Approaching the complexity of biomedical signal processing

1. Approaching the complexity of biomedical signal processing An agent-centered perspective Part III - Application to Patient Monitoring 5th IEEE-EMBS Summer School on Biomedical Signal Processing

2. Part III - Application to Patient Monitoring • 1. Monitoring as a physically distributed problem • 2. Monitoring as a (distributed) cognition issue • 3. Monitoring as a negotiation problem 5th IEEE-EMBS Summer School on Biomedical Signal Processing

3. 1. - Monitoring as a physically distributed problem • Dimitrios G. Katehakis et al. : A Distributed, Agent-Based Architecture for the Acquisition, Management, Archiving and Display of Real-Time Monitoring Data in the Intensive Care Unit, Technical Report FORTH-ICS / TR-261 • http://www.ics.forth.gr/ICS/acti/cmi_hta/publications/technical_reports/tr261/ICU.html 5th IEEE-EMBS Summer School on Biomedical Signal Processing

4. Cardiovascular Monitor Ventilator Other machines Pumps A A N Isolation transformer N Data collector A : Analogical N : Numerical HIS Patient control Interactive panel Intensive Care Units…a physically distributed environment 5th IEEE-EMBS Summer School on Biomedical Signal Processing

5. Intensive Care Units…a physically distributed environment • Many variables : • Continuous measurements of : electrocardiogram, central venous pressure, systemic arterial pressures, cardiac output, urine output, pulmonary arterial pressures, blood gases, and mixed venous saturation ; • Measurements made by the ventilator itself: respiratory rate, tidal volume, peak inspiratory pressure, average airway pressure, spontaneous minute volume, lung mechanics, oxygen consumption, and metabolic rate ; • Many interaction / monitoring needs ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

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8. System Architecture • Two types of agents : the acquisition agent and the monitoring agent. Acquisition agents perform data acquisition and feed data to monitoring agents, who facilitate data visualization and storage ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

9. Agent ’s role • The acquisition agent collects data either from patient connected sensors or from clinical information systems ; • Acquired data are kept temporarily on a local data store, until they are transmitted to the appropriate monitoring agents ; • An acquisition agent may have a number of input and output channels, each of which can be dedicated to a different monitoring agent. The acquisition agent is therefore communicating with several monitoring agents simultaneously ; • The monitoring agents receive data, which are stored temporarily in a data repository and are visualized through a Graphical User Interface (GUI) ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

10. Introducing cognition agents… 5th IEEE-EMBS Summer School on Biomedical Signal Processing

11. 2. - Monitoring as a (distributed) cognitive issue • GUARDIAN -- A prototype intelligent agent for monitoring and therapeutics in intensive care • Barbara Hayes-Roth • Knowledge System, Laboratory at Stanford University • http://ai.eecs.umich.edu/cogarch2/authors/bhayes-roth.html 5th IEEE-EMBS Summer School on Biomedical Signal Processing

12. Monitoring as a (distributed) cognitive issue • The perception - cognition - action problem : • A compromise to be reached between the quality and rapidity of reasoning ; • Sacrifice the quality of a solution for one that meets the deadline ; • A quick action of less quality will push off the deadline far enough so that a quality solution can be found ; • Interleave reactive and cognitive behaviours ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

13. Architecture • Three independent sub-systems : cognition, perception and action, which : • Operate concurrently and asynchronously ; • Communicate through a globally accessable communication interface which asynchronously relays data among limited size I/O buffers ; • the system interact concurrently with subsets of the environment, • thereby increasing performance, • and reducing the overall complexity each subsystem must be able to deal with ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

14. Communication interface I/O I/O I/O I/O filters filters filters I/O I/O filters I/O I/O Cognitive system Perceptual input / Cognitive events Action parameter/filters Perceptual input/filters Fast reflex arcs Action agents Perception agents Perceptual input Action parameter Interactive displays Sensors Actuators I/O Patient, ventilator, human users, … 5th IEEE-EMBS Summer School on Biomedical Signal Processing

15. Perception agent ’s role : interpretation • The sensor agent ’s role is to acquire a given type of signals, transduce it into an internal representation, and holds the results in its I/O buffer ; • The perception agent ’s role is to retrieve the sensor agent information, to analyze and interpret it and transmit the results to the Communication Interface ; • Example : peak inspiratory pressure • value « high » • trend « rising » • relevance to ongoing reasoning tasks : « not relevant  » • priority : « high » 5th IEEE-EMBS Summer School on Biomedical Signal Processing

