A cascade of classifiers for extracting medication information from discharge summaries

<p>Abstract</p> <p>Background</p> <p>Extracting medication information from clinical records has many potential applications, and recently published research, systems, and competitions reflect an interest therein. Much of the early extraction work involved rules and lexicons, but more recently machine learning has been applied to the task.</p> <p>Methods</p> <p>We present a hybrid system consisting of two parts. The first part, field detection, uses a cascade of statistical classifiers to identify medication-related named entities. The second part uses simple heuristics to link those entities into medication events.</p> <p>Results</p> <p>The system achieved performance that is comparable to other approaches to the same task. This performance is further improved by adding features that reference external medication name lists.</p> <p>Conclusions</p> <p>This study demonstrates that our hybrid approach outperforms purely statistical or rule-based systems. The study also shows that a cascade of classifiers works better than a single classifier in extracting medication information. The system is available as is upon request from the first author.</p

Computational analysis of pathogen-borne metallo β-lactamases reveals discriminating structural features between B1 types

<p>Abstract</p> <p>Background</p> <p>Genes conferring antibiotic resistance to groups of bacterial pathogens are cause for considerable concern, as many once-reliable antibiotics continue to see a reduction in efficacy. The recent discovery of the metallo β-lactamase <it>blaNDM-1 </it>gene, which appears to grant antibiotic resistance to a variety of Enterobacteriaceae <it>via </it>a mobile plasmid, is one example of this distressing trend. The following work describes a computational analysis of pathogen-borne MBLs that focuses on the structural aspects of characterized proteins.</p> <p>Results</p> <p>Using both sequence and structural analyses, we examine residues and structural features specific to various pathogen-borne MBL types. This analysis identifies a linker region within MBL-like folds that may act as a discriminating structural feature between these proteins, and specifically resistance-associated acquirable MBLs. Recently released crystal structures of the newly emerged NDM-1 protein were aligned against related MBL structures using a variety of global and local structural alignment methods, and the overall fold conformation is examined for structural conservation. Conservation appears to be present in most areas of the protein, yet is strikingly absent within a linker region, making NDM-1 unique with respect to a linker-based classification scheme. Variability analysis of the NDM-1 crystal structure highlights unique residues in key regions as well as identifying several characteristics shared with other transferable MBLs.</p> <p>Conclusions</p> <p>A discriminating linker region identified in MBL proteins is highlighted and examined in the context of NDM-1 and primarily three other MBL types: IMP-1, VIM-2 and ccrA. The presence of an unusual linker region variant and uncommon amino acid composition at specific structurally important sites may help to explain the unusually broad kinetic profile of NDM-1 and may aid in directing research attention to areas of this protein, and possibly other MBLs, that may be targeted for inactivation or attenuation of enzymatic activity.</p

Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations

The EC2KEGG output for the RV1423 analysis sorted in ascending order by the FDR value. (XLSX 58 kb

Extracting medication information from clinical text

The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research

BIOMEDIATOR DATA INTEGRATION AND INFERENCE FOR FUNCTIONAL ANNOTATION OF ANONYMOUS SEQUENCES

Scientists working on genomics projects are often faced with the difficult task of sifting through large amounts of biological information dispersed across various online data sources that are relevant to their area or organism of research. Gene annotation, the process of identifying the functional role of a possible gene, in particular has become increasingly more time-consuming and laborious to conduct as more genomes are sequenced and the number of candidate genes continues to increase at near-exponential pace; genes are left un-annotated, or worse, incorrectly annotated. Many groups have attempted to address the annotation backlog through automated annotation systems that are geared toward specific organisms, and which may thus not possess the necessary flexibility and scalability to annotate other genomes. In this paper, we present a method and framework which attempts to address problems inherent in manual and automatic annotation by coupling a data integration system, BioMediator, to an inference engine with the aim of elucidating functional annotations. The framework and heuristics developed are not specific to any particular genome. We validated the method with a set of randomly-selected annotated sequences from a variety of organisms. Preliminary results show that the hybrid data integration and inference approach generates functional annotations that are as good as or better than “gold standard ” annotations ~80 % of the time. 1

Bootstrapping the Location-enhanced Word Wide Web

Our challenge to the research community is to make location-enhanced web services valuable and readily accessible to a very large number of people in daily, real world, situations. We envisage a global scale, multiorganization and interdisciplinary initiative, Place Lab, that will bootstrap the broad adoption of the location-enhanced Web. Our research collective is developing an open software base (providing low-cost private positioning technology) and fostering the formation of user and developer communities. Through individual Place Labs initially seeded on the campuses of universities, colleges, and research organizations this initiative will be a vehicle for research, instruction, collaboration and application sharing. This paper describes some of our first steps towards meeting this challenge. Keywords Location-aware; context-aware; ubiquitous; positioning systems; WiFi; GPS; web services; wireless hotspots; wardriving