DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult to correlate phenotypic data to genomic data. In addition, genomic data are being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from electronic medical records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually annotated dataset of the University of Pittsburgh Medical Center breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment. Cancer Res; 77(21); e115-8. ©2017 AACR.

Automated annotation and classification of BI-RADS assessment from radiology reports.

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities. The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015. Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART. Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop.

A Taxonomy of Delivery and Documentation Deviations During Delivery of High-Fidelity Simulations.

INTRODUCTION: We developed a taxonomy of simulation delivery and documentation deviations noted during a multicenter, high-fidelity simulation trial that was conducted to assess practicing physicians' performance. Eight simulation centers sought to implement standardized scenarios over 2 years. Rules, guidelines, and detailed scenario scripts were established to facilitate reproducible scenario delivery; however, pilot trials revealed deviations from those rubrics. A taxonomy with hierarchically arranged terms that define a lack of standardization of simulation scenario delivery was then created to aid educators and researchers in assessing and describing their ability to reproducibly conduct simulations. METHODS: Thirty-six types of delivery or documentation deviations were identified from the scenario scripts and study rules. Using a Delphi technique and open card sorting, simulation experts formulated a taxonomy of high-fidelity simulation execution and documentation deviations. The taxonomy was iteratively refined and then tested by 2 investigators not involved with its development. RESULTS: The taxonomy has 2 main classes, simulation center deviation and participant deviation, which are further subdivided into as many as 6 subclasses. Inter-rater classification agreement using the taxonomy was 74% or greater for each of the 7 levels of its hierarchy. Cohen kappa calculations confirmed substantial agreement beyond that expected by chance. All deviations were classified within the taxonomy. CONCLUSIONS: This is a useful taxonomy that standardizes terms for simulation delivery and documentation deviations, facilitates quality assurance in scenario delivery, and enables quantification of the impact of deviations upon simulation-based performance assessment.

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.

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.

BACKGROUND: Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. RESULTS: We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. CONCLUSION: NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.

Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network.

Building a gold standard to construct search filters: a case study with biomarkers for oral cancer.

OBJECTIVE: To support clinical researchers, librarians and informationists may need search filters for particular tasks. Development of filters typically depends on a "gold standard" dataset. This paper describes generalizable methods for creating a gold standard to support future filter development and evaluation using oral squamous cell carcinoma (OSCC) as a case study. OSCC is the most common malignancy affecting the oral cavity. Investigation of biomarkers with potential prognostic utility is an active area of research in OSCC. The methods discussed here should be useful for designing quality search filters in similar domains. METHODS: The authors searched MEDLINE for prognostic studies of OSCC, developed annotation guidelines for screeners, ran three calibration trials before annotating the remaining body of citations, and measured inter-annotator agreement (IAA). RESULTS: We retrieved 1,818 citations. After calibration, we screened the remaining citations (n = 1,767; 97.2%); IAA was substantial (kappa = 0.76). The dataset has 497 (27.3%) citations representing OSCC studies of potential prognostic biomarkers. CONCLUSIONS: The gold standard dataset is likely to be high quality and useful for future development and evaluation of filters for OSCC studies of potential prognostic biomarkers. IMPLICATIONS: The methodology we used is generalizable to other domains requiring a reference standard to evaluate the performance of search filters. A gold standard is essential because the labels regarding relevance enable computation of diagnostic metrics, such as sensitivity and specificity. Librarians and informationists with data analysis skills could contribute to developing gold standard datasets and subsequent filters tuned for their patrons' domains of interest.

A Prototype for a Hybrid System to Support Systematic Review Teams: A Case Study of Organ Transplantation.

We describe a prototype for a hybrid system designed to reduce the number of citations needed to re-screen (NNRS) by systematic reviewers, where citations include titles, abstracts, and metadata. The system obviates the need for screening the entire set of citations a second time, which is typically done to control human error. The reference set is based on a complex review about organ transplantation (N=10,796 citations). Data were split into 50% training and test sets, randomly stratified for percentage eligible citations. The system consists of a rule-based module and a machine-learning (ML) module. The former substantially reduces the number of negative citations passed to the ML module and improves imbalance. Relative to the baseline, the system reduces classification error (5.6% vs 2.9%) thereby reducing NNRS by 47.3% (300 vs 158). We discuss the implications of de-emphasizing sensitivity (recall) in favor of specificity and negative predictive value to reduce screening burden.

METACOGNITIVE SCAFFOLDS IMPROVE SELF-JUDGMENTS OF ACCURACY IN A MEDICAL INTELLIGENT TUTORING SYSTEM.

