A Realistic Model for Temporal Reasoning in Real-Time Patient Monitoring
M. DOJAT *, C. SAYETTAT ** *INSERM U. 296, 94010, Créteil, France. **URA CNRS 817 HeuDiaSyC, UTC, 60206, Compiègne, France.
Running title: Temporal Reasoning in Patient Monitoring
Address for correspondence: Michel DOJAT INSERM Unité 296 Faculté de Médecine 8, avenue du Général Sarrail 94010 CRETEIL FRANCE Phone: 33 1 48 98 46 03 Fax: 33 1 48 98 17 77 e-mail:
[email protected]
ABSTRACT
Time is a central factor in patient monitoring. Introduction of domaindependent knowledge is essential to ensure efficiency of time managers especially when embedded into systems that interact with real world. We present a realistic temporal reasoning model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgetting of non-relevant information. We describe in detail how we represented the proposed model and how, by refinement of domain-independent temporal entities and inferences, we added domain-specific knowledge to manage a clinical therapy. The model allows clinical observations to be incrementally interpreted as they are acquired by an intelligent system, mainly reactive in its reasoning, for the management of patients receiving respiratory support.
INTRODUCTION To avoid some of the problems encountered by human operators, such as data overload or missing important events, caused by the massive flux of information, systems have been developed to perform intelligent monitoring. Intelligent monitoring (Uckun, 1993) is a complex task that includes 1) collection of data in real-time, 2) diagnosis of observed situations, 3) prediction of system evolution, 4) construction of action plans with prompt reaction in alarming cases, and for closed-loop systems, 5) execution of planned actions. The advanced biomedical equipment present in high dependency clinical environments such as operating rooms, recovery rooms or Intensive Care Units (ICU's), provides the clinical staff a large mass of data about the patient's state. It is foreseen that information to be handled by nurses and physicians will exponentially increase in the future, because of the increasing needs of information in order to perform more effective medical cares and due to the sophistication of modern medical devices. Applied to the medical field, intelligent monitoring assists the clinical staff perform the difficult task of medical decision making, especially for critically ill patients. Several researchers (Long, 1983; Shortliffe, 1990) have pointed out the necessity for an explicit representation of time in medical diagnosis. Moreover, time is a central factor in intelligent monitoring systems that are supposed to interact with dynamic environments. Since the earlier work in Artificial Intelligence (AI), many formal studies have been performed about change and time representation (McDermott, 1982; Allen, 1984; Galton, 1990; Dechter et al., 1991; Van Beek, 1992) . However, the complexity of temporal constraint propagation algorithms have limited the integration of a time manager into real-time systems such as intelligent monitoring systems. Moreover, expressive power of formal approaches are generally insufficient to reflect real situations that are various and complex. Less theoretical attempts have been made for integrating temporal reasoning into usable clinical systems (Rucker et al., 1990; Lau and Vincent, 1993). Because they exploit domain information and domain structure, these systems are less general but more efficient than domain-independent formal approaches. We pursue in this direction and our approach is an endeavour to mimic the clinician's reasoning who takes care of a patient in ICU's. Starting from medical expertises in ICU's, we elaborated a realistic model, clearly a well-
3
defined simplification of the real medical reasoning process, and used it to design an extendable working prototype for patient monitoring. This paper presents the temporal part of our model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgetting of non-relevant information. We assume that these two mechanisms, which require domain and context dependent knowledge, are essential for a class of applications such as real-time patient monitoring. Temporal constraint propagation is reduced to the needs of the discourse to model. This allows clinical observations to be incrementally interpreted as they are acquired. There is a general agreement in AI about the need for an elicitation from the expert of an implementation-independent model (referred as "the knowledge level" (Newell, 1982) ) that may be transformed into an implementation (referred as "the symbol level"). The symbol level (or design model) realises a concrete representation and implements an approximation as close as possible to its specifications at the knowledge level (or conceptual model). Therefore, the reminder of the paper is structured as follows. In Section 2, we analyse the expert's reasoning to elicit its temporal components, and propose a conceptual model that consists in a temporal ontology and temporal inferences. After a brief summary of those aspects of a typical dynamic medical expertise that are illustrative of aggregation and forgetting mechanisms, Section 3 describes our design model applied to the management of patients receiving respiratory support. Section 4 shows the adaptation of the model by gradual refinement to manage real clinical situations. Finally, we discuss some aspects of this work to show that the system realised provides an useful extendable framework for real-time patient monitoring.
