Leveraging Electronic Health Care Record Information to Measure Pressure Ulcer Risk in Veterans With Spinal Cord Injury: A Longitudinal Study Protocol.

JMIR Res Protoc. 2017 Jan 19;6(1):e3

Authors: Luther SL, Thomason SS, Sabharwal S, Finch DK, McCart J, Toyinbo P, Bouayad L, Matheny ME, Gobbel GT, Powell-Cope G

Abstract
BACKGROUND: Pressure ulcers (PrUs) are a frequent, serious, and costly complication for veterans with spinal cord injury (SCI). The health care team should periodically identify PrU risk, although there is no tool in the literature that has been found to be reliable, valid, and sensitive enough to assess risk in this vulnerable population.
OBJECTIVE: The immediate goal is to develop a risk assessment model that validly estimates the probability of developing a PrU. The long-term goal is to assist veterans with SCI and their providers in preventing PrUs through an automated system of risk assessment integrated into the veteran's electronic health record (EHR).
METHODS: This 5-year longitudinal, retrospective, cohort study targets 12,344 veterans with SCI who were cared for in the Veterans Health Administration (VHA) in fiscal year (FY) 2009 and had no record of a PrU in the prior 12 months. Potential risk factors identified in the literature were reviewed by an expert panel that prioritized factors and determined if these were found in structured data or unstructured form in narrative clinical notes for FY 2009-2013. These data are from the VHA enterprise Corporate Data Warehouse that is derived from the EHR structured (ie, coded in database/table) or narrative (ie, text in clinical notes) data for FY 2009-2013.
RESULTS: This study is ongoing and final results are expected in 2017. Thus far, the expert panel reviewed the initial list of risk factors extracted from the literature; the panel recommended additions and omissions and provided insights about the format in which the documentation of the risk factors might exist in the EHR. This list was then iteratively refined through review and discussed with individual experts in the field. The cohort for the study was then identified, and all structured, unstructured, and semistructured data were extracted. Annotation schemas were developed, samples of documents were extracted, and annotations are ongoing. Operational definitions of structured data elements have been created and steps to create an analytic dataset are underway.
CONCLUSIONS: To our knowledge, this is the largest cohort employed to identify PrU risk factors in the United States. It also represents the first time natural language processing and statistical text mining will be used to expand the number of variables available for analysis. A major strength of this quantitative study is that all VHA SCI centers were included in the analysis, reducing potential for selection bias and providing increased power for complex statistical analyses. This longitudinal study will eventually result in a risk prediction tool to assess PrU risk that is reliable and valid, and that is sensitive to this vulnerable population.

PMID: 28104580 [PubMed - in process]

Synthesis of human parainfluenza virus 4 nucleocapsid-like particles in yeast and their use for detection of virus-specific antibodies in human serum.

Appl Microbiol Biotechnol. 2017 Jan 19;:

Authors: Bulavaitė A, Lasickienė R, Tamošiūnas PL, Simanavičius M, Sasnauskas K, Žvirblienė A

Abstract
The aim of this study was to produce human parainfluenza virus type 4 (HPIV4) nucleocapsid (N) protein in yeast Saccharomyces cerevisiae expression system, to explore its structural and antigenic properties and to evaluate its applicability in serology. The use of an optimized gene encoding HPIV4 N protein amino acid (aa) sequence GenBank AGU90031.1 allowed high yield of recombinant N protein forming nucleocapsid-like particles (NLPs) in yeast. A substitution L332D disrupted self-assembly of NLPs, confirming the role of this position in the N proteins of Paramyxovirinae. Three monoclonal antibodies (MAbs) were generated against the NLP-forming HPIV4 N protein. They recognised HPIV4-infected cells, demonstrating the antigenic similarity between the recombinant and virus-derived N proteins. HPIV4 N protein was used as a coating antigen in an indirect IgG ELISA with serum specimens of 154 patients with respiratory tract infection. The same serum specimens were tested with previously generated N protein of a closely related HPIV2, another representative of genus Rubulavirus. Competitive ELISA was developed using related yeast-produced viral antigens to deplete the cross-reactive serum antibodies. In the ELISA either without or with competition using heterologous HPIV (2 or 4) N or mumps virus N proteins, the seroprevalence of HPIV4 N-specific IgG was, respectively, 46.8, 39.6 and 40.3% and the seroprevalence of HPIV2 N-specific IgG-47.4, 39.0 and 37.7%. In conclusion, yeast-produced HPIV4 N protein shares structural and antigenic properties of the native virus nucleocapsids. Yeast-produced HPIV4 and HPIV2 NLPs are prospective tools in serology.

