Biomedical subjects

M G Kahn

Publications and source records attributed to M G Kahn.

7 recordsLinked to original sources

PubMed1992

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them, and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making trade-offs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such trade-offs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preference of the physician prescribing the treatment. A figure of merit is computed for each tentative plan and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Decision Making, Computer-Assisted↗

PubMed1991

TQuery: a context-sensitive temporal query language.

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.

Databases, Factual↗

PubMed1991

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making tradeoffs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such tradeoffs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preferences of the physician prescribing the treatment. A figure of merit is computed for each tentative plan, and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Artificial Intelligence↗

PubMed1991

Combining physiologic models and symbolic methods to interpret time-varying patient data.

This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.

Adult↗

PubMed1991

Extensions to the time-oriented database model to support temporal reasoning in medical expert systems.

Physicians faced with diagnostic and therapeutic decisions must reason about clinical features that change over time. Database-management systems (DBMS) can increase access to patient data, but most systems are limited in their ability to store and retrieve complex temporal information. The Time-Oriented Databank (TOD) model, the most widely used data model for medical database systems, associates a single time stamp with each observation. The proper analysis of most clinical data requires accounting for multiple concurrent clinical events that may alter the interpretation of the raw data. Most medical DBMSs cannot retrieve patient data indexed by multiple clinical events. We describe two logical extensions to TOD-based databases that solve a set of temporal reasoning problems we encountered in constructing medical expert systems. A key feature of both extensions is that stored data are partitioned into groupings, such as sequential clinical visits, clinical exacerbations, or other abstract events that have clinical decision-making relevance. The temporal network (TNET) is an object-oriented database that extends the temporal reasoning capabilities of ONCOCIN, a medical expert system that provides chemotherapy advice. TNET uses persistent objects to associate observations with intervals of time during which "an event of clinical interest" occurred. A second object-oriented system called the extended temporal network (ETNET), is both an extension and a simplification of TNET. Like TNET, ETNET uses persistent objects to represent relevant intervals; unlike the first system, however, ETNET contains reasoning methods (rules) that can be executed when an event "begins", and that are withdrawn when that event "concludes". TNET and ETNET capture temporal relationships among recorded information that are not represented in TOD-based databases. Although they do not solve all temporal reasoning problems found in medical decision making, these new structures enable patient database systems to encode complex temporal relationships, to store and retrieve patient data based on multiple clinical contexts and, in ETNET, to modify the reasoning methods available to an expert system based on the onset or conclusion of specific clinical events.

Diagnosis, Computer-Assisted↗

PubMed1989

A psychophysical comparison of two methods for adaptive histogram equalization.

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

Contrast Media↗

PubMedDate not supplied

Modeling time in medical decision-support programs.

To derive meaningful conclusions in a changing medical setting, medical decision-support systems must represent and reason about the temporal nature of the clinical environments they attempt to model. Because all difficult medical problems have significant temporal features, designers of medical decision support systems must recognize the unique problems caused by representing and reasoning with temporal concepts. This report has three goals: 1) to describe a set of fundamental issues in creating and reasoning with computer models of a changing clinical environment, 2) to present a taxonomy for characterizing the temporal characteristics of computer models of temporal reasoning, and 3) to use this taxonomy to compare the models of time used in some implemented medical decision-support programs. From this examination, it is argued that computational models of time based on a single uniform representational or inferential method are limited by the expressive power of that method. Multiple modeling formalisms that express different temporal properties of the domain task and that work cooperatively are required to capture the subtlety and diversity of temporal features used in expert clinical problem solving. As an example of this approach, the author describes a program called TOPAZ that contains two temporal models that represent different temporal features of the clinical domain.

Classification↗