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Biomedical subjects

C E Kahn

Publications and source records attributed to C E Kahn.

At least 19 recordsLinked to original sources
PubMed1996

Positive predictive value of clinical suspicion for abdominal aortic aneurysm. Implications for use of ultrasonography.

To measure the positive predictive value (PPV) of clinical suspicion of abdominal aortic aneurysm (AAA), as confirmed by ultrasonography, we reviewed the records of 343 patients at a university medical center referred to ultrasonography for newly suspected AAA. Positive predictive value was 11.1% for large aneurysms of at least 5.0 cm and 18.7% for aneurysms of at least 3.5 cm, and was higher for men and older patients. For patients under 50 years of age, PPV was only 2.6%. Ultrasonography for clinically suspected AAA has a low positive predictive yield, particularly for men under age 50 and for women.

Adult↗
PubMed1996

Structured entry of radiology reports using World Wide Web technology.

Structured data entry--in which information is entered by using predetermined data elements and formats--has the potential to improve the radiology reporting process. The dependence on particular computer hardware and software platforms has posed a barrier to wider use of this approach. The World Wide Web (WWW), a client-server protocol for delivery of multimedia data via the Internet, was used to achieve platform-independent structured entry of radiology reports. A developmental system for structured entry of radiology reports, called SPIDER, incorporates a knowledge base of hierarchically organized concepts, a WWW server, and two specialized programs. The WebForm program transforms the system's knowledge into graphical WWW data-entry forms; the WebReport program converts data entered on these forms into outline-format reports. SPIDER received favorable evaluations from sonographers and physicians who used the system to record the results of several test cases. WWW technology can be used to achieve platform-independent entry of the results of radiologic procedures.

Artificial Intelligence↗
PubMed1996

Knowing the score: using predictive scoring systems in clinical practice.

Outcome scores have been promoted as adjuncts to clinical decision making, especially when further care is thought to be futile. The Pediatric Risk of Mortality score is used to calculate the risk of mortality for patients admitted to pediatric intensive care units. In this article the Pediatric Risk of Mortality score in evaluated for its ability to contribute to individual patient care decisions in the context of clinical practice. Through analysis several features of the Pediatric Risk of Mortality score were identified that require discretion if the score is to be used in decisions involving individual patients. These features include variability and bias introduced in data collection and data presentation. Also, outcome scores do not allow for the incorporation of patient and family values into the decision process. Outcome scores can provide some adjunctive information to clinicians, but they should be used with caution when making patient care decisions. Use of Pediatric Risk of Mortality scores in clinical practice must be tempered with a knowledge of the limitations of the scores, individual patient variability, the conditions under which the scores have been validated and collected and, most importantly, an awareness that outcome scores do not take into account the caregiver and patient values that are inherent in any treatment decision.

Child↗
PubMed1996

Decision aids in radiology.

Computer systems can help radiologists decide which tests to perform and which diagnoses to consider. Repositories of clinical data and general medical knowledge can provide information on demand for decision-making tasks; many of these information resources are available remotely via the Internet. Decision support systems can incorporate the techniques of artificial intelligence to apply their general knowledge to the features of a particular patient. Successful use of these technologies requires careful attention to design, implementation, and rigorous evaluation. Computer-based decision aids can improve the cost effectiveness and diagnostic accuracy of radiologic practice and are poised to play an important role in the future of radiology.

Decision Support Techniques↗
PubMed1996

Health status assessment via the World Wide Web.

We explored the use of the World Wide Web to collect health status information for medical outcomes research. The RAND 36-Item Health Survey 1.0 (RAND-36), which contains the 36 multiple-choice questions of the Medical Outcomes Study SF-36 "Short Form" and differs only in its simplified scoring scheme, was made available for anonymous use on the Internet. Participation in the survey was invited through health-related Internet news groups and mailing lists. Participants entered data and received, their scores using the World Wide Web protocol. Entries were recorded from 15 June 1995 to 14 June 1996 (1 year). The survey was completed anonymously by 4876 individuals with access to the World Wide Web. Two-thirds completed the survey within 5 minutes, and 97% did so within 10 minutes. The item-completion rate was 99.28%. Values of Cronbach's alpha of 0.76 to 0.90 for the scoring scales matched the high reliability found in the Medical Outcomes Study. The World Wide Web provides a method of rapidly measuring individual health status and may play an important role in advancing health services research and outcomes-based patient care.