PMID: 28102432 [PubMed - as supplied by publisher]

Developing timely insights into comparative effectiveness research with a text-mining pipeline.

Drug Discov Today. 2016 Mar;21(3):473-80

Authors: Chang M, Chang M, Reed JZ, Milward D, Xu JJ, Cornell WD

Abstract
Comparative effectiveness research (CER) provides evidence for the relative effectiveness and risks of different treatment options and informs decisions made by healthcare providers, payers, and pharmaceutical companies. CER data come from retrospective analyses as well as prospective clinical trials. Here, we describe the development of a text-mining pipeline based on natural language processing (NLP) that extracts key information from three different trial data sources: NIH ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), and Citeline Trialtrove. The pipeline leverages tailored terminologies to produce an integrated and structured output, capturing any trials in which pharmaceutical products of interest are compared with another therapy. The timely information alerts generated by this system provide the earliest and most complete picture of emerging clinical research.

PMID: 26854423 [PubMed - indexed for MEDLINE]

Assessing occurrence of hypoglycemia and its severity from electronic health records of patients with type 2 diabetes mellitus.

Diabetes Res Clin Pract. 2016 Nov;121:192-203

Authors: Nunes AP, Yang J, Radican L, Engel SS, Kurtyka K, Tunceli K, Yu S, Iglay K, Doherty MC, Dore DD

Abstract
AIMS: Accurate measures of hypoglycemia within electronic health records (EHR) can facilitate clinical population management and research. We quantify the occurrence of serious and mild-to-moderate hypoglycemia in a large EHR database in the US, comparing estimates based only on structured data to those from structured data and natural language processing (NLP) of clinical notes.
METHODS: This cohort study included patients with type 2 diabetes identified from January 2009 through March 2014. We compared estimates of occurrence of hypoglycemia derived from diagnostic codes to those recorded within clinical notes and classified via NLP. Measures of hypoglycemia from only structured data (ICD-9 Algorithm), only note mentions (NLP Algorithm), and either structured data or notes (Combined Algorithm) were compared with estimates of the period prevalence, incidence rate, and event rate of hypoglycemia, overall and by seriousness.
RESULTS: Of the 844,683 eligible patients, 119,695 had at least one recorded hypoglycemic event identified with ICD-9 or NLP. The period prevalence of hypoglycemia was 12.4%, 25.1%, and 32.2% for the ICD-9 Algorithm, NLP Algorithm, and Combined Algorithm, respectively. There were 6128 apparent non-serious events utilizing the ICD-9 Algorithm, which increased to 152,987 non-serious events within the Combined Algorithm.
CONCLUSIONS: Ascertainment of events from clinical notes more than doubled the completeness of hypoglycemia capture overall relative to measures from structured data, and increased capture of non-serious events more than 20-fold. The structured data and clinical notes are complementary within the EHR, and both need to be considered in order to fully assess the occurrence of hypoglycemia.

PMID: 27744128 [PubMed - indexed for MEDLINE]

ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files.

J Cheminform. 2016;8:73

Authors: Karthikeyan M, Vyas R

Abstract
Digital access to chemical journals resulted in a vast array of molecular information that is now available in the supplementary material files in PDF format. However, extracting this molecular information, generally from a PDF document format is a daunting task. Here we present an approach to harvest 3D molecular data from the supporting information of scientific research articles that are normally available from publisher's resources. In order to demonstrate the feasibility of extracting truly computable molecules from PDF file formats in a fast and efficient manner, we have developed a Java based application, namely ChemEngine. This program recognizes textual patterns from the supplementary data and generates standard molecular structure data (bond matrix, atomic coordinates) that can be subjected to a multitude of computational processes automatically. The methodology has been demonstrated via several case studies on different formats of coordinates data stored in supplementary information files, wherein ChemEngine selectively harvested the atomic coordinates and interpreted them as molecules with high accuracy. The reusability of extracted molecular coordinate data was demonstrated by computing Single Point Energies that were in close agreement with the original computed data provided with the articles. It is envisaged that the methodology will enable large scale conversion of molecular information from supplementary files available in the PDF format into a collection of ready- to- compute molecular data to create an automated workflow for advanced computational processes. Software along with source codes and instructions available at https://sourceforge.net/projects/chemengine/files/?source=navbar.Graphical abstract.

