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Biomedical subjects

S Miksch

Publications and source records attributed to S Miksch.

11 recordsLinked to original sources
PubMed2001

Knowledge-based verification of clinical guidelines by detection of anomalies.

As shown in numerous studies, a significant part of published clinical guidelines is tainted with different types of semantical errors that interfere with their practical application. The adaptation of generic guidelines, necessitated by circumstances such as resource limitations within the applying organization or unexpected events arising in the course of patient care, further promotes the introduction of defects. Still, most current approaches for the automation of clinical guidelines are lacking mechanisms, which check the overall correctness of their output. In the domain of software engineering in general and in the domain of knowledge-based systems (KBS) in particular, a common strategy to examine a system for potential defects consists in its verification. The focus of this work is to present an approach, which helps to ensure the semantical correctness of clinical guidelines in a three-step process. We use a particular guideline specification language called Asbru to demonstrate our verification mechanism. A scenario-based evaluation of our method is provided based on a guideline for the artificial ventilation of newborn infants. The described approach is kept sufficiently general in order to allow its application to several other guideline representation formats.

Artificial Intelligence↗
PubMed2001

Metaphors of movement: a visualization and user interface for time-oriented, skeletal plans.

Therapy planning plays an increasingly important role in the everyday work of physicians. Clinical protocols or guidelines are typically represented using flow-charts, decision tables, or plain text. These representations are badly suited, however, for complex medical procedures.One representation method that overcomes these problems is the language Asbru. But because Asbru has a LISP-like syntax (and also incorporates many concepts from computer science), it is not suitable for physicians.Therefore, we developed a visualization and user interface to deal with treatment plans expressed in Asbru. We use graphical metaphors to make the underlying concepts easier to grasp, employ glyphs to communicate complex temporal information and colors to make it possible to understand the connection between the two views (Topological View and Temporal View) available in the system. In this paper, we present the design ideas behind AsbruView, and discuss its usefulness based on the results of a usability study we performed with six physicians.

Decision Making, Computer-Assisted↗
PubMed2001

Visualizing complex notions of time.

Time plays an important role in medicine. Conditions are not just evaluated at single instances in time, but traced over periods. Medications must be administered within specified temporal limits, and their effects observed with regard to time. When planning treatments, the temporal aspect becomes even more complicated. The planner has to deal with uncertainty and allowable intervals. A visual representation of the information would be helpful, but there are few visualizations of time that are powerful enough. We present a visualization that graphically represents a complex notion of time, and has also been implemented in a program that allows users to directly specify this information. The results of a small user study are reported.

Clinical Protocols↗
PubMed2001

Combining diagnosis and treatment using Asbru.

Traditionally, diagnosis and treatment have been seen as two distinct tasks. Consequently, most approaches to computer supported health care focus on one of the two - mostly on diagnosis or rather on the interpretation of measurements which is much better understood and formalized. However, in practice diagnosis and treatment overlap and influence each other in many ways. Combinations range from repeatedly going through the diagnosis-treatment loop over a period of time to permanent monitoring of the patients' health condition as it is done in intensive care units. In this paper we describe how to model these combinations using the clinical protocol-representation language Asbru. It implements treatment steps in a hierarchy of skeletal, time-oriented plans. Diagnosis can either be described in a declarative way in the conditions, under which treatment steps are taken or it can be modelled explicitly as plans of their own right. We demonstrate our approach using examples taken from the American Association of Paediatricians' guideline for the treatment of hyperbilirubinemia in the new-born.

Decision Making, Computer-Assisted↗
PubMed1997

Effective data validation of high-frequency data: time-point-, time-interval-, and trend-based methods.

Real-time systems for monitoring and therapy planning, which receive their data from on-line monitoring equipment and computer-based patient records, require reliable data. Data validation has to utilize and combine a set of fast methods to detect, eliminate, and repair faulty data, which may lead to life-threatening conclusions. The strength of data validation results from the combination of numerical and knowledge-based methods applied to both continuously-assessed high-frequency data and discontinuously-assessed data. Dealing with high-frequency data, examining single measurements is not sufficient. It is essential to take into account the behavior of parameters over time. We present time-point-, time-interval-, and trend-based methods for validation and repair. These are complemented by time-independent methods for determining an overall reliability of measurements. The data validation benefits from the temporal data-abstraction process, which provides automatically derived qualitative values and patterns. The temporal abstraction is oriented on a context-sensitive and expectation-guided principle. Additional knowledge derived from domain experts forms an essential part for all of these methods. The methods are applied in the field of artificial ventilation of newborn infants. Examples from the real-time monitoring and therapy-planning system VIE-VENT illustrate the usefulness and effectiveness of the methods.