16. Perception agent ’s role : focus of attention • The perception agent ’s retrieves the sensor agent information at some given rate, according to given filters ; • Rate and filter information is transmitted by the cognitive system, according to the current reasoning state ; • The system is therefore provided with focus of attention capabilities ; • The more important the data, the more often the system will see it ; • Conversely, as the buffer size is limited, if the cognitive system does not look at the buffer often enough, perceptual information may be lost ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

17. Action agent ’s role : action • The action agent ’s role is to : • Monitor its input buffer, retrieve intended actions, and translate them into executable programs of actuator commands ; • Control the execution of these programs by sending successive commands to its actuator at appropriate times ; • Example action : dynamically adjust the ventilator ’s settings ; • The action agents relieve the reasoning system of the computational burden of managing the low-level details of action execution 5th IEEE-EMBS Summer School on Biomedical Signal Processing

18. Action agent ’s role : user interaction • The action agent ’s role is also to communicate with the external users, in order to : • Recommend other interventions to correct diagnosed problems or avoid predicted problems ; • Give explanations about the system ’s current monitoring strategy, its reasoning about some particular problem, and the biological and physical phenomena underlying the patient ’s condition. 5th IEEE-EMBS Summer School on Biomedical Signal Processing

19. Coordination between perception, reasoning and action • The perception, reasoning, and action systems work concurrently, in a continuous way : • Information from the environment is perceived continuously, even while the system is engaged in computationally expensive reasoning tasks ; • The system is guaranteed to perceive any critical events that occur ; • Conversely, the system reasons continuously, regardless of the rate of incoming events. Thus, it is guaranteed to complete a critical reasoning task without interruption, unless it decides to attend to a more critical new event ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

20. Coordination between perception/reasoning and action • Fast reflex reactions occur across perception-action arcs and allow perception to drive action directly ; • For example, Guardian might automatically sound an alarm and deliver a simple explanation whenever perceived values of key physiological parameters enter critical ranges ; • Comparatively slow cognitive reactions involve all three systems, with cognition mediating actions in response to perception ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

21. Cognitive system ’s role • The role of the cognitive system is twofold : • To interpret perceived information from the environment, perform the needed reasoning tasks and decide what actions to perform (several reasoning agents) ; • To construct and modify dynamic control plans to coordinate the perception, reasoning, and action tasks (one control agent) ; • These tasks are performed by agents operating according to the blackboard model of control ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

22. Control agent Reasoning Agents Perception/Action Agents Cognitive system ’s Architecture 5th IEEE-EMBS Summer School on Biomedical Signal Processing

23. Control agent • The control agent comprises 3 components that run sequentially : • The agenda manager uses current perceptual and cognitive events and the current control plan to identify and rate possible reasoning operations ; it records them on the agenda buffer ; • The scheduler takes information from the agenda and uses the control plan to select the operation that best matches the current plan ; it records that operation on the next operation buffer ; it may also decide to interrupt the agenda management to give priority to critical operations ; • The executor executes the chosen operation, producing changes to the global memory : new perceptual preprocessing parameters or intended actions in output buffers; new reasoning results for ongoing tasks; or new control decisions ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

24. Agenda Control Plan Next Operation Cogn./perceptive events Controller agent cycle Scheduler Agenda Manager Executor Reasoning Agents Perception/Action Agents 5th IEEE-EMBS Summer School on Biomedical Signal Processing

25. Cognitive state • Holds the control information necessary to drive control ; it is comprised of three buffers : • The event buffer holds current asynchronously arriving perceptual inputs and cognitive events produced by reasoning ; • The agenda holds currently executable reasoning operations - those whose trigger conditions are satisfied ; • The next operation holds the reasoning operation to be executed next ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

26. Cognitive state • All of these buffers have limited capacity, and are exploited according to two criteria : • Best-first retrieval (items that score higher are retrieved earlier) ; • Worst-first overflow (items that score lower overflow earlier) ; • defined in terms of four orthogonal attributes : • Relevance to Guardian's current reasoning activities ; • Importance with respect to Guardian's global objectives ; • Recency of entering the buffer ; • Urgency of processing the item in order to have the intended effect (e.g., meet deadlines) ; • If too many critical events occur simultaneously, they will overflow the buffers ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

27. The control plan • A control plan is a temporal pattern of control decisions, each describing a class of operations to be performed, under specified constraints, during some time period ; • It is used to focus the reasoning cycle given a strategy to complete the task at hand ; • This includes determining which actions have priority on the agenda, when the scheduler should interrupt, and what the perceptual filters should contain ; • The only changes to the control plan are those made by the control KS, and hence were determined necessary by the control plan and environment at that time ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

