PubMed Health⌕ Search

Biomedical subjects

B G Buchanan

Publications and source records attributed to B G Buchanan.

At least 19 recordsLinked to original sources
PubMed1995

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.

Animals↗
PubMed1995

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.

Anthropology, Cultural↗
PubMed1995

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.

Abstracting and Indexing↗
PubMed1995

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.

Algorithms↗
PubMed1995

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.

Abstracting and Indexing↗
PubMed1995

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.

Animals↗
PubMed1994

Induction of rules for biological macromolecule crystallization.

X-ray crystallography is the method of choice for determining the 3-D structure of large macromolecules at a high enough resolution. The rate limiting step in structure determination is the crystallization itself. It takes anywhere between a few weeks to several years to obtain macromolecular crystals that yield good diffraction patterns. The theory of forces that promote and maintain crystal growth is preliminary, and crystallographers systematically search a large parameter space of experimental settings to grow good crystals. There is a wealth of experimental data on crystal growth most of which is in paper laboratory notebooks. Some of the data has been gathered in electronic form, e.g., the Biological Macromolecular Crystallization Database (BMCD) which is a repository of successful experimental conditions for growing over 800 different macromolecules (Gilliland 1987). Crystallographers are in need of computational tools to gather and analyze past data to design new crystal growth trails. We are building the Crystallographer's Assistant (CA) to help crystallographers record and maintain experimental context in electronic form, offer suggestions on experimental conditions that are likely to be successful, and provide explanations for failed experiments. As an initial step in this project, we have applied RL, an inductive learning program, to the BMCD. In this paper we report initial experiments and findings in applying RL to the BMCD. From the point of view of crystallography, we have discovered possibly significant new empirical relationships in crystal growth. From the point of view of machine learning, our work suggests refinements of existing methods for incorporating detailed domain knowledge into inductive analysis techniques.

Animals↗
PubMed1993

Protein secondary structure prediction using two-level case-based reasoning.

We have developed a two-level case-based reasoning architecture for predicting protein secondary structure. The central idea is to break the problem into two levels: first, reasoning at the object (protein) level, and using the global information from this level to focus on a more restricted problem space; second, decomposing objects into pieces (segments), and reasoning at the internal structures level; finally, synthesizing the pieces back to the objects. The architecture has been implemented and tested on a commonly used data set with 69.3% predictive accuracy. It was then tested on a new data set with 67.3% accuracy. Additional experiments were conducted to determine the effects of using different similarity matrices.

Amino Acid Sequence↗
PubMed1992

Expanding the concept of medical information: an observational study of physicians' information needs.

Obtaining and managing clinically relevant information constitutes a major problem for physicians, for which the development of automated tools is often proposed as a solution. However, designing and implementing appropriate automated solutions presumes knowledge of physicians' information needs. We describe an empirical study of information needs in four clinical settings in internal medicine in a university teaching hospital. In contrast to the retrospective data often used in previous studies, this research used ethnographic techniques to facilitate direct observation of communication about information needs. On the basis of this experience, we address two main issues: how to identify and interpret expressions of information needs in medicine and how to broaden our conception of "information needs" to account for the empirical data.

Computer Systems↗
PubMed1992

Involving patients in health care: explanation in the clinical setting.

The long-term goal of our research is to improve the overall effectiveness of physicians' time, by improving the information exchange between physicians and chronic-care patients, initially migraine patients. The computer system we are constructing has a partial knowledge base about migraines, common therapies, and common side effects of those therapies. The system consists of two main programs: data collection and explanation. The design of our system is based on empirical data concerning patients' information needs.

Humans↗
PubMed1991

Physicians' information needs: analysis of questions posed during clinical teaching.

OBJECTIVE: To describe information requests expressed during clinical teaching. SETTING: Residents' work rounds, attending rounds, morning report, and interns' clinic in a university-based general medicine service. SUBJECTS: Attending physicians, medical house staff, and medical students in a general medicine training program. METHODS: An anthropologist observed communication among study subjects and recorded in field notes expressions of a need for information. We developed a coding scheme for describing information requests and applied the coding scheme to the data recorded. Based on assigned codes, we created a subset of strictly clinical requests. MEASUREMENTS: Five hundred nineteen information requests recorded during 17 hours of observed clinical activity were selected for detailed analysis. These requests related to the care of approximately 90 patients by 24 physicians and medical students. Sixty-five requests were excluded because they were not strictly clinical, leaving a subset of 454 clinical questions for analysis. MAIN RESULTS: On average, five clinical questions were raised for each patient discussed. Three hundred thirty-seven requests (74%) concerned patient care. Of these 337 questions, 175 (52%) requested a fact that could have been found in a medical record. Seventy-seven (23%) of these questions, motivated by the needs of patient care, were potentially answerable by a library, a textbook, a journal, or MEDLINE. Eighty-eight (26%) of the questions asked for patient care required synthesis of patient information and medical knowledge. CONCLUSIONS: Clinicians in the study settings requested information frequently. Many of these information needs required the synthesis of patient information and medical knowledge and thus were potentially difficult to satisfy. A typology is proposed that characterizes information needs as consciously recognized, unrecognized, and currently satisfied.