Artificial Intelligence↗
PubMed1996

Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants.

Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising information management problem at neonatal ICUs (NICUs). We developed methods for data validation and therapy planning which incorporate knowledge about point and interval data, as well as expected qualitative trend descriptions to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our methods are based on schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to different degrees of parameters' abnormalities as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable. We show their applicability in detecting anomalous system behavior early, in recommending therapeutic actions, and in assessing the effectiveness of these actions within a certain period. We implemented our methods in VIE-VENT, an open-loop knowledge-based monitoring and therapy planning system for artificially ventilated newborn infants. The applicability and usefulness of our approach are illustrated by examples of VIE-VENT. Finally, we present our first experiences with using VIE-VENT in a real clinical setting.

Algorithms↗
PubMed1996

The patient advocate: a cooperative agent to support patient-centered needs and demands.

Knowledge-based monitoring and therapy planning systems were mainly built for the convenience of health care providers. They neglected the consumers of health care, namely, the patients. Our approach is concentrated on the individual patients' demands and needs. We are designing, building, and demonstrating a cooperative agent to support patients' management of their own health-related behavior on a day-to-day basis at home. Clinical treatment protocols are represented in an intention-based time-oriented representation language to overcome the drawbacks of vague or ill-structured problem definitions (e.g., missing functional dependencies). These representations are used to guide the patients, to provide necessary explanations, and to observe and critique whether the patients obey the instructions of the health-care providers. We will present a prototype which supports women with gestational diabetes mellitus.

Ambulatory Care↗
PubMed1996

An intention-based language for representing clinical guidelines.

Automated support for guideline-based care would be enhanced considerably by a standard representation of clinical guidelines. To faciliate use and reuse, we suggest a representation that includes the explicit intentions of the guideline's author. These intentions include the desirable actions of the care provider and the patient states to be achieved before, during, and after the administration of the guideline. Intentions are temporal patterns of provider actions or patient states to be maintained, achieved, or avoided. We view automated support as a collaborative effort of the health-care provider and an automated assistant and involves several different tasks. We defined the syntax and, the semantics of a text-based language (ASBRU) for representation and annotation of clinical guidelines. The language supports maintenance of the automated assistant's knowledge base and could improve the quality and flexibility of the automated assistant's recommendations. In the ASGAARD project, we are developing reasoning mechanisms that use the ASBRU language for execution and critiquing tasks in conjunction with online electronic patient medical records.

Expert Systems↗
PubMed1995

[VIE-PNN: an expert system for calculating parenteral nutrition of intensive care premature and newborn infants].

Daily renewed composition of parenteral nutrition for premature and full-term newborn infants in intensive care is time consuming and prone to inherent calculation errors. We developed a knowledge based system, VIE-PNN (Vienna Expert System for Calculating Parenteral Nutrition of Neonates) for calculating the proposed composition of parenteral nutrition on the basis of the calculating algorithm used at our neonatal intensive care unit. The system needs manual input on postnatal age, body weight, serum electrolytes (or normal values if not available), amount and content of additional oral feeds, venous access (peripheral or central), total amount of fluid intake, and complications such as sepsis (reduced lipid supply) or cholestasis (reduced amino acid supply). The parenteral nutrition proposal may interactively be modified by the attending physician. There are possibilities for error detection to reduce the probability of typing or calculation errors. The system was developed to run on IBM compatible PCs and has been tested clinically. We describe the problem domain, system structure, clinical evaluation of VIE-PNN and the calculation of a standard parenteral nutrition solution from the data stored in the system's database.

Algorithms↗
PubMedDate not supplied

The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines.

Clinical guidelines can be viewed as generic skeletal-plan schemata that represent clinical procedural knowledge and that are instantiated and refined dynamically by care providers over significant time periods. In the Asgaard project, we are investigating a set of tasks that support the application of clinical guidelines by a care provider other than the guideline's designer. We are focusing on the application of the guideline, recognition of care providers' intentions from their actions, and critique of care providers' actions given the guideline and the patient's medical record. We are developing methods that perform these tasks in multiple clinical domains, given an instance of a properly represented clinical guideline and an electronic medical patient record. In this paper, we point out the precise domain-specific knowledge required by each method, such as the explicit intentions of the guideline designer (represented as temporal patterns to be achieved or avoided). We present a machine-readable language, called Asbru, to represent and to annotate guidelines based on the task-specific ontology. We also introduce an automated tool for the acquisition of clinical guidelines based on the same ontology, developed using the PROTEGE-II framework.

Artificial Intelligence↗