28. Control Plan Cognitive State Perception/Action Agents Cognitive system’s Architecture Control KS Reasoning KS Control agent 5th IEEE-EMBS Summer School on Biomedical Signal Processing

29. Example Control Plan • Example plan : • Respond to critical events • Monitor all parameters for % changes • D 10D 2D • Time • According to the first decision, Guardian decides to respond to critical events. With the second decision, it decides that the perceptual preprocessor should send new values for patient parameters only when their values change by a threshold percentage. These decisions remain in effect (with some changes in preprocessing threshold) throughout the given period of time ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

30. Example Control KS • Name: Urgent-Reaction • Trigger: Critical observation, O • Action: Record control decision with • Prescription: Quickly react to O • Criticality: Criticality of O • Goal: Diagnosed problems related to O are corrected • This operation is triggered and its parameter, O, is instantiated whenever the perception system delivers an observation with high criticality (such as high PIP - Peak Inspiratory Pressure) ; • When executed, it generates a control decision favoring « quick » reasoning operations that « react to » O, and gives it the same criticality as O ; • The decision is deactivated when its goal is achieved, namely that all diagnosed problems related to O have been corrected. 5th IEEE-EMBS Summer School on Biomedical Signal Processing

31. Resulting control plan • Modified plan • Respond to critical events • Monitor all parameters for % changes • D 10D 2D • Quickly react to high PIP • Time 5th IEEE-EMBS Summer School on Biomedical Signal Processing

32. Reasoning knowledge : a multispecialist approach • Reasoning knowledge is distributed among several task-dependent specialists : • Diagnosis of observed signs and symptoms ; • Prediction of patient condition ; • Causal inference of precursors and consequences of observations, problems, etc. ; • Explanation of underlying causal phenomena ; • Each of these tasks may be performed using associative or model-based reasoning methods ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

33. Associative methods • Associative methods use clinical knowledge, apply to familiar problems, and give simple « answers », with minimal explanation ; • For example, Guardian responds to an observed rise in PIP by quickly diagnosing a hypoventilation problem and increasing the patient's ventilation ; • Having relieved the symptoms and extended the hard deadline, it acquires additional data to diagnose and correct the specific underlying problem (e.g., pneumothorax) ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

34. Model-based methods • Model-based methods use biological and first-principles knowledge, apply to familiar and unfamiliar problems, and give detailed « answers » with informative explanations ; • For example, Guardian can give a pathophysiological explanation of its prediction that normal minute ventilation of a cold post-operative patient will result in low arterial partial pressure of CO2 : • The patient's low temperature leads to decreased metabolic activity in the cells, this results in decreased O2 consumption and decreased CO2 production in the tissue compartment. 5th IEEE-EMBS Summer School on Biomedical Signal Processing

35. Model-based methods • Another example : • Name : Find-Generic-Causes • Trigger : Observe condition C where C exemplifies Generic-fault F • Action : Find Generic-fault that can-cause F • Find-Generic-Causes is triggered when C is observed ; • Upon execution, the action is to look for generic-faults that « can-cause » F ; • By recording each such cause in the global memory, this operation creates internal events that trigger other reasoning operations ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

36. An illustrative scenario • A scenario illustrating the system capacity to : • Manage moderately important, slowly evolving problems (e.g., low temperature and its consequences) ; • Manage time-critical problems (e.g., high PIP and the underlying pneumothorax) ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

37. A strategy to investigate a patient ’s low temperature • The system is monitoring all patient parameters for value changes of a threshold percentage ; • It notices the patient ’s low temperature, a non-critical problem but worth investigating ; • It makes control decisions that instantiate an abstract strategy for investigating this type of problem: • a) Diagnose the low temperature ; • b) Infer and correct immediate consequences ; • c) Predict changes ; • d) Infer and act to avoid expected consequences ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

38. a) Diagnose the low temperature : • Attribute the low temperature to the patient ’s immediate post-operative status ; • b) Infer and correct immediate consequences : • Infer that the patient's PaCO2 is currently low, due to the interaction between low temperature and normal breathing rate ; • c) Predict changes : • Predict that the temperature will rise to high and then fall to normal over several hours ; • Predict that the PaCO2 will rise to high and fall to normal with temperature ; • d) Infer and act to avoid expected consequences : • Decide to lower the breathing rate to correct the PaCO2 ; • Plan a series of rate changes correlated with temperature to maintain the PaCO2 within an acceptable range ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

