Brain connectivity in body dysmorphic disorder compared with controls: a diffusion tensor imaging study.

BACKGROUND: Several neuroimaging studies have investigated brain grey matter in people with body dysmorphic disorder (BDD), showing possible abnormalities in the limbic system, orbitofrontal cortex, caudate nuclei and temporal lobes. This study takes these findings forward by investigating white matter properties in BDD compared with controls using diffusion tensor imaging. It was hypothesized that the BDD sample would have widespread significantly reduced white matter connectivity as characterized by fractional anisotropy (FA). METHOD: A total of 20 participants with BDD and 20 healthy controls matched on age, gender and handedness underwent diffusion tensor imaging. FA, a measure of water diffusion within a voxel, was compared between groups on a voxel-by-voxel basis across the brain using tract-based spatial statistics within the FSL package. RESULTS: Results showed that, compared with healthy controls, BDD patients demonstrated significantly lower FA (p < 0.05) in most major white matter tracts throughout the brain, including in the superior longitudinal fasciculus, inferior fronto-occipital fasciculus and corpus callosum. Lower FA levels could be accounted for by increased radial diffusivity as characterized by eigenvalues 2 and 3. No area of higher FA was found in BDD. CONCLUSIONS: This study provided the first evidence of compromised white matter integrity within BDD patients. This suggests that there are inefficient connections between different brain areas, which may explain the cognitive and emotion regulation deficits within BDD patients.

A simple algorithm for identifying negated findings and diseases in discharge summaries.

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.

Evaluation of negation phrases in narrative clinical reports.

OBJECTIVE: Automatically identifying findings or diseases described in clinical textual reports requires determining whether clinical observations are present or absent. We evaluate the use of negation phrases and the frequency of negation in free-text clinical reports. METHODS: A simple negation algorithm was applied to ten types of clinical reports (n=42,160) dictated during July 2000. We counted how often each of 66 negation phrases was used to mark a clinical observation as absent. Physicians read a random sample of 400 sentences, and precision was calculated for the negation phrases. We measured what proportion of clinical observations were marked as absent. RESULTS: The negation algorithm was triggered by sixty negation phrases with just seven of the phrases accounting for 90% of the negations. The negation phrases received an overall precision of 97%, with "not" earning the lowest precision of 63%. Between 39% and 83% of all clinical observations were identified as absent by the negation algorithm, depending on the type of report analyzed. The most frequently used clinical observations were negated the majority of the time. CONCLUSION: Because clinical observations in textual patient records are frequently negated, identifying accurate negation phrases is important to any system processing these reports.

Intelligent aids for parallel experiment planning and macromolecular crystallization.

This paper presents a framework called Parallel Experiment Planning (PEP) that is based on an abstraction of how experiments are performed in the domain of macromolecular crystallization. The goal in this domain is to obtain a good quality crystal of a protein or other macromolecule that can be X-ray diffracted to determine three-dimensional structure. This domain presents problems encountered in real-world situations, such as a parallel and dynamic environment, insufficient resources and expensive tasks. The PEP framework comprises of two types of components: (1) an information management system for keeping track of sets of experiments, resources and costs; and (2) knowledge-based methods for providing intelligent assistance to decision-making. The significance of the developed PEP framework is three-fold--(a) the framework can be used for PEP even without one of its major intelligent aids that simulates experiments, simply by collecting real experimental data; (b) the framework with a simulator can provide intelligent assistance for experiment design by utilizing existing domain theories; and (c) the framework can help provide strategic assessment of different types of parallel experimentation plans that involve different tradeoffs.

The use of physician domain knowledge to improve the learning of rule-based models for decision-support.

This paper describes a study testing the hypothesis that the learning of a decision-support model by a computer learning algorithm from clinical data can be improved by the addition of domain knowledge from practicing physicians. The domain of the experiment is community-acquired pneumonia. The overall design of the study compares a computer learning algorithm given clinical data to one given clinical data plus domain knowledge added by physician subjects. This study showed that the performance of the computer-generated models augmented with knowledge added by physician subjects were significantly better than the computer-generated models generated without added knowledge using a two-stage rule induction algorithm in the domain of community-acquired pneumonia. This result was highly significant and shows that the addition of domain knowledge may be beneficial to the learning of clinical decision-support models, especially in domains where data is limited.

Identifying patient subgroups with simple Bayes'.

Medical records can form the basis of retrospective studies, be used to evaluate hospital practices and guidelines, and provide examples for teaching medicine. Each of these tasks presumes the ability to accurately identify patient subgroups. We describe a method for selecting patient subgroups based on the text of their medical records and demonstrate its effectiveness. We also describe a modification of the basic system that does not assume the existence of a preclassified training set, and illustrate its effectiveness in one retrieval task.

Designing computer-based frameworks that facilitate doctor--patient collaboration.

