Knowledge Author: Facilitating user-driven, Domain content development to support clinical information extraction

Background: Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician's domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. Results: Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author's schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system's performance. The first study evaluated Knowledge Author's flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76%) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author's output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author's domain content produced high recall for concepts (targeted findings: 86%) and varied recall for modifiers (certainty: 91% sidedness: 80%, neurovascular anatomy: 46%). Conclusion: Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts

Annotation analysis for testing drug safety signals using unstructured clinical notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records

The influence of the atmospheric boundary layer on nocturnal layers of noctuids and other moths migrating over southern Britain

Insects migrating at high altitude over southern Britain have been continuously monitored by automatically-operating, vertical-looking radars over a period of several years. During some occasions in the summer months, the migrants were observed to form well-defined layer concentrations, typically at heights of 200-400 m, in the stable night-time atmosphere. Under these conditions, insects are likely to have control over their vertical movements and are selecting flight heights which are favourable for long-range migration. We therefore investigated the factors influencing the formation of these insect layers by comparing radar measurements of the vertical distribution of insect density with meteorological profiles generated by the UK Met. Office’s Unified Model (UM). Radar-derived measurements of mass and displacement speed, along with data from Rothamsted Insect Survey light traps provided information on the identity of the migrants. We present here three case studies where noctuid and pyralid moths contributed substantially to the observed layers. The major meteorological factors influencing the layer concentrations appeared to be: (a) the altitude of the warmest air, (b) heights corresponding to temperature preferences or thresholds for sustained migration and (c), on nights when air temperatures are relatively high, wind-speed maxima associated with the nocturnal jet. Back-trajectories indicated that layer duration may have been determined by the distance to the coast. Overall, the unique combination of meteorological data from the UM and insect data from entomological radar described here show considerable promise for systematic studies of high-altitude insect layering

CSI-OMIM - Clinical Synopsis Search in OMIM

<p>Abstract</p> <p>Background</p> <p>The OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.</p> <p>These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications.</p> <p>Description</p> <p>Our method allows efficient and complete search of OMIM phenotypes as well as improved data-mining of the OMIM phenome. Applying natural language processing, each phrase is tagged with additional semantic information using UMLS and MESH. Using a grammar based method, annotated phrases are clustered into groups denoting similar phenotypes. These groups of synonymous expressions enable precise search, as query terms can be matched with the many variations that appear in OMIM, while avoiding over-matching expressions that include the query term in a negative context. On the basis of these clusters, we computed pair-wise similarity among syndromes in OMIM. Using this new similarity measure, we identified 79,770 new connections between syndromes, an average of 16 new connections per syndrome. Our project is Web-based and available at <url>http://fohs.bgu.ac.il/s2g/csiomim</url></p> <p>Conclusions</p> <p>The resulting enhanced search functionality provides clinicians with an efficient tool for diagnosis. This search application is also used for finding similar syndromes for the candidate gene prioritization tool S2G.</p> <p>The enhanced OMIM database we produced can be further used for bioinformatics purposes such as linking phenotypes and genes based on syndrome similarities and the known genes in Morbidmap.</p

Orientation in high-flying migrant insects in relation to flows: mechanisms and strategies

High-flying insect migrants have been shown to display sophisticated flight orientations that can, for example, maximize distance travelled by exploiting tailwinds, and reduce drift from seasonally optimal directions. Here, we provide a comprehensive overview of the theoretical and empirical evidence for the mechanisms underlying the selection and maintenance of the observed flight headings, and the detection of wind direction and speed, for insects flying hundreds of metres above the ground. Different mechanisms may be used—visual perception of the apparent ground movement or mechanosensory cues maintained by intrinsic features of the wind—depending on circumstances (e.g. day or night migrations). In addition to putative turbulence-induced velocity, acceleration and temperature cues, we present a new mathematical analysis which shows that 'jerks' (the time-derivative of accelerations) can provide indicators of wind direction at altitude. The adaptive benefits of the different orientation strategies are briefly discussed, and we place these new findings for insects within a wider context by comparisons with the latest research on other flying and swimming organisms

Developing a manually annotated clinical document corpus to identify phenotypic information for inflammatory bowel disease