Education, Medical↗
PubMed1991

Broadening our approach to evaluating medical information systems.

Evaluation in medical informatics tends to follow the paradigm of controlled clinical trials. This model carries with it a number of assumptions whose implications for medical informatics deserve examination. In this paper, we describe the conventional wisdom on evaluation, pointing out some of its underlying assumptions and suggesting that these assumptions are problematic when applied to some aspects of evaluation. In particular, we believe that these assumptions contribute to the problem of user acceptance. We then suggest a broader approach to evaluation, offering some conceptual and methodological distinctions that we believe will be of use to the medical informatics community in rethinking this issue.

Bias↗
PubMed1988

Heuristic refinement method for the derivation of protein solution structures: validation on cytochrome b562.

A method is described for determining the family of protein structures compatible with solution data obtained primarily from nuclear magnetic resonance (NMR) spectroscopy. Starting with all possible conformations, the method systematically excludes conformations until the remaining structures are only those compatible with the data. The apparent computational intractability of this approach is reduced by assembling the protein in pieces, by considering the protein at several levels of abstraction, by utilizing constraint satisfaction methods to consider only a few atoms at a time, and by utilizing artificial intelligence methods of heuristic control to decide which actions will exclude the most conformations. Example results are presented for simulated NMR data from the known crystal structure of cytochrome b562 (103 residues). For 10 sample backbones an average root-mean-square deviation from the crystal of 4.1 A was found for all alpha-carbon atoms and 2.8 A for helix alpha-carbons alone. The 10 backbones define the family of all structures compatible with the data and provide nearly correct starting structures for adjustment by any of the current structure determination methods.

Computer Systems↗
PubMed1987

Validation of the first step of the heuristic refinement method for the derivation of solution structures of proteins from NMR data.

A new method for the analysis of NMR data in terms of the solution structure of proteins has been developed. The method consists of two steps: first a systematic search of the conformational space to define the region allowed by the initial set of experimental constraints, and second, the narrowing of this region by the introduction of additional constraints and optional refinement procedures. The search of the conformational space is guided by heuristics to make it computationally feasible. The method is therefore called the heuristic refinement method and is coded in an expert system called PROTEAN. The paper describes the validation of the first step of the method using an artificial NMR data set generated from the known crystal structure of sperm whale carbon monoxymyoglobin. It is shown that the initial search procedure yields a low-resolution structure of the myoglobin molecule, accurately reproducing its main topological features, and that the precision of the structure depends on the quality of the initial data set.

Expert Systems↗
PubMed1984

Computer-assisted decision making in medicine.

This article reviews the strengths and limitations of five major paradigms of medical computer-assisted decision making (CADM): (1) clinical algorithms, (2) statistical analysis of collections of patient data, (3) mathematical models of physical processes, (4) decision analysis, and (5) symbolic reasoning or artificial intelligence (AI). No one technique is best for all applications, and there is recent promising work which combines two or more established techniques. We emphasize both the inherent power of symbolic reasoning and the promise of artificial intelligence and the other techniques to complement each other.

Computers↗
PubMed1979

Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts.

An evaluation of a computer-based consultation system called MYCIN was made. Eight independent evaluators with special expertise in the management of meningitis compared MYCIN's choice of antimicrobials with the choices of nine human prescribers for ten test cases of meningitis. MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%. The system never failed to cover a treatable pathogen while demonstrating efficiency in minimizing the number of antimicrobials prescribed. The study design may be useful in assessing the performance of other computer-based clinical decision-making systems.

Adult↗
PubMed1979

Evaluating the performance of a computer-based consultant.

The performance of a computer-based clinical consultation system is evaluated. The program, called MYCIN, is designed to function as an aid for infectious disease diagnosis and therapy selection, with an initial emphasis on bacteremias. The evaluation methodology is discussed, as well as the difficulties encountered in attempting to evaluate clinical judgments. Specialists in infectious diseases judged MYCIN's final therapy recommedation, and intermediate conclusions about the significance of the infection and identity of infecting organisms. The evaluation techniques described may be useful in assessing the performance of other clinical decision aids. Results of the evaluation show that the program's therapy recommedations meet Stanford experts' standards of acceptable practice 90.9% of the time (table 2), with some variation noted both among individual experts and between Stanford experts and others (tables 1, 2).

Animals↗