In this study, we examined the effect of two metacognitive scaffolds on the accuracy of confidence judgments made while diagnosing dermatopathology slides in SlideTutor. Thirty-one (N = 31) first- to fourth-year pathology and dermatology residents were randomly assigned to one of the two scaffolding conditions. The cases used in this study were selected from the domain of Nodular and Diffuse Dermatitides. Both groups worked with a version of SlideTutor that provided immediate feedback on their actions for two hours before proceeding to solve cases in either the Considering Alternatives or Playback condition. No immediate feedback was provided on actions performed by participants in the scaffolding mode. Measurements included learning gains (pre-test and post-test), as well as metacognitive performance, including Goodman-Kruskal Gamma correlation, bias, and discrimination. Results showed that participants in both conditions improved significantly in terms of their diagnostic scores from pre-test to post-test. More importantly, participants in the Considering Alternatives condition outperformed those in the Playback condition in the accuracy of their confidence judgments and the discrimination of the correctness of their assertions while solving cases. The results suggested that presenting participants with their diagnostic decision paths and highlighting correct and incorrect paths helps them to become more metacognitively accurate in their confidence judgments.

Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence.

OBJECTIVES: Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance. METHODS: We built a database of citations from 5 systematic reviews that varied with respect to domain, topic, and sponsor. Consensus judgments regarding eligibility were inferred from published reports. We extracted 5 feature sets from citations: alphabetic, alphanumeric(+), indexing, features mapped to concepts in systematic reviews, and topic models. To simulate a two-person team, we divided the data into random halves. We optimized the parameters of a Bayesian classifier, then trained and tested models on alternate data halves. Overall, we conducted 50 independent tests. RESULTS: All tests of summary performance (mean F3) surpassed the corresponding baseline, P<0.0001. The ranks for mean F3, precision, and classification error were statistically different across feature sets averaged over reviews; P-values for Friedman's test were .045, .002, and .002, respectively. Differences in ranks for mean recall were not statistically significant. Alphanumeric(+) features were associated with best performance; mean reduction in screening burden for this feature type ranged from 88% to 98% for the second pass through citations and from 38% to 48% overall. CONCLUSIONS: A computer-assisted, decision support system based on our methods could substantially reduce the burden of screening citations for systematic review teams and solo reviewers. Additionally, such a system could deliver quality assurance both by confirming concordant decisions and by naming studies associated with discordant decisions for further consideration.

An exploratory investigation of links between changes in adipokines and quality of life in individuals undergoing weight loss interventions: possible implications for cancer research.

OBJECTIVE: Obesity has been linked to a wide spectrum of malignancies, with the strongest association demonstrated for endometrial cancer. Although the mechanisms are not yet entirely clear, a number of risk biomarkers have been proposed, including altered adipokines. Systemic levels of these adipose derived molecules have also been linked in prior research to self-reported quality of life (QOL). The study objective was to examine the hypothesis that adipokine changes during intentional weight loss may be associated with changes in QOL. METHODS: Fifty-two female participants were selected from two behavioral weight loss trials (SMART and PREFER) on the basis of achieving successful weight loss at 6month assessment, availability of blood samples and completion of standard SF-36 QOL questionnaires. Levels of adiponectin, leptin, and resistin were measured using xMAP immunoassays. Changes in QOL were examined using linear regression models in relation to pre- and post-intervention changes in biomarker levels and BMI. RESULTS: Significant changes between pre- and post-intervention were observed for leptin. Controlling for baseline BMI, leptin was the only biomarker that predicted change in QOL (Physical Component Scale, PCS). Linear regression models demonstrated that leptin continued to be a significant predictor of change in PCS when other possible predictor variables were included in the model. CONCLUSIONS: This study is among the first to demonstrate that changes in PCS may be regulated by levels of both metabolic variables and adipokines. An improved understanding of biological mechanisms associated with weight loss and the role of QOL may help guide preventive strategies for obesity-associated cancers.

Digital pathology consultations-a new era in digital imaging, challenges and practical applications.

Digital pathology has grown dramatically in the last 10 years and has created opportunities to not only support the triaging of difficult cases among specialists within an organization, but also enable remote pathology consultations with external organizations across the world. This study investigated one organization's need for a vendor agnostic Digital Pathology Consultation workflow solution that overcomes the challenges associated with the transfer of large studies across a local area network or across the Internet. The organization investigated is a large multifacility healthcare organization that consists of 20 hospitals spread across a wide geographical area. The organization has one of the largest academic pathology departments in the USA, with more than 100 diagnostic anatomic pathologists. This organization developed a set of web-based tools to support the workflow of digital pathology consultations and allow the viewing of whole slide images. The challenges and practical implementations of two different use cases are addressed: the occasional end user (professional or patient) requesting a second opinion and the external laboratory or hospital looking for an established consultative relationship with a large volume of cases. The solution presented in this study addresses the challenges associated with the distribution of large images and the lack of established imaging standards, while providing for a convenient and secure portal for pathologist report entry and distribution.