<p>Abstract</p> <p>Background</p> <p>Natural Language Processing (NLP) systems can be used for specific Information Extraction (IE) tasks such as extracting phenotypic data from the electronic medical record (EMR). These data are useful for translational research and are often found only in free text clinical notes. A key required step for IE is the manual annotation of clinical corpora and the creation of a reference standard for (1) training and validation tasks and (2) to focus and clarify NLP system requirements. These tasks are time consuming, expensive, and require considerable effort on the part of human reviewers.</p> <p>Methods</p> <p>Using a set of clinical documents from the VA EMR for a particular use case of interest we identify specific challenges and present several opportunities for annotation tasks. We demonstrate specific methods using an open source annotation tool, a customized annotation schema, and a corpus of clinical documents for patients known to have a diagnosis of Inflammatory Bowel Disease (IBD). We report clinician annotator agreement at the document, concept, and concept attribute level. We estimate concept yield in terms of annotated concepts within specific note sections and document types.</p> <p>Results</p> <p>Annotator agreement at the document level for documents that contained concepts of interest for IBD using estimated Kappa statistic (95% CI) was very high at 0.87 (0.82, 0.93). At the concept level, F-measure ranged from 0.61 to 0.83. However, agreement varied greatly at the specific concept attribute level. For this particular use case (IBD), clinical documents producing the highest concept yield per document included GI clinic notes and primary care notes. Within the various types of notes, the highest concept yield was in sections representing patient assessment and history of presenting illness. Ancillary service documents and family history and plan note sections produced the lowest concept yield.</p> <p>Conclusion</p> <p>Challenges include defining and building appropriate annotation schemas, adequately training clinician annotators, and determining the appropriate level of information to be annotated. Opportunities include narrowing the focus of information extraction to use case specific note types and sections, especially in cases where NLP systems will be used to extract information from large repositories of electronic clinical note documents.</p

Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

Automatic construction of rule-based ICD-9-CM coding systems

Background: In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health institutes and are assigned to clinical records manually following clinical treatment. Since this labeling task requires expert knowledge in the field of medicine, the process itself is costly and is prone to errors as human annotators have to consider thousands of possible codes when assigning the right ICD-9-CM labels to a document. In this study we use the datasets made available for training and testing automated ICD-9-CM coding systems by the organisers of an International Challenge on Classifying Clinical Free Text Using Natural Language Processing in spring 2007. The challenge itself was dominated by entirely or partly rule-based systems that solve the coding task using a set of hand crafted expert rules. Since the feasibility of the construction of such systems for thousands of ICD codes is indeed questionable, we decided to examine the problem of automatically constructing similar rule sets that turned out to achieve a remarkable accuracy in the shared task challenge. Results: Our results are very promising in the sense that we managed to achieve comparable results with purely hand-crafted ICD-9-CM classifiers. Our best model got a 90.26 % F measure on the training dataset and an 88.93 % F measure on the challenge test dataset, using the micro-averaged Fβ=1 measure, the official evaluatio

Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search

Radiology reports contain information that can be mined using a search engine for teaching, research, and quality assurance purposes. Current search engines look for exact matches to the search term, but they do not differentiate between reports in which the search term appears in a positive context (i.e., being present) from those in which the search term appears in the context of negation and uncertainty. We describe RadReportMiner, a context-aware search engine, and compare its retrieval performance with a generic search engine, Google Desktop. We created a corpus of 464 radiology reports which described at least one of five findings (appendicitis, hydronephrosis, fracture, optic neuritis, and pneumonia). Each report was classified by a radiologist as positive (finding described to be present) or negative (finding described to be absent or uncertain). The same reports were then classified by RadReportMiner and Google Desktop. RadReportMiner achieved a higher precision (81%), compared with Google Desktop (27%; p < 0.0001). RadReportMiner had a lower recall (72%) compared with Google Desktop (87%; p = 0.006). We conclude that adding negation and uncertainty identification to a word-based radiology report search engine improves the precision of search results over a search engine that does not take this information into account. Our approach may be useful to adopt into current report retrieval systems to help radiologists to more accurately search for radiology reports