A CONCEPTUAL MODEL OF TEMPORAL REASONING IN PATIENT MONITORING Several abstraction paradigms and conceptual architectures have been proposed to build rationally knowledge-based systems (Clancey, 1985; Chandrasekaran, 1986; McDermott, 1988; Wielinga et al., 1992) . The generic tasks approach proposed by Chandrasekaran considers that a complex task, as the supervision of a patient, can be decomposed into specialised sub-tasks. Each sub-task is in turn decomposed and so forth. At the bottom level of the hierarchy of sub-tasks, we find a set of basic elements named generic tasks. We
4
adopt this approach to enumerate some sub-tasks present in patient monitoring and to extract those which must take time into account. Generic Tasks in Real-Time Patient Monitoring In a slightly different version of the initial Chandrasekaran's propositions, (Stefanelli et al., 1992) consider three fundamental tasks in medical reasoning: 1) diagnosis 2) therapy planning and 3) monitoring. We refine this view to take into account the specificity of real-time patient monitoring. We elicited nine sub-tasks from intensive care expertises: -1) Data Processing (DP): the operator collects information about supervised system from several data sources (such as in ICU's, ventilator, gas monitor ...) to characterise quantitatively the current situation. -2) Classification (C1): the current situation is qualitatively defined by comparison to a collection of typical cases (such as normal ventilation, hyperventilation or hypoventilation in a ventilation monitoring process). -3) Temporal Reasoning (TR1): the operator assesses the observed evolution of system behaviour. He/she appreciates stability or change in system behaviour. -4) Classification (C2): the observed evolution is compared to a set of typical situations (scenarios). This allows a theoretical prediction of the behaviour of the supervised system (called extrapolated evolution). -5) Classification (C3): the comparison between the extrapolated evolution and the ideal evolution expected by the operator (called expected evolution), generates an estimation of the effectiveness of the previous executed actions. -6) Action Planning (AP1): An updated general action plan is constructed. Each action is detailed according to the current context. A new ideal state for the system is defined depending on the planned actions. -7) Temporal Reasoning (TR2): this step allows to analyse the coherence (stability, change, ...) of the new ideal state with the current expected evolution. -8) Action Planning (AP2): An examination of expected evolution, observed evolution and planned actions can lead to a refinement or a modification of the initial proposed action plan. -9) Action Execution (AE): in the case of a closed-loop system, planned actions are performed on the system. The set of sub-tasks is shown in the Figure 1. The sub-tasks 2, 3 and 4 correspond to the definition of diagnosis task given by (Stefanelli et al., 1992) : "the best explanation of patient's condition". The sub-tasks 5 and 6 are 5
conformed to the definition of the therapy planning task: "the best action to perform in order to improve patient's condition", and sub-tasks 1, 7, 8 and 9 are consistent with the definition of monitoring task: "the best strategy to verify if the planned action proves to be effective". Depending on the medical application, this simple scheme accepts several variations. For example, the recognition of the current patient's state (diagnosis task) can be more or less complex and can necessitate the elaboration of a set of diagnostic hypothesis and the hypothesis selection by requesting new information (monitoring task). Likewise, when expected evolution (monitoring task) is in contradiction with observed evolution (diagnosis task), despite several actions, a new examination of the patient's data (diagnosis task) must be envisaged. The sequencing of the reasoning may also be broken especially in alarming cases where specific actions have to be preferentially executed. In our model, classification is used several times to recognise current state (C1), to theoretically extrapolate future behaviour (C2) and to appreciate the effectiveness of action plan (C3). Temporal abstractions (TR1, TR2) are used to determine observed and expected evolution. The complete description of the proposed reasoning model for patient monitoring is beyond the scope of this paper. We focus our presentation on time and change representation we used. Temporal Ontology Our approach is based on the philosophical assumption that time exists only because change is perceived (phenomenological point of view). In patient monitoring process, we are interested in physiological disorders evolution and in the patient's response to therapeutic actions. The clinician has to answer questions as "how long has been the patient hyperventilated?", "is increase of mechanical respiratory assistance effective?", "has been the patient stable long enough so as to decrease the level of assistance?" ... Thus, a model with events, states and cause-effect relationships seems well adapted. Our ontology divides the world between "existing" domain entities that by essence have no temporal dimension (description of the world) and temporal entities that are time stamped and are used to develop a temporal discourse about the changing world. The perception of change (or time) is highly dependent on the context, relative to the actions we envisage to perform on the environment. Temporal entities are considered as conceptual entities about which an operator can have knowledge and about which it can reason. 6
Temporal Entities and Links among Entities The model is based on an object-oriented paradigm in which temporal concepts are represented by entities, called objects, containing both variables (attributes) and procedures. We model how concepts belong to one another using taxonomic links among entities. The Figure 2 shows the complete organisation of entities. The basic entity of our ontology called TemporalObject possesses two attributes: a temporal stamp to locate it on a temporal axis and a value that refers to "a domain object" that is subject to change over time. We consider time as a linearly ordered set of instants. The resolution depends on the application. We refine TemporalObject into three concepts: Events, States and Chronicles. Event An event has a temporal dimension and its occurrence modifies the state of the world. According to (Kowalski and Sergot, 1986) events are central in our model; they may initiate a change in our description of the world. Events are generally either instantaneous objects or objects whose duration is without interest. A time point may be associated to their occurrence date (attribute temporal stamp). State A state introduces the notion of duration. A state characterises a local property of the world that fills the space, an information (attribute: value) that lasts over a time period. The transition between two states is caused by an event's occurrence and a state is assumed to persist until an event terminates it. Thus, a state is valid over a period (attribute: validity), represented by an interval, defined by events corresponding to its start (attribute temporal stamp) and if needed to its end (attribute: end). A state is equivalent to the McDermott's definition (McDermott, 1982) , and to the notion of a property or a relationship that is persistent (Allen, 1984) , (Kowalski and Sergot, 1986) . Generally, information represented by a state is persistent over the associated time period. In some cases, that is not true and we have added the notion of
7
discontinuous interval for which an information is not persistent over all its sub-intervals*.