Automated detection of heuristics and biases among pathologists in a computer-based system.

The purpose of this study is threefold: (1) to develop an automated, computer-based method to detect heuristics and biases as pathologists examine virtual slide cases, (2) to measure the frequency and distribution of heuristics and errors across three levels of training, and (3) to examine relationships of heuristics to biases, and biases to diagnostic errors. The authors conducted the study using a computer-based system to view and diagnose virtual slide cases. The software recorded participant responses throughout the diagnostic process, and automatically classified participant actions based on definitions of eight common heuristics and/or biases. The authors measured frequency of heuristic use and bias across three levels of training. Biases studied were detected at varying frequencies, with availability and search satisficing observed most frequently. There were few significant differences by level of training. For representativeness and anchoring, the heuristic was used appropriately as often or more often than it was used in biased judgment. Approximately half of the diagnostic errors were associated with one or more biases. We conclude that heuristic use and biases were observed among physicians at all levels of training using the virtual slide system, although their frequencies varied. The system can be employed to detect heuristic use and to test methods for decreasing diagnostic errors resulting from cognitive biases.

Perceptual analysis of the reading of dermatopathology virtual slides by pathology residents.

CONTEXT: The process by which pathologists arrive at a given diagnosis-a combination of their slide exploration strategy, perceptual information gathering, and cognitive decision making-has not been thoroughly explored, and many questions remain unanswered. OBJECTIVE: To determine how pathology residents learn to diagnose inflammatory skin dermatoses, we contrasted the slide exploration strategy, perceptual capture of relevant histopathologic findings, and cognitive integration of identified features between 2 groups of residents, those who had and those who had not undergone their dermatopathology rotation. DESIGN: Residents read a case set of 20 virtual slides (10 depicting nodular and diffuse dermatitis and 10 depicting subepidermal vesicular dermatitis), using an in-house-developed interface. We recorded residents' reports of diagnostic findings, conjectured diagnostic hypotheses, and final (or differential) diagnosis for each case, and time stamped each interaction with the interface. We created search maps of residents' slide exploration strategy. RESULTS: No statistically significant differences were observed between the resident groups in the number of correctly or incorrectly reported diagnostic findings, but residents with dermatopathology training generated significantly more correct hypotheses (mean improvement of 88.5%) and correct diagnoses (70% of all correct diagnoses). CONCLUSIONS: Two types of slide exploration strategy were identified for both groups: (1) a focused and efficient search, observed when the final diagnosis was correct; and (2) a more dispersed, time-consuming strategy, observed when the final diagnosis was incorrect. This difference was statistically significant, and it suggests that initial interpretation of a slide may bias further slide exploration.

Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold.

Previous studies in our laboratory have shown the benefits of immediate feedback on cognitive performance for pathology residents using an intelligent tutoring system (ITS) in pathology. In this study, we examined the effect of immediate feedback on metacognitive performance, and investigated whether other metacognitive scaffolds will support metacognitive gains when immediate feedback is faded. Twenty-three participants were randomized into intervention and control groups. For both groups, periods working with the ITS under varying conditions were alternated with independent computer-based assessments. On day 1, a within-subjects design was used to evaluate the effect of immediate feedback on cognitive and metacognitive performance. On day 2, a between-subjects design was used to compare the use of other metacognitive scaffolds (intervention group) against no metacognitive scaffolds (control group) on cognitive and metacognitive performance, as immediate feedback was faded. Measurements included learning gains (a measure of cognitive performance), as well as several measures of metacognitive performance, including Goodman-Kruskal gamma correlation (G), bias, and discrimination. For the intervention group, we also computed metacognitive measures during tutoring sessions. Results showed that immediate feedback in an intelligent tutoring system had a statistically significant positive effect on learning gains, G and discrimination. Removal of immediate feedback was associated with decreasing metacognitive performance, and this decline was not prevented when students used a version of the tutoring system that provided other metacognitive scaffolds. Results obtained directly from the ITS suggest that other metacognitive scaffolds do have a positive effect on G and discrimination, as immediate feedback is faded. We conclude that immediate feedback had a positive effect on both metacognitive and cognitive gains in a medical tutoring system. Other metacognitive scaffolds were not sufficient to replace immediate feedback in this study. However, results obtained directly from the tutoring system are not consistent with results obtained from assessments. In order to facilitate transfer to real-world tasks, further research will be needed to determine the optimum methods for supporting metacognition as immediate feedback is faded.

Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths.

OBJECTIVES: Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement. METHODS: Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined. RESULTS: Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path. CONCLUSIONS: Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.

A natural language intelligent tutoring system for training pathologists: implementation and evaluation.