PMID: 28090216 [PubMed - in process]

Development of medical treatment for eye injuries in the mainland of China over the past decade.

Chin J Traumatol. 2016 Dec 01;19(6):311-316

Authors: Wang CG, Ma ZZ

Abstract
In the article, the development of medical treatment for eye injuries in the mainland of China was reviewed. According to the data provided in Eye Injury Vitrectomy Study (EIVS), 27% of 72 eyes with no light perception (NLP) gained recovery in term of antomy and visual function. Vitrectomy initiated at more than 4 weeks after open eye injury is an independent risk factor for developing PVR. Prognosis of anatomy and visual function of the injured eye with PVR is markedly worse than that without PVR. Serious injuries of ciliary body, choroid and retina are three key parts of the eye with NLP. The concept that the treatment of the eye injury gradually focus on the whole globe is embodied. The data from 13575 in patients with traumatic eyes in 14 hospitals revealed that the rate of immediate enucleation was remarkable reduced with comparison of 20 years ago.

PMID: 28088931 [PubMed - in process]

Optic nerve avulsion after blunt ocular trauma - Case report.

Ann Agric Environ Med. 2016 Jun 02;23(2):382-3

Authors: Mackiewicz J, Tomaszewska J, Jasielska M

Abstract
INTRODUCTION: Avulsion of the optic nerve head is a rare and severe complication of ocular blunt trauma. The case reported is a 28-year old man presenting to the emergency department due to blunt trauma to his right eye globe with a tree branch. Lid haematoma and subconjunctival haemorrhage were present. Visual acuity soon after the injury was counting fingers (CF) and on admission to the Department of Ophthalmology he had no light perception (NLP). Fundus examination revealed prepapillary haemorrhage, which after few days dispersed into the vitreous cavity. Despite no light perception in the affected eye, the patient was qualified for vitrectomy. During surgery, an optic nerve avulsion with cicatricial gliosis was diagnosed. Six months after vitrectomy, the visual acuity was NLP in the right eye.
DISCUSSION: The clinical signs, mechanism, treatment and natural history of this poorly known disease are described.
CONCLUSION: Optic nerve avulsion must be considered in cases of trauma with forced rotation of the eye. Damage occurring at the disc may suggest mechanisms involving anterior luxation of the globe, retropulsion of the nerve, forced globe rotation, or a sudden explosive rise in intraocular pressure blowing the nerve off the sclera into its dural sheath. Damage and break of the nerve fibres are responsible for immediate visual impairments, and involving secondary haematomas and oedemas In spite of required safety precautions in agriculture work, eye injuries are still prevalent. Blunt ocular trauma remains a large part of this group, leading even to irreversible blindness.

PMID: 27294653 [PubMed - indexed for MEDLINE]

The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

Database (Oxford). 2017;2017:

Authors: Islamaj Doğan R, Kim S, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Wilbur WJ, Comeau DC, Dolinski K, Tyers M

Abstract
A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html.

PMID: 28077563 [PubMed - in process]

Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository.

J Digit Imaging. 2016 Feb;29(1):59-62

Authors: Hassanpour S, Langlotz CP

Abstract
Radiology report narrative contains a large amount of information about the patient's health and the radiologist's interpretation of medical findings. Most of this critical information is entered in free text format, even when structured radiology report templates are used. The radiology report narrative varies in use of terminology and language among different radiologists and organizations. The free text format and the subtlety and variations of natural language hinder the extraction of reusable information from radiology reports for decision support, quality improvement, and biomedical research. Therefore, as the first step to organize and extract the information content in a large multi-institutional free text radiology report repository, we have designed and developed an unsupervised machine learning approach to capture the main concepts in a radiology report repository and partition the reports based on their main foci. In this approach, radiology reports are modeled in a vector space and compared to each other through a cosine similarity measure. This similarity is used to cluster radiology reports and identify the repository's underlying topics. We applied our approach on a repository of 1,899,482 radiology reports from three major healthcare organizations. Our method identified 19 major radiology report topics in the repository and clustered the reports accordingly to these topics. Our results are verified by a domain expert radiologist and successfully explain the repository's primary topics and extract the corresponding reports. The results of our system provide a target-based corpus and framework for information extraction and retrieval systems for radiology reports.

