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

Instant availability of patient records, but diminished availability of patient information: A multi-method study of GP's use of electronic patient records

<p>Abstract</p> <p>Background</p> <p>In spite of succesful adoption of electronic patient records (EPR) by Norwegian GPs, what constitutes the actual benefits and effects of the use of EPRs in the perspective of the GPs and patients has not been fully characterized. We wanted to study primary care physicians' use of electronic patient record (EPR) systems in terms of use of different EPR functions and the time spent on using the records, as well as the potential effects of EPR systems on the clinician-patient relationship.</p> <p>Methods</p> <p>A combined qualitative and quantitative study that uses data collected from focus groups, observations of primary care encounters and a questionnaire survey of a random sample of general practitioners to describe their use of EPR in primary care.</p> <p>Results</p> <p>The overall availability of individual patient records had improved, but the availability of the information within each EPR was not satisfactory. GPs' use of EPRs were efficient and comprehensive, but have resulted in transfer of administrative work from secretaries to physicians. We found no indications of disturbance of the clinician-patient relationship by use of computers in this study.</p> <p>Conclusion</p> <p>Although GPs are generally satisfied with their EPRs systems, there are still unmet needs and functionality to be covered. It is urgent to find methods that can make a better representation of information in large patient records as well as prevent EPRs from contributing to increased administrative workload of physicians.</p

Mps1Mph1 kinase phosphorylates Mad3 to inhibit Cdc20Slp1-APC/C and maintain spindle checkpoint arrests

<div><p>The spindle checkpoint is a mitotic surveillance system which ensures equal segregation of sister chromatids. It delays anaphase onset by inhibiting the action of the E3 ubiquitin ligase known as the anaphase promoting complex or cyclosome (APC/C). Mad3/BubR1 is a key component of the mitotic checkpoint complex (MCC) which binds and inhibits the APC/C early in mitosis. Mps1^Mph1 kinase is critical for checkpoint signalling and MCC-APC/C inhibition, yet few substrates have been identified. Here we identify Mad3 as a substrate of fission yeast Mps1^Mph1 kinase. We map and mutate phosphorylation sites in Mad3, producing mutants that are targeted to kinetochores and assembled into MCC, yet display reduced APC/C binding and are unable to maintain checkpoint arrests. We show biochemically that Mad3 phospho-mimics are potent APC/C inhibitors <i>in vitro</i>, demonstrating that Mad3p modification can directly influence Cdc20^Slp1-APC/C activity. This genetic dissection of APC/C inhibition demonstrates that Mps1^Mph1 kinase-dependent modifications of Mad3 and Mad2 act in a concerted manner to maintain spindle checkpoint arrests.</p></div

Rewriting Natural Language Queries Using Patterns

International audienceIn this paper, a method based on pre-defined patterns, which rewrites natural language queries into a multi-layer, flexible, scalable and object-oriented query language, is presented. The method has been conceived to assist physicians in their search for clinical information in an Electronic Health Records system. Indeed, the query language of the system being difficult to handle for physicians, this method allows querying using natural language vs. using dedicated object-oriented query language. The information extraction method that has been developed can be seen as a named entity recognition system based on regular expressions that tags pieces of the query. The patterns are constructed recursively from the initial natural language query and from atomic patterns that correspond to the entities, the relationships and the constraints of the underlying model representing Electronic Health Records. Further evaluation is needed, but the preliminary results obtained by testing a set of natural language queries are very encouraging

A Textual Recommender System for Clinical Data

When faced with an exceptional clinical case, doctors like to review information about similar patients to guide their decision-making. Retrieving relevant cases, however, is a hard and time-consuming task: Hospital databases of free-text physician letters provide a rich resource of information but are usually only searchable with string-matching methods. Here, we present a recommender system that automatically finds physician letters similar to a specified reference letter using an information retrieval procedure. We use a small-scale, prototypical dataset to compare the system’s recommendations with physicians’ similarity judgments of letter pairs in a psychological experiment. The results show that the recommender system captures expert intuitions about letter similarity well and is usable for practical applications