A current trend in medicine involves establishing collaborative problem solving between patients and physicians in order to involve patients more in their own care. Neither diagnosis nor therapy can be completely successful unless the patient and the doctor understand each other and collaborate with each other in an effort to gauge the other's requests, needs and concerns. This is made even more difficult by the fact that there is often a big difference between the doctors and patients in terms of expectations, vocabulary used, and other factors. For diagnosis of many disorders, a detailed description of the problem and of the patient's history are required. For therapy, patients must understand how and when to take prescribed drugs, what changes to make in diet, exercise, or lifestyle and why they are important. This paper describes a model of asynchronous collaboration between people with very different knowledge of medicine in which a computer framework attempts to mediate between patients and physicians and reduce some of the differences in communication. It allows patients to pace themselves in familiarizing themselves with the relevant domain terms, some of the medical factors underlying the conditions under question, and the justifications and implications of the prescribed treatment plan. It also allows physicians to request more information of patients and gives patients contextual information to help them understand the underlying reasons for the questions. This framework has been partially implemented in the domain of migraines. As described in the paper, not only is the system designed to cooperate with the patient, but using the system also results in better mutual understanding between the doctor and the patient, thus leading to better collaboration between them.

Using computer modeling to help identify patient subgroups in clinical data repositories.

OBJECTIVE: The ability to accurately and efficiently identify patient cases of interest in a hospital information system has many important clinical, research, educational and administrative uses. The identification of cases of interest sometimes can be difficult. This paper describes a two-stage method for searching for cases of interest. DESIGN: First, a Boolean search is performed using coded database variables. The user classifies the retrieved cases as being of interest or not. Second, based on the user-classified cases, a computer model of the patient cases of interest is constructed. The model is then used to help locate additional cases. These cases provide an augmented training set for constructing a new computer model of the cases of interest. This cycle of modeling and user classification continues until halted by the user. MEASUREMENTS: This paper describes a pilot study in which this method is used to identify the records of patients who have venous thrombosis. RESULTS: The results indicate that computer modeling enhances the identification of patient cases of interest.

Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems.

The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, ease of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.

An evaluation of machine-learning methods for predicting pneumonia mortality.

This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model's potential to assist a clinician in deciding whether to treat a given patient in the hospital or at home. We examined the error rates of the models when predicting that a given fraction of patients will survive. We examined survival fractions between 0.1 and 0.6. Over this range, each model's predictive error rate was within 1% of the error rate of every other model. When predicting that approximately 30% of the patients will survive, all the models have an error rate of less than 1.5%. The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.

Carcinogenicity predictions for a group of 30 chemicals undergoing rodent cancer bioassays based on rules derived from subchronic organ toxicities.

Rodent carcinogenicities for a group of 30 chemicals which form the subject of the Second NIEHS Predictive-Toxicology Evaluation Experiment are predicted based on their subchronic organ toxicities. Predictions are made by rules learned by the rule learning (RL) induction program.

The potential of organ specific toxicity for predicting rodent carcinogenicity.

Relationships between organ specific toxicity (specifications of the presence or absence of 43 morphological effects in 32 organs) observed from 13-week subchronic studies and rodent carcinogenicity were investigated by manually measuring the concordance of each feature and also automatically using the RL (Rule Learner) induction program. Of the 32 organs, the presence or absence of any effect in liver or kidney was found very relevant to rodent carcinogenicity. While the concordance of Salmonella genotoxicity with rodent carcinogenicity was only 60%, the battery of liver and kidney was 74% accurate with 75% sensitivity and 71% specificity. Further, using the RL program, rule sets based on organ specific toxicity together with the default predictions based on Salmonella mutagenicity were on average 80% accurate with 83% sensitivity and 82% specificity.

A temporal analysis of QMR.

OBJECTIVE: To understand better the trade-offs of not incorporating explicit time in Quick Medical Reference (QMR), a diagnostic system in the domain of general internal medicine, along the dimensions of expressive power and diagnostic accuracy. DESIGN: The study was conducted in two phases. Phase I was a descriptive analysis of the temporal abstractions incorporated in QMR's terms. Phase II was a pseudo-prospective controlled experiment, measuring the effect of history and physical examination temporal content on the diagnostic accuracy of QMR. MEASUREMENTS: For each QMR finding that would fit our operational definition of temporal finding, several parameters describing the temporal nature of the finding were assessed, the most important ones being: temporal primitives, time units, temporal uncertainty, processes, and patterns. The history, physical examination, and initial laboratory results of 105 consecutive patients admitted to the Pittsburgh University Presbyterian Hospital were analyzed for temporal content and factors that could potentially influence diagnostic accuracy (these included: rareness of primary diagnosis, case length, uncertainty, spatial/causal information, and multiple diseases). RESULTS: 776 findings were identified as temporal. The authors developed an ontology describing the terms utilized by QMR developers to express temporal knowledge. The authors classified the temporal abstractions found in QMR in 116 temporal types, 11 temporal templates, and a temporal hierarchy. The odds of QMR's making a correct diagnosis in high temporal complexity cases is 0.7 the odds when the temporal complexity is lower, but this result is not statistically significant (95% confidence interval = 0.27-1.83). CONCLUSIONS: QMR contains extensive implicit time modeling. These results support the conclusion that the abstracted encoding of time in the medical knowledge of QMR does not induce a diagnostic performance penalty.

Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals.

The results of short-term assays (induction of chromosomal aberrations and sister-chromatid exchanges, oncogenic transformations and cellular toxicity) together with MTD (maximum tolerated dose) values and physical chemical properties of non-genotoxic (i.e. Salmonella non-mutagens) carcinogens and non-carcinogens were submitted to RL, an inductive learning program. RL was able to learn rules that correctly predicted between 70 and 80% of non-genotoxic chemicals. This is a marked improvement over current predictions using only the results of short-term assays and exceeds the predictions of human experts that used the whole spectrum of acute and subchronic toxicity results as well as human knowledge and intuition.

An intelligent interactive system for delivering individualized information to patients.

This paper is a report on the first phase of a long-term, interdisciplinary project whose goal is to increase the overall effectiveness of physicians' time, and thus the quality of health care, by improving the information exchange between physicians and patients in clinical settings. We are focusing on patients with long-term and chronic conditions, initially on migraine patients, who require periodic interaction with their physicians for effective management of their condition. We are using medical informatics to focus on the information needs of patients, as well as of physicians, and to address problems of information exchange. This requires understanding patients' concerns to design an appropriate system, and using state-of-the-art artificial intelligence techniques to build an interactive explanation system. In contrast to many other knowledge-based systems, our system's design is based on empirical data on actual information needs. We used ethnographic techniques to observe explanations actually given in clinic settings, and to conduct interviews with migraine sufferers and physicians. Our system has an extensive knowledge base that contains both general medical terminology and specific knowledge about migraine, such as common trigger factors and symptoms of migraine, the common therapies, and the most common effects and side effects of those therapies. The system consists of two main components: (a) an interactive history-taking module that collects information from patients prior to each visit, builds a patient model, and summarizes the patients' status for their physicians; and (b) an intelligent explanation module that produces an interactive information sheet containing explanations in everyday language that are tailored to individual patients, and responds intelligently to follow-up questions about topics covered in the information sheet.

Building a medical multimedia database system to integrate clinical information: an application of high-performance computing and communications technology.

The rapid growth of diagnostic-imaging technologies over the past two decades has dramatically increased the amount of nontextual data generated in clinical medicine. The architecture of traditional, text-oriented, clinical information systems has made the integration of digitized clinical images with the patient record problematic. Systems for the classification, retrieval, and integration of clinical images are in their infancy. Recent advances in high-performance computing, imaging, and networking technology now make it technologically and economically feasible to develop an integrated, multimedia, electronic patient record. As part of The National Library of Medicine's Biomedical Applications of High-Performance Computing and Communications program, we plan to develop Image Engine, a prototype microcomputer-based system for the storage, retrieval, integration, and sharing of a wide range of clinically important digital images. Images stored in the Image Engine database will be indexed and organized using the Unified Medical Language System Metathesaurus and will be dynamically linked to data in a text-based, clinical information system. We will evaluate Image Engine by initially implementing it in three clinical domains (oncology, gastroenterology, and clinical pathology) at the University of Pittsburgh Medical Center.

The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.

Cost-effective health care is at the forefront of today's important health-related issues. A research team at the University of Pittsburgh has been interested in lowering the cost of medical care by attempting to define a subset of patients with community-acquire pneumonia for whom outpatient therapy is appropriate and safe. Sensitivity and specificity requirements for this domain make it difficult to use rule-based learning algorithms with standard measures of performance based on accuracy. This paper describes the use of misclassification costs to assist a rule-based machine-learning program in deriving a decision-support aid for choosing outpatient therapy for patients with community-acquired pneumonia.

Image engine: an integrated multimedia clinical information system.

Image Engine is a microcomputer-based system for the integration, storage, retrieval, and sharing of digitized clinical images. The system seeks to address the problem of integrating a wide range of clinically important images with the text-based electronic patient record. Rather than create a single, integrated database system for all clinical data, we are developing a separate image database system that creates real-time, dynamic links to other network-based clinical databases. To the user, this system will present an integrated multimedia representation of the patient record, providing access to both the image and text-based data required for effective clinical decision making.

Temporal reasoning abstractions in QMR.

A medical decision-support system (MDSS) employs either explicit or implicit temporal representation and reasoning (TRR). In this paper we first examine the factors that make explicit TRR necessary. We argue that for diagnostic MDSSs in large domains, such as internal medicine, implicit TRR is often sufficient for acceptable diagnostic performance. A necessary prerequisite for implementing implicit TRR is the identification of a set of proper TRR abstractions. We analyze the implicit TRR utilized in QMR, a MDSS operating in the domain of general internal medicine, and describe three classes of TRR abstractions. We discuss our findings in relation to work on temporal reasoning in medical informatics.