PMID: 26353748 [PubMed - indexed for MEDLINE]

Bias in the reporting of sex and age in biomedical research on mouse models.

Elife. 2016 Mar 03;5:

Authors: Flórez-Vargas O, Brass A, Karystianis G, Bramhall M, Stevens R, Cruickshank S, Nenadic G

Abstract
In animal-based biomedical research, both the sex and the age of the animals studied affect disease phenotypes by modifying their susceptibility, presentation and response to treatment. The accurate reporting of experimental methods and materials, including the sex and age of animals, is essential so that other researchers can build on the results of such studies. Here we use text mining to study 15,311 research papers in which mice were the focus of the study. We find that the percentage of papers reporting the sex and age of mice has increased over the past two decades: however, only about 50% of the papers published in 2014 reported these two variables. We also compared the quality of reporting in six preclinical research areas and found evidence for different levels of sex-bias in these areas: the strongest male-bias was observed in cardiovascular disease models and the strongest female-bias was found in infectious disease models. These results demonstrate the ability of text mining to contribute to the ongoing debate about the reproducibility of research, and confirm the need to continue efforts to improve the reporting of experimental methods and materials.

PMID: 26939790 [PubMed - indexed for MEDLINE]

Unsupervised entity and relation extraction from clinical records in Italian.

Comput Biol Med. 2016 May 01;72:263-75

Authors: Alicante A, Corazza A, Isgrò F, Silvestri S

Abstract
This paper proposes and discusses the use of text mining techniques for the extraction of information from clinical records written in Italian. However, as it is very difficult and expensive to obtain annotated material for languages different from English, we only consider unsupervised approaches, where no annotated training set is necessary. We therefore propose a complete system that is structured in two steps. In the first one domain entities are extracted from the clinical records by means of a metathesaurus and standard natural language processing tools. The second step attempts to discover relations between the entity pairs extracted from the whole set of clinical records. For this last step we investigate the performance of unsupervised methods such as clustering in the space of entity pairs, represented by an ad hoc feature vector. The resulting clusters are then automatically labelled by using the most significant features. The system has been tested on a fairly large data set of clinical records in Italian, investigating the variation in the performance adopting different similarity measures in the feature space. The results of our experiments show that the unsupervised approach proposed is promising and well suited for a semi-automatic labelling of the extracted relations.

PMID: 26851833 [PubMed - indexed for MEDLINE]

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

Bioinformatics. 2017 Jan 05;:

Authors: Chen Y, Xu R

Abstract
MOTIVATION: Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery.
RESULTS: We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ([Formula: see text]). The area under the receiver operating characteristic curve for the CSN approach was also significantly higher than the SBN approach (0.91 versus 0.87, [Formula: see text]). In addition, we predicted genes for Parkinson's disease using CSNs, and demonstrated that the top-ranked genes are highly relevant to PD pathologenesis. We pin-pointed a top-ranked drug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets.
AVAILABILITY AND IMPLEMENTATION: nlp.
CASE: edu/public/data/ CONTACT: rxx@case.edu.

PMID: 28062449 [PubMed - as supplied by publisher]

Predicting High-Impact Pharmacological Targets by Integrating Transcriptome and Text-Mining Features.

