Systems and methods for medical diagnosis or risk assessment for a patient are provided. These systems and methods are designed to be employed at the point of care, such as in emergency rooms and operating rooms, or in any situation in which a rapid and accurate result is desired. The systems and methods process patient data, particularly data from point of care diagnostic tests or assays, including immunoassays, electrocardiograms, X-rays and other such tests, and provide an indication of a medical condition or risk or absence thereof. The systems include an instrument for reading or evaluating the test data and software for converting the data into diagnostic or risk assessment information.

Point of care diagnostic systems

Operational status information associated with a process entity in a process plant(10) is received. The operational status information is mapped into one of a plurality of status conditions. Then, an alert message associated with the process entity is generated, where the alert message is indicative of the one status condition of the plurality of status conditions.

Integrated alert generation in a process plant

A diagnostic system for use in a process control system collects and stores in a database information pertaining to the operation of the process control system, and that uses an expert engine to apply rules for analysis to the information in the database to determine solutions to problems. The database stores various types of information such as event and alarm data, notices of scheduled maintenance and changes to operating parameters, and historical data related to previous changes to the process control system that are relevant to determining both the source of the problems detected in the process control system and the steps necessary to either further analyze or correct the detected problems. The diagnostic system identifies the source of the problem and identifies and runs the appropriate analytical tools or takes remedial measures based on the rules for analysis for the expert engine.

Diagnostic expert in a process control system

A diagnostic tool automatically collects and stores data indicative of a variability parameter, a mode parameter, a status parameter and a limit parameter associated with each of the different devices, loops or function blocks within a process control system, processes the collected data to determine which devices, loops or function blocks have problems that result in reduced performance of the process control system, displays a list of detected problems to an operator and then suggests the use of other, more specific diagnostic tools to further pinpoint or correct the problems. When the diagnostic tool recommends and executes a data intensive application as the further diagnostic tool, it automatically configures a controller of the process control network to collect the data needed for such a tool.

Diagnostics in a process control system

A system and method for analyzing a process collects process data within a process control plant and transmits the collected process data to a remote data processing facility. The remote data processing facility analyzes the process data using a process analysis tool such as a process monitoring tool, an equipment monitoring tool, a device monitoring tool, an index generation tool, a work order generation tool and/or an accounting tool to generate analysis data. The analysis data is then transmitted to the process control plant via a communication link such as the Internet.

Remote analysis of process control plant data

A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102). The output of the residual layer (102) is input to a hidden layer (108) which also receives the control inputs to generate a predicted output in an output layer (106). During back propagation of error, the residual values in the residual layer (102) are latched and only the control inputs allowed to vary.

Residual activation neural network

A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.

Method for steady-state identification based upon identified dynamics

A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse model (36) to generate predicted control inputs. The predicted control inputs are processed through a filter (46) to apply hard constraints and sensitivity modifiers, the values of which are received from a control parameter block (22). During operation, the sensitivity of output variables on various input variables is determined. This information can be displayed and then the user allowed to select which of the input variables constitute the most sensitive input variables. These can then be utilized with a control network (470) to modify the predicted values of the input variables. Additionally, a neural network (406) can be trained on only the selected input variables that are determined to be the most sensitive. In this operation, the network is first configured and trained with all input nodes and with all training data. This provides a learned representation of the output wherein the combined effects of all other input variables are taken into account in the determination of the effect of each of the input variables thereon. The network (406) is then reconfigured with only the selected inputs and then the network (406) again trained on only the input/output pairs associated with the select input variables.

Method and apparatus for determining the sensitivity of inputs to a neural network on output parameters

System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

Parametric universal nonlinear dynamics approximator and use

In certain embodiments, a control/optimization system includes an instantiated model object stored in memory on a model server. The model object includes a model of a plant or process being controlled. The model object comprises an interface that precludes the transmission of proprietary information via the interface. The control/optimization system also includes a decision engine software module stored in memory on a decision support server. The decision engine software module is configured to request information from the model object through a communication network via a communication protocol that precludes the transmission of proprietary information, and to receive the requested information from the model object through the communication network via the communication protocol.

Kadir Liano

Papers

Residual activation neural network

Method for steady-state identification based upon identified dynamics

Method and apparatus for determining the sensitivity of inputs to a neural network on output parameters

Parametric universal nonlinear dynamics approximator and use

Secure models for model-based control and optimization