Abstract: An interactive computer assisted method reviews, and analyzes, a patient who needs one or more medications using a computer and a user associated therewith. The method includes the steps of pre-selecting patients to obtain a preliminary set of patients eligible for the interactive computer assisted method responsive to first predetermined criteria, and filtering the preliminary set of patients to identify and form a secondary set of patients from the preliminary set of patients having a greater likelihood of benefiting from the interactive computer assisted method responsive to second predetermined criteria. The method also includes the steps of enrolling a patient from the secondary set of patients, and communicating with the patient to obtain information to assist the user in determining whether therapy and/or medication issues are relevant.

Abstract: An Intelligent Profiling Engine system contains a knowledge base and form base in which domain knowledge, forms, filters, rules, and clocks are represented. The system profiles users by presenting forms containing questions to users via multiple communication channels. Forms are selected based upon the results of utilizing filters, rules, and clocks to identify target populations of users. Filters, rules, and clocks also specify what information is important with respect to each user and how frequently information about each user must be refreshed. Liaisons present forms to users, store answers in a user data tank, and deliver benefits to users as a reward for answering questions. The system maximizes the utility of information gathered from users over time according to form weights. Thus, the invention is an automated profiling scheme.

Abstract: Delays in event detection in time-varying data can be reduced by predicting the time-varying data and then detecting the event in the predicted data. This finds application in the triggering of medical imaging devices, where physiological events can be detected in the time-varying data. An artificial neural network can be trained to predict data such as ECG signals from which a detection algorithm can accurately predict the occurrence of an event that will serve as a reference point for triggering.

Abstract: An automated, real-time, reprogrammable monitoring and control system for portable, remote sensors and subjects includes one or more portable monitoring units, each of the portable monitoring units having a sensor, a location-determining device, and a sensor interface unit. Each sensor interface unit is separately configured to monitor its sensor and to transmit that sensor's data, via a digital wireless communications network, to a central monitoring device. The portable unit is carried or worn by a person or animal, or affixed to an inanimate subject.

Abstract: A critical event notification system continuously monitors patient statistics and lab data to detect critical events, and automatically pages a responsible physician or physicians, each having an alphanumeric pager. In particular, a computer is used to continually access real-time data and multiple hospital databases which are periodically updated. These databases include patient chart databases, databases corresponding to patient history and databases maintained by various labs. The computer, preferably a clinical information system, is automatically provided with certain data, or periodically extracts it from other, relational databases. The computer automatically reviews this data, makes the critical event determination, and formulates an alphanumeric message that is informative as to the patient's condition and the reasons why a critical event was detected.

Abstract: A method of determining the skin-line in a digital medical image comprising the steps of:providing a digital medical image including a matrix of lines and columns of pixels;determining the minimum gray level of the background pixels using a background detection routine;selecting all or at least a representative sample of said lines and/or columns of said image, and for each selected line or column;smoothing the line to minimize the effect of noise on the delineation of pixel gray level transitions;identifying significant transitions from segments of monotonically decreasing gray level values that span a minimum gray level range and start near or above said minimum background gray level;computing a set of features for each identified significant transition; andbased on the set of features computed for each of the identified significant transitions, classifying the transition as either a skin-line transition or as a non-skin-line transition.

Abstract: A fuzzy control system, particularly for dose rate control in an X-ray diagnostics apparatus, such a control system converting input signals into output signals. A first and a second individual module are provided, whereby respectively at least two input signals can be supplied to the first and to the second individual module for generating an output signal. Compared to a fuzzy control system that does not comprise these individual modules, the rule base becomes smaller, only input conditions that have influence on the individual module need be provided with rules, so that a simple check of the rules for completeness is assured.

Abstract: A process is set forth in which cancer of the colon is assessed in a patient. The probabilities of developing cancer involves the initial step of extracting a set of sample body fluids from the patient. Fluids can be evaluated to determine certain marker constituents in the body fluids. Fluids which are extracted have some relationship to me development of cancer, precancer or tendency toward cancerous conditions. The body fluid markers are measured and other quantified. The marker data then is evaluated using a nonlinear technique exemplified through the use of a multiple input and multiple output neural network having a variable learning rate and training rate. The neural network is provided with data from other patients for the same or similar markers. Data from other patients who did and did not have cancer is used in the leaning of the neural network which thereby processes the data and provides a determination that the patient has a cancerous condition, precancer cells or a tendency towards cancer.