J Pharm Pharm Sci. 2016 Oct - Dec;19(4):475-495

Authors: Mayburd A, Baranova A

Abstract
PURPOSE: Novel, "outside of the box" approaches are needed for evaluating candidate molecules, especially in oncology. Throughout the years of 2000-2010, the efficiency of drug development fell to barely acceptable levels, and in the second decade of this century, levels have improved only marginally. This dismal condition continues despite unprecedented progress in the development of a variety of high-throughput tools, computational methods, aggregated databases, drug repurposing programs and innovative chemistries. Here we tested a hypothesis that the economic impact of targeting a particular gene product is predictable a priori by employing a combination of transcriptome profiles and quantitative metrics reflecting existing literature.
METHODS: To extract classification features, the gene expression patterns of a posteriori high-impact and low-impact anti-cancer target sets were compared. To minimize the possible bias of text-mining, the number of manuscripts published prior to the first clinical trial or relevant review paper, as well as its first derivative in this interval, were collected and used as quantitative metrics of public interest.
RESULTS: By combining the gene expression and literature mining features, a 4-fold enrichment in high-impact targets was produced, resulting in a favourable ROC curve analysis for the top impact targets. The dataset was enriched by the highest impact anti-cancer targets, while demonstrating drastic differences in economic value between high and low-impact targets. Known anti-cancer products of EGFR, ERBB2, CYP19A1/aromatase, MTOR, PTGS2, tubulin, VEGFA, BRAF, PGR, PDGFRA, SRC, REN, CSF1R, CTLA4 and HSP90AA1 genes received the highest scores for predicted impact, while microsomal steroid sulfatase, anticoagulant protein C, p53, CDKN2A, c-Jun, and TNSFS11 were highlighted as most promising research-stage targets.
CONCLUSIONS: A significant cost reduction may be achieved by a priori impact assessment of targets and ligands before their development or repurposing. Expanding a suite of combinational treatments could also decrease the costs, while achieving a higher impact per developed ligand. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.

PMID: 28057171 [PubMed - in process]

Prevention of Evisceration or Enucleation in Endogenous Bacterial Panophthalmitis with No Light Perception and Scleral Abscess.

PLoS One. 2017;12(1):e0169603

Authors: Chen KJ, Chen YP, Chao AN, Wang NK, Wu WC, Lai CC, Chen TL

Abstract
Panophthalmitis is the most extensive ocular involvement in endophthalmitis with inflammation in periocular tissues. Severe inflammation of the anterior and posterior segments is frequently accompanied by corneal opacity, scleral abscess, and perforation or rupture. Enucleation or evisceration was the only remaining viable treatment option when all options to salvage the eye had been exhausted. The purpose of this retrospective study is to examine the outcomes of patients with endogenous bacterial panophthalmitis, no light perception and scleral abscess who were treated with multiple intravitreal and periocular injections of antibiotics and dexamethasone. Evaluation included spreading of infection to contiguous or remote sites, following evisceration or enucleation, and sympathetic ophthalmia. Eighteen patients were diagnosed with EBP, with liver abscesses in eight patients, retroperitoneal infection in four, pneumonia in two, infective endocarditis in one, cellulitis in one, drug abuse in one, and mycotic pseudoaneurysm in one. Culture results were positive for Klebsiella pneumoniae in 12 patients, Streptococcus spp. in three, Pseudomonas aeruginosa in one, Escherichia coli in one, and Staphylococcus aureus in one. The average number of periocular injections was 2.2, and the average number of intravitreal injections was 5.8. No eye required evisceration or enucleation and developed the spreading of infection to contiguous or remote sites during the follow-up. No sympathetic ophthalmia was observed in the fellow eye of all patients. Prevention of evisceration or enucleation in patients with EBP, NLP and scleral abscess can be achieved by multiple intravitreal and periocular injections of antibiotics and dexamethasone.

PMID: 28056067 [PubMed - in process]

An analysis of disease-gene relationship from Medline abstracts by DigSee.

Sci Rep. 2017 Jan 05;7:40154

Authors: Kim J, Kim JJ, Lee H

Abstract
Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer's disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.

PMID: 28054646 [PubMed - in process]

BRONCO: Biomedical entity Relation ONcology COrpus for extracting gene-variant-disease-drug relations.