Chronicles Defined as a ordered collection of temporal objects, a Chronicle represents the real history of the world as it is perceived by the system or by an operator. Chronicles can be grouped into still larger blocks. Some methods may be developed to manage the set of Chronicles that constitutes a temporal database. Chronicles have a different meaning than in (McDermott, 1982) where they are considered as a possible history of the world. Temporal Inferences Temporal reasoning has been studied by several researchers in terms of constraint satisfaction problem. The constraint propagation is NP-complete with interval-algebra or tractable with a point-algebra and a restricted intervalalgebra (Vilain and Kautz, 1986) . Much work is aimed at improving the efficiency of graph algorithms (Ghallab and Mounir Alaoui, 1989) or constraint-based algorithms (Van Beek, 1992) to manage large data sets of temporal information with acceptable performance. However for realistic applications, the performance of formal approaches remains still problematic and domain-dependent considerations have to be introduced to ensure computational tractability. Our solution of this problem as described below, is to adopt the same attitude than used by a physician who manages patient's state evolution. Temporal Abstractions Driven by collected data, the expert must diagnose the current state of the system, predict the evolution and act if necessary on the process in order to reach a fixed goal. Temporal constraints imposed by such a task are structured (see the reasoning scheme previously described) and the construction of a temporal graph is not required. We consider that accurate time-stamps for the observations are available, thus temporal structures are temporally totallyordered.
* This is equivalent to Allen's processes.
8
We assume that the expert uses three forms of temporal abstractions: Event-State relations, Aggregation and Forgetting. Temporal abstractions are schematically described by the Figure 3. Event-State Relations The occurrence of an event can terminate the time period associated with some temporal objects or create new temporal objects. There is a cause-effect relationship between event and state. Events are not necessarily unpredictable. Some events are triggered to act on the world or to evaluate local change by producing a scansion of time. In process monitoring, after classification that requires domain-specific heuristics, a diagnosis (an event) introduces a new explanation of system situation (a state) at a precise instant. Thus, initial quantitative observations are converted into qualitative information over time intervals. By interpolation, the recognised situation is assumed to persist at worse since the last diagnosis. This assumption switches a time point based representation (discrete event) to an interval based representation (continuous state). Aggregation and Forgetting Across time, many successive states could be diagnosed and accumulated. But guided by natural necessity to act, the expert aggregates similar states, recognises sudden interruption in a continuous state (break), and forgets non-relevant, redundant or out of date information. Aggregation and forgetting are used to dynamically modify the length and the location of a mobile temporal window that brings to light a set of temporal information useful to the current reasoning process. All states not visible through this window are forgotten at least for a while. Activation of these two mechanisms is context-dependent and essential to build dynamically a global interpretation of the system evolution in order to define action plans. Aggregation of temporal objects or recognition of breaks require the introduction of semantic considerations. The individuality of entities is concealed each time there is no practical interest to perceive it. Forgetting is crucial for artificial or natural systems with memory. There are many forms of forgetting; we distinguish two simple forms: 1) active forgetting* : according to particular deductions some information are deliberately erased during the reasoning process 2) passive forgetting** : * similar to conscious repression. ** similar to unconscious repression without a Freudian dimension.
9
infrequently used information is naturally inclined to vanish with time. When processing information tend to exceed the capacity of the working memory (mental overload), forgetting mechanism seems automatically triggered to garbage out of date information. Formalism for Modelling Temporal Abstractions The presentation is reduced to the most important predicates or axioms. By analogy to Kowalski notation, we introduced two predicates terminates and initiates relating a temporal object to its starting and ending points. This defines causal relations between events and some temporal objects and formalises the first category of temporal abstractions: event-state relationships. We formalised relations between predicates using a first-order logic. We considered that a state is persistent until it is explicitly terminated by the occurrence of an event. Let TO1,..., TOn be n temporal objects associated with time periods I1,..., In and E1,..., Em be m events. r1: r2:
terminates(E1, TO1) E1 = ends(TO1) initiates(E1, TO1) E1 = starts(TO1)
A predicate associates a time-point to an event. r3: appearsAt(E1, Date) We defined two axioms A1 and A2 that indicate when a temporal object is valid: A1: persistent(TO1) ¬ (∃E1) terminates(E1, TO1) A2: broken(TO1) (∃E1) terminates(E1, TO1) We introduced A3 axiom to aggregate identical consecutive objects and forget redundant information: A3: (∀TO1), (∀TO2) sameAs(TO1, TO2) TO1
