Alessio Bottrighi

Adopting model checking techniques for clinical guidelines verification

OBJECTIVES Clinical guidelines (GLs) are assuming a major role in the medical area, in order to grant the quality of the medical assistance and to optimize medical treatments within healthcare organizations. The verification of properties of the GL (e.g., the verification of GL correctness with respect to several criteria) is a demanding task, which may be enhanced through the adoption of advanced Artificial Intelligence techniques. In this paper, we propose a general and flexible approach to address such a task. METHODS AND MATERIALS Our approach to GL verification is based on the integration of a computerized GL management system with a model-checker. We propose a general methodology, and we instantiate it by loosely coupling GLARE, our system for acquiring, representing and executing GLs, with the model-checker SPIN. RESULTS We have carried out an in-depth analysis of the types of properties that can be effectively verified using our approach, and we have completed an overview of the usefulness of the verification task at the different stages of the GL life-cycle. In particular, experimentation on a GL for ischemic stroke has shown that the automatic verification of properties in the model checking approach is able to discover inconsistencies in the GL that cannot be detected in advance by hand. CONCLUSION Our approach thus represents a further step in the direction of general and flexible automated GL verification, which also meets usability requirements.

Applying Artificial Intelligence to Clinical Guidelines: The GLARE Approach.

In this paper, we present GLARE, a domain-independent system for acquiring, representing and executing clinical guidelines. GLARE is characterized by the adoption of Artificial Intelligence (AI) techniques at different levels in the definition and implementation of the system. First of all, a high-level and user-friendly knowledge representation language has been designed, providing a set of representation primitives. Second, a user-friendly acquisition tool has been designed and implemented, on the basis of the knowledge representation formalism. The acquisition tool provides various forms of help for the expert physicians, including different levels of syntactic and semantic tests in order to check the “well-formedness” of the guidelines being acquired. Third, a tool for executing guidelines on a specific patient has been made available. The execution module provides a hypothetical reasoning facility, to support physicians in the comparison of alternative diagnostic and/or therapeutic strategies. Moreover, advanced and extended AI techniques for temporal reasoning and temporal consistency checking are used both in the acquisition and in the execution phase. The GLARE approach has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure.

Conformance Checking of Executed Clinical Guidelines in Presence of Basic Medical Knowledge

Clinical Guidelines (CGs) capture medical evidence, but are not meant to deal with single patients’ peculiarities and specific context limitations and/or constraints. In practice, the physician has to exploit basic medical knowledge (BMK) in order to adapt the general CG to the specific case at hand. The interplay between CG knowledge and BMK can be very complex. In this paper, we explore such interaction from the viewpoint of the conformance problem, intended as the adherence of an observed CG execution trace to both types of knowledge. We propose an approach based on the GLARE language to represent CGs, and on an homogeneous formalization of both CGs and BMK using Event Calculus (EC) and its Prolog-based implementation \(\mathcal{REC}\), focusing on “a posteriori” conformance evaluation.

Supporting Knowledge-Based Decision Making in the Medical Context: The GLARE Approach

Knowledge-based clinical decision making is one of the most challenging activities of physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such activities by encoding the indications provided by evidence-based medicine. Computer-based approaches can provide useful facilities to put guidelines into practice and to support physicians in decision-making. Specifically, GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent prototypical tool providing advanced Artificial Intelligence techniques to support medical decision making, including what-if analysis, temporal reasoning, and decision theory analysis. The paper describes such facilities considering a real-world running example and focusing on the treatment of therapeutic decisions.

A Hybrid Approach to Clinical Guideline and to Basic Medical Knowledge Conformance

Several computer-based approaches to Clinical Guidelines have been developed in the last two decades. However, only recently the community has started to cope with the fact that Clinical Guidelines are just a part of the medical knowledge that physicians have to take into account when treating patients. The procedural knowledge in the guidelines have to be complemented by additional declarative medical knowledge. In this paper, we analyse such an interaction, by studying the conformance problem, defined as evaluating the adherence of a set of performed clinical actions w.r.t. the behaviour recommended by the guideline and by the medical knowledge.

A Case-Based Architecture for Temporal Abstraction Configuration and Processing

In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextual knowledge plays a fundamental role in the time series interpretation. Since defining the right configuration for each possible contextual situation may be impractical, we propose to adopt a case-based approach, where the suitable configuration can be obtained by looking at the most similar already configured case, with respect to the current situation. Configured cases are indexed by means of contextual information. The obtained configuration can then be used as input to a temporal abstraction module, providing a set of qualitative states, trends and suitable combination of both as a result. Cases can then be exploited in the processing of such results as well, by providing an evaluation of the whole abstraction processing, possibly leading to the revision of the case base. The approach is illustrated by means of an example taken from a medical application, concerning the monitoring and evaluation of patients undergoing hemodialysis treatment

Clinical guidelines adaptation: managing authoring and versioning issues

One of the biggest issues in guideline dissemination nowadays is the need of adapting guidelines themselves to the application contexts, and to keep them up to date. In this paper, we propose a computer-based approach to facilitate the adaptation task. In particular, we focus on the management of two different levels of authors (users and supervisors), and of the history of the guideline versions.

Modeling Clinical Guidelines through Petri Nets

Clinical guidelines (GLs) play an important role to standardize and organize clinical processes according to evidence-based medicine. Several computer-based GL representation languages have been defined, usually focusing on expressiveness and/or on user-friendliness. In many cases, the interpretation of some constructs in such languages is quite unclear. Only recently researchers have started to provide a formal semantics for some of such languages, thus providing an unambiguous specification for implementers, and a formal ground in which different approaches can be compared, and verification techniques can be applied. Petri Nets are a natural candidate formalism to cope with GL semantics, since they are explicitly geared towards the representation of processes, and are paired with powerful verification mechanisms. We show how Petri Nets can cope with the semantics of GLs in a clear way, taking the system GLARE formalism as a case study.

Multi-level Abstractions and Multi-dimensional Retrieval of Cases with Time Series Features

Time series retrieval is a critical issue in all domains in which the observed phenomenon dynamics have to be dealt with. In this paper, we propose a novel, domain independent time series retrieval framework, based on Temporal Abstractions (TA). Our framework allows for multi-level abstractions , according to two dimensions , namely a taxonomy of (trend or state) symbols, and a variety of time granularities. Moreover, we allow for flexible querying , where queries can be expressed at any level of detail in both dimensions, also in an interactive fashion, and ground cases as well as generalized ones can be retrieved. We also take advantage of multi-dimensional orthogonal index structures , which can be refined progressively and on demand . The framework in practice is illustrated by means of a case study in hemodialysis.

A CBR-BASED, CLOSED-LOOP ARCHITECTURE FOR TEMPORAL ABSTRACTIONS CONFIGURATION

In the hemodialysis domain, we are implementing a case‐based, closed‐loop architecture aimed at configuring temporal abstractions (TA), which will be applied to time series data. The advantage of a case‐based approach is the one of “quickly” obtaining a suitable TA parameter configuration, simply by looking at the most similar already configured case, where configured cases are indexed by means of contextual information. The retrieved configuration, together with the time series data, is then used as an input to a TA processing module, able to provide a set of qualitative states, trends, and significant combinations of both as an output. TA processing results can finally be evaluated, possibly leading to a (human‐supervized) reorganization/revision of the case base content, to ameliorate future TA configuration sessions—thus closing the loop. The work is being integrated with RHENE, a system for case‐based retrieval in hemodialysis, able to work both on raw time series data and on preprocessed (by means of TA) ones.