Database (Oxford). 2016;2016:

Authors: Lee K, Lee S, Park S, Kim S, Kim S, Choi K, Tan AC, Kang J

Abstract
Comprehensive knowledge of genomic variants in a biological context is key for precision medicine. As next-generation sequencing technologies improve, the amount of literature containing genomic variant data, such as new functions or related phenotypes, rapidly increases. Because numerous articles are published every day, it is almost impossible to manually curate all the variant information from the literature. Many researchers focus on creating an improved automated biomedical natural language processing (BioNLP) method that extracts useful variants and their functional information from the literature. However, there is no gold-standard data set that contains texts annotated with variants and their related functions. To overcome these limitations, we introduce a Biomedical entity Relation ONcology COrpus (BRONCO) that contains more than 400 variants and their relations with genes, diseases, drugs and cell lines in the context of cancer and anti-tumor drug screening research. The variants and their relations were manually extracted from 108 full-text articles. BRONCO can be utilized to evaluate and train new methods used for extracting biomedical entity relations from full-text publications, and thus be a valuable resource to the biomedical text mining research community. Using BRONCO, we quantitatively and qualitatively evaluated the performance of three state-of-the-art BioNLP methods. We also identified their shortcomings, and suggested remedies for each method. We implemented post-processing modules for the three BioNLP methods, which improved their performance.Database URL:http://infos.korea.ac.kr/bronco.

PMID: 27074804 [PubMed - indexed for MEDLINE]

Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall.

Database (Oxford). 2016;2016:

Authors: Lowe DM, O'Boyle NM, Sayle RA

Abstract
Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical-Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.

PMID: 27060160 [PubMed - indexed for MEDLINE]

Natural language processing to ascertain two key variables from operative reports in ophthalmology.

Pharmacoepidemiol Drug Saf. 2017 Jan 03;:

Authors: Liu L, Shorstein NH, Amsden LB, Herrinton LJ

Abstract
PURPOSE: Antibiotic prophylaxis is critical to ophthalmology and other surgical specialties. We performed natural language processing (NLP) of 743 838 operative notes recorded for 315 246 surgeries to ascertain two variables needed to study the comparative effectiveness of antibiotic prophylaxis in cataract surgery. The first key variable was an exposure variable, intracameral antibiotic injection. The second was an intraoperative complication, posterior capsular rupture (PCR), which functioned as a potential confounder. To help other researchers use NLP in their settings, we describe our NLP protocol and lessons learned.
METHODS: For each of the two variables, we used SAS Text Miner and other SAS text-processing modules with a training set of 10 000 (1.3%) operative notes to develop a lexicon. The lexica identified misspellings, abbreviations, and negations, and linked words into concepts (e.g. "antibiotic" linked with "injection"). We confirmed the NLP tools by iteratively obtaining random samples of 2000 (0.3%) notes, with replacement.
RESULTS: The NLP tools identified approximately 60 000 intracameral antibiotic injections and 3500 cases of PCR. The positive and negative predictive values for intracameral antibiotic injection exceeded 99%. For the intraoperative complication, they exceeded 94%.
CONCLUSION: NLP was a valid and feasible method for obtaining critical variables needed for a research study of surgical safety. These NLP tools were intended for use in the study sample. Use with external datasets or future datasets in our own setting would require further testing. Copyright © 2017 John Wiley & Sons, Ltd.

PMID: 28052483 [PubMed - as supplied by publisher]

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs.

J Med Syst. 2017 Feb;41(2):32

Authors: Garvin JH, Kalsy M, Brandt C, Luther SL, Divita G, Coronado G, Redd D, Christensen C, Hill B, Kelly N, Treitler QZ

Abstract
In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic. We undertook 15 interviews and held two workshop discussions. The main areas of requirements related to; design and functionality, resources, and information. Stakeholders also confirmed the vision of the second generation of the Ecosystem and recommendations included; adding mechanisms to better understand terms, measuring collaboration to demonstrate value, and datasets/tools to navigate spelling errors with consumer language, among others. Stakeholders also recommended capability to: communicate with developers working on the next version of the VA electronic health record (VistA Evolution), provide a mechanism to automatically monitor download of tools and to automatically provide a summary of the downloads to Ecosystem contributors and funders. After three rounds of coding and discussion, we determined the percent agreement of two coders to be 97.2% and the kappa to be 0.7851. The vision of the VA Clinical NLP Ecosystem met stakeholder needs. Interviews and discussion provided key requirements that inform the design of the VA Clinical NLP Ecosystem.

PMID: 28050745 [PubMed - in process]

Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.

J Digit Imaging. 2017 Jan 03;:

Authors: Hassanpour S, Bay G, Langlotz CP

Abstract
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.

PMID: 28050714 [PubMed - as supplied by publisher]