Computer Communication Networks↗
PubMed1996

Knowledge representation for platform-independent structured reporting.

Structured reporting systems allow health care providers to record observations using predetermined data elements and formats. We present a generalized language, based on the Standard Generalized Markup Language (SGML), for platform-independent structured reporting. DRML (Data-entry and Report Markup Language) specifies hierarchically organized concepts to be included in data-entry forms and reports. DRML documents serve as the knowledge base for SPIDER, a reporting system that uses the World Wide Web as its data-entry medium. SPIDER generates platform-independent documents that incorporate familiar data-entry objects such as text windows, checkboxes, and radio buttons. From the data entered on these forms, SPIDER uses its knowledge base to generate outline-format textual reports, and creates datasets for analysis of aggregate results. DRML allows knowledge engineers to design a wide variety of clinical reports and survey instruments.

Computer Communication Networks↗
PubMed1995

CHORUS: a computer-based radiology handbook for international collaboration via the World Wide Web.

To facilitate collaboration among physicians, a computer-based radiology handbook was developed and published electronically via the World Wide Web on the Internet. This system, called CHORUS (Collaborative Hypertext of Radiology), allows physicians without computer expertise to read documents, contribute knowledge, and critically review the handbook's content by using a simple, graphical user interface from virtually any type of computer system. CHORUS contains 1,168 "note-card" documents that describe radiologic findings; differential diagnoses; technical information; and pertinent anatomy, pathology, and physiology. Documents are indexed by title and by organ system and are linked to related documents. Data entry forms allow physicians to comment on published documents, submit new documents, and review submitted documents. CHORUS uses public-domain technologies to present useful, easily accessible knowledge for education and clinical decision making, and it provides a medium for international medical collaboration via the Internet.

Computer Communication Networks↗
PubMed1995

Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.

Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional-probability data, such as sensitivity and specificity, were derived from peer-reviewed journal articles and from expert opinion. In testing with a set of 77 cases from a mammography atlas and a clinical teaching file, MammoNet performed well in distinguishing between benign and malignant lesions, and yielded a value of 0.881 (+/- 0.045) for the area under the receiver operating characteristic curve. We conclude that Bayesian networks provide a potentially useful tool for mammographic decision support.

Bayes Theorem↗
PubMed1995

Decision-theoretic refinement planning: a new method for clinical decision analysis.

Clinical decision analysis seeks to identify the optimal management strategy by modelling the uncertainty and risks entailed in the diagnosis, natural history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines the techniques of decision theory and artificial intelligence. Our model uses a modular representation of knowledge that simplifies model building and enables more fully automated decision making. Moreover, the model exploits problem structures to yield better computational efficiency. As an example we apply our techniques to the problem of management of acute deep venous thrombosis.

Artificial Intelligence↗
PubMed1995

Optimizing, diagnostic, and therapeutic strategies using decision-theoretic planning: principles and applications.

OBJECTIVE: Decision-theoretic planning is a new technique for selecting optimal actions. The authors sought to determine whether decision-theoretic planning could be applied to medical decision making to identify optimal strategies for diagnosis and therapy. METHODS: An existing model of acute deep venous thrombosis (DVT) of the lower extremities--in which 24 management strategies were compared--was converted into a set of conditional-probabilistic actions for use by the DRIPS decision-theoretic planning system. Actions were grouped into an abstraction/decomposition hierarchy. A utility function was defined in accordance with the existing DVT management model to incorporate the costs and risks of the diagnostic tests and treatments. RESULTS: From 18 primitive actions (such as "perform venography" and "treat if venography shows thigh DVT"), a total of 312 possible concrete plans were encoded within the abstraction/decomposition hierarchy. The DRIPS planning system used abstraction techniques to eliminate 136 possible plans (44%) from consideration. It determined that, given the parameters specified, the most cost-effective management strategy was "no tests, no treatment." This result differed from the published result of "perform ultrasonography, treat if positive." In reviewing the original article, it was determined that DRIPS had revealed an error in the manually constructed decision trees used in that manuscript. At values of $75,000 and greater for the cost of death, the optimal strategy became "impedance plethysmography (IPG), don't wait, perform venography if IPG is positive, and treat only if venography shows thigh DVT." CONCLUSION: Decision-theoretic planning is applicable to medical decision making and may be an extremely useful technique for complex decisions. The use of inheritance abstraction makes the technique computationally tractable for complex planning problems, and the modular nature of the data entry may help eliminate errors that appear in manually encoded decision trees.

