven techniques can combine with common standards and documentation practices to make activity and participation information standardized and accessible for improving patient care. Recommendations We recommend four specific actions to improve the capture and analysis of activity and participation information throughout the continuum of care: (1) make activity and participation annotation standards and datasets available to the broader research community; (2) define common research problems in automatically processing activity and participation information; (3) develop robust, machine-readable ontologies for function that describe the components of activity and participation information and their relationships; and (4) establish standards for how and when to document activity and participation status during clinical encounters. We further provide specific short-term goals to make significant progress in each of these areas within a reasonable time frame.

This is a repository copy of Inductive identification of functional status information and establishing a gold standard corpus: a case study on the mobility domain.

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