INTRODUCTION: We developed and evaluated a Natural Language Interface (NLI) for an Intelligent Tutoring System (ITS) in Diagnostic Pathology. The system teaches residents to examine pathologic slides and write accurate pathology reports while providing immediate feedback on errors they make in their slide review and diagnostic reports. Residents can ask for help at any point in the case, and will receive context-specific feedback. RESEARCH QUESTIONS: We evaluated (1) the performance of our natural language system, (2) the effect of the system on learning (3) the effect of feedback timing on learning gains and (4) the effect of ReportTutor on performance to self-assessment correlations. METHODS: The study uses a crossover 2 x 2 factorial design. We recruited 20 subjects from 4 academic programs. Subjects were randomly assigned to one of the four conditions--two conditions for the immediate interface, and two for the delayed interface. An expert dermatopathologist created a reference standard and 2 board certified AP/CP pathology fellows manually coded the residents' assessment reports. Subjects were given the opportunity to self grade their performance and we used a survey to determine student response to both interfaces. RESULTS: Our results show a highly significant improvement in report writing after one tutoring session with 4-fold increase in the learning gains with both interfaces but no effect of feedback timing on performance gains. Residents who used the immediate feedback interface first experienced a feature learning gain that is correlated with the number of cases they viewed. There was no correlation between performance and self-assessment in either condition.

Evaluation of an intelligent tutoring system in pathology: effects of external representation on performance gains, metacognition, and acceptance.

OBJECTIVE: Determine effects of computer-based tutoring on diagnostic performance gains, meta-cognition, and acceptance using two different problem representations. Describe impact of tutoring on spectrum of diagnostic skills required for task performance. Identify key features of student-tutor interaction contributing to learning gains. DESIGN: Prospective, between-subjects study, controlled for participant level of training. Resident physicians in two academic pathology programs spent four hours using one of two interfaces which differed mainly in external problem representation. The case-focused representation provided an open-learning environment in which students were free to explore evidence-hypothesis relationships within a case, but could not visualize the entire diagnostic space. The knowledge-focused representation provided an interactive representation of the entire diagnostic space, which more tightly constrained student actions. MEASUREMENTS: Metrics included results of pretest, post-test and retention-test for multiple choice and case diagnosis tests, ratios of performance to student reported certainty, results of participant survey, learning curves, and interaction behaviors during tutoring. RESULTS: Students had highly significant learning gains after one tutoring session. Learning was retained at one week. There were no differences between the two interfaces in learning gains on post-test or retention test. Only students in the knowledge-focused interface exhibited significant metacognitive gains from pretest to post-test and pretest to retention test. Students rated the knowledge-focused interface significantly higher than the case-focused interface. CONCLUSIONS: Cognitive tutoring is associated with improved diagnostic performance in a complex medical domain. The effect is retained at one-week post-training. Knowledge-focused external problem representation shows an advantage over case-focused representation for metacognitive effects and user acceptance.

ReportTutor - an intelligent tutoring system that uses a natural language interface.

ReportTutor is an extension to our work on Intelligent Tutoring Systems for visual diagnosis. ReportTutor combines a virtual microscope and a natural language interface to allow students to visually inspect a virtual slide as they type a diagnostic report on the case. The system monitors both actions in the virtual microscope interface as well as text created by the student in the reporting interface. It provides feedback about the correctness, completeness, and style of the report. ReportTutor uses MMTx with a custom data-source created with the NCI Metathesaurus. A separate ontology of cancer specific concepts is used to structure the domain knowledge needed for evaluation of the student's input including co-reference resolution. As part of the early evaluation of the system, we collected data from 4 pathology residents who typed in their reports without the tutoring aspects of the system, and compared responses to an expert dermatopathologist. We analyzed the resulting reports to (1) identify the error rates and distribution among student reports, (2) determine the performance of the system in identifying features within student reports, and (3) measure the accuracy of the system in distinguishing between correct and incorrect report elements.

Fiswidgets: a graphical computing environment for neuroimaging analysis.

Current fMRI neuroimaging software programs offer the researcher a wealth of analysis methods and tools. However, the incompatibilities in user interface, data format, and computing environment in these tools make it difficult if not impossible for most researchers to take advantage of the full set of resources available for neuroimaging analyses. We describe a graphical computing environment, Functional Imaging Software Widgets (fiswidgets), which was developed to address these interoperability and usability problems. This environment provides a desktop style framework into which 100 subcomponents from a number of widely used fMRI analysis software packages (e.g., AFNI, AIR) are incorporated. It is an open-source, extensible environment available for reuse and modification by other software developers. A discussion of the design criteria (open architecture, modularity, wrapper technology, commercial utilities) that support such loosely integrative computing environments, and the problems entailed in maintaining them (development overhead, distribution logistics, format incompatibilities, graphics vs scripting tradeoffs, and appropriate acknowledgment of software developers) follows.