39. An unexpected event • In the course of this strategy, the system observes high, rising PIP, indicating a potentially life-threatening condition with a deadline for corrective action on the order of minutes ; • A control decision is made that instantiates an abstract strategy for correcting critical conditions as quickly as possible; • Fast associative reasoning is favoured to diagnose and correct the problem ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

40. A strategy to correct critical conditions • a) Consider other patient data to diagnose the problem class, hypoventilation problem ; • b) Advise increasing ventilation so the patient will get enough oxygen; • c) Request diagnostic actions : auscultation of the chest for asymmetric breathing sounds and inspection of chest xrays ; • d) Diagnose the underlying problem, a pneumothorax; • e) Advise insertion of a chest tube to relieve the pressure of accumulated air in the chest cavity ; • f) Predict and confirm the resulting drop in PIP ; • g) Advise reduction of the breathing rate as increased ventilation is no longer necessary ; • h) Request a lab test in twenty minutes to confirm that blood gases are normal ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

41. Discussion • Knowledge representation is complex, even for simple and well-kown situations : it is difficult to ensure the order and time of execution of the system modules ; • There is a number of coefficients and variables to adjust… • The basic functions of sensing, reasoning and acting are distributed among local agents ; sensing and acting may be engaged in a pure reactive way but as well be influenced by the reasoning process under development ; • The ratio of intra-agent computation to inter-agent com-munication is relatively high ; • Consistency is ensured by a global control plan influencing what the agents tackle and constraining their internal decisions ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

42. 3. - Monitoring as a negotiation problem • Anthropic agency: a multiagent system for physiological processes Francesco Amigoni, Marco Dini, Nicola Gatti, and Marco Somalvico Artificial Intelligence in Medicine Journal, Vol. 27, n°3, 2003 Special issue « Software agents in health care » 5th IEEE-EMBS Summer School on Biomedical Signal Processing

43. The anthropic agency • Agency : a multiagent system as a single machine composed of complex components: the agents ; • Anthropic : from the Greek anthropos, namely man ; the agency is employed to model the physiological processes of the human being ; • An example application : the regulation of the glucose-insulin metabolism in diabetic patients, a process where partially overlapping models of glucose level regulation coexist ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

44. Diabetic pathology • Glucose is one of the body’s main sources of energy ; • The body regulates the processes that control the production and storage of glucose by secreting the endocrine hormone, insulin, from the pancreatic B-cells ; • Type 1 diabetes is characterized by a loss of pancreatic beta-cell (B-cell) function and an absolute insulin deficiency ; • Since insulin is the primary anabolic hormone that regulates blood glucose level, this results in the inability to maintain blood glucose concentrations within physiological limits ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

45. Diabetic pathology • A long time exposition to very high values of blood glucose concentration causes serious complications to other body organs (cardiovascular and renal system, retina) ; • Type 1 diabetics require a continuous supply of insulin for survival (multiple daily injections or a continuous subcutaneous insulin infusion guided by daily blood glucose measurements) in order to try and keep the glucose concentration under control ; • Many factors have to be considered to choose the current dose of insulin to inject : amount of food, current glucose concentration value, general physical state… 5th IEEE-EMBS Summer School on Biomedical Signal Processing

46. Diabetic pathology • In diabetes, there is an uncoupling of blood glucose levels and the concentration of insulin that prevents the proper regulation of glycemia. Instead of a narrow glycemic range, blood glucose deviations can extend from hypoglycemia into hyperglycemia ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

47. Diabetic pathology • The main problem in the diabetic pathology is the insulin response when the person eats because it is when the glucose concentration reaches the maximum value ; • Another issue is the effect of physical activity on the insulin level : • There is a need to keep constant the glucose level to sustain the physical activity ; • Conversely, physical activity helps regulating the glucose level and keeping more sensitive to insulin, therefore being able to function with less insulin ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

48. Variation of the glucose level when eating 5th IEEE-EMBS Summer School on Biomedical Signal Processing

49. Purpose of a monitoring system • To constantly monitor the patient, eg analyze its current physiological state ; • To inject isulin when needed ; • To adjust the insulin amount in order to keep the glucose and insulin concentrations as close as possible to the concentrations of a normal person ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing

50. System architecture • Three groups of agents working in an asynchronous way : knowledge extraction, decision making, and plan generation ; • Several types of decisional agents with only partial views of the phenomenon to be controlled and different viewpoints (eg physiological models) ; • Presence of overlapping decisional models : the input parameters as well as the output proposed decisions may intersect ; • A negotiation mechanism to fuse the corresponding decisions ; 5th IEEE-EMBS Summer School on Biomedical Signal Processing