Computer Simulation↗
PubMed1994

Case-based reasoning and imaging procedure selection.

RATIONALE AND OBJECTIVES: Case-based reasoning, an artificial intelligence technique for learning and reasoning from experience, has shown great potential for use in decision support systems. The authors developed and tested a prototype case-based decision support system to explore the applicability of this technique to the selection of diagnostic imaging procedures. METHODS: A case-based system, ProtoISIS, was developed based on the Protos learning apprentice. ProtoISIS learned the domain of ultrasonography and body computed tomography by reviewing 200 consecutive cases of actual requests for imaging procedures. ProtoISIS was tested by using it to classify four sets of 25 cases of actual imaging procedure requests. RESULTS: ProtoISIS correctly classified 72% of the imaging-procedure requests. Its performance improved as it gained experience: in the last two test series, it correctly classified 84% of the cases presented. CONCLUSIONS: Case-based reasoning can be applied successfully to the selection of diagnostic imaging procedures and holds potential for use in clinical decision support aids. Further work is necessary to realize a clinically useful system.

Artificial Intelligence↗
PubMed1994

A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease.

Bayesian networks, a technique for reasoning under uncertainty, currently are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, laboratory test results, and imaging study findings. The connections between nodes incorporate conditional probabilities, such as sensitivity and specificity, to represent probabilistic influences. Statistical data were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network successfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a posteriori probabilities. The most informative examination was identified, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very promising technique for decision support in radiology: they can assist physicians in formulating diagnoses and in selecting imaging procedures.

Adult↗
PubMed1994

Artificial intelligence in radiology: decision support systems.

Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.

Artificial Intelligence↗
PubMed1994

Hepatic helical CT: contrast material injection protocol.

PURPOSE: To develop and compare contrast material injection protocols suitable for hepatic helical computed tomography (CT). MATERIALS AND METHODS: Monophasic and biphasic helical CT were performed with contrast material with an iodine load of 50 g at 3 mL/sec for 60 seconds or at 5 mL/sec for 10 seconds and 2 mL/sec for 65 seconds, respectively. In 58 men and 51 women, aged 22-77 years, aortic and hepatic enhancement curves were constructed from a cluster acquisition with a slip-ring scanner operating in a nonhelical mode. RESULTS: The monophasic protocol produced a higher peak aortic enhancement (180 HU +/- 47 [+/- 1 standard deviation]) than the biphasic protocol (150 HU +/- 24). Peak hepatic enhancement (63-64 HU +/- 15) was equivalent. Calculated equilibrium time for the monophasic protocol was 95.1 seconds and for the biphasic protocol was 101.4 seconds. The contrast enhancement index differed only marginally between the two protocols (P < .4). CONCLUSION: Monophasic and biphasic protocols produced equivalent results when tailored for the shorter temporal window of a rapid-sequence helical acquisition.

Adult↗
PubMed1994

Generating explanations and tutorial problems from Bayesian networks.

We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.

Algorithms↗
PubMed1994

Planning diagnostic imaging work-up strategies using case-based reasoning.

ISIS is a developmental decision support system that helps physicians select diagnostic imaging procedures. It uses case-based reasoning, an artificial-intelligence approach that emphasizes reasoning and planning from prior experience. The development, training, and evaluation of a prototype system were used to guide the development of ISIS. To realize a clinically useful system, particular emphasis has been placed on increasing the depth and breadth of case-based knowledge, enhancing the explanatory capabilities of the system, and refining the human-computer interfaces to include a critiquing approach.

Algorithms↗
PubMed1993

Graphical knowledge presentation in a MUMPS-based decision-support system.

Conventional knowledge-based medical expert systems present information and elicit responses in the form of text. PHOENIX, a decision-support system designed to help non-radiologist physicians select diagnostic imaging procedures, offers a graphical user interface. The system constructs and displays algorithms, or flowcharts, from the rules in its knowledge base. Users can view the flowcharts and move through them by answering questions at branch points. The system provides detailed textual explanations of the rules and descriptions of the imaging procedures. The system is written in MUMPS, a common programming language for biomedical information systems, and is easily incorporated into clinical computer systems.

Algorithms↗