835 results about "SAPS II" patented technology

This patent document discusses, among other things, systems, devices, and methods for enhancing detection of pulmonary edema using, in addition to thoracic impedance, one or a combination of: physiologic information about a subject, at least one statistical parameter, a user-programmable detection level, at least one parameter associated with a previous pulmonary edema event, and patient symptom information about the subject. In one example, a (base) thoracic impedance threshold is modified to an adjusted thoracic impedance threshold. The adjusted thoracic impedance threshold provides an increased sensitivity of pulmonary edema detection as compared to the base thoracic impedance threshold. In another example, an alert is provided to a subject, a caregiver, or other user based on a pulmonary edema indication determined by the present systems, devices, and methods. In a further example, a therapy (provided to the subject) is adjusted or initiated in response to the pulmonary edema indication.

A method is disclosed for detecting symptoms in a network infrastructure, combining one or more symptoms into a problem, then optionally applying detailed diagnosis to the network elements experiencing the problems, ultimately providing detailed problem determination.

For Parkinson's patients to function at their best, their medications need to be optimally adjusted to the diurnal variation of symptoms. For this to occur, it is important for the managing clinician to have an accurate picture of how the patient's bradykinesia/hypokinesia and dyskinesia and the patient's perception of movement state fluctuate throughout the normal daily activities. The present invention uses wearable accelerometers coupled with computer implemented learning and statistical analysis techniques in order to classify the movement states of Parkinson's patients and to provide a timeline of how the patients fluctuate throughout the day.

A computer implemented method on a computer readable media is provided for determining the source of a problem in a complex system of managed components based upon symptoms. The problem source identification process is split into different activities. Explicit configuration non-specific representations of types of managed components, their problems, symptoms and the relations along which the problems or symptoms propagate are created that can be manipulated by executable computer code. A data structure is produced for determining the source of a problem by combining one or more of the representations based on information of specific instances of managed components in the system. Computer code is then executed which uses the data structure to determine the source of the problem from one or more symptoms.

The present invention relates to the intrathecal (IT) administration of recombinant enzyme to treat lysosomal storage disorders. In an exemplary embodiment, intrathecal administration of human α-L-iduronidase (rhIDU) injections in MPS I affected animals resulted in significant enzyme uptake, significant rh-iduronidase activity in brain and meninges and a decrease of glycosaminoglycan (GAG) storage in cells of MPS I subjects to that of normal subjects. Intrathecal administration proved more effective than intravenous treatment at alleviating MPS I symptoms, indicating it is a useful method of treating lysosomal storage disorders.

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular stroke risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which computes: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic ground truth information about the training patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular stroke risk score value on the basis of the four set of wall features.

The invention features methods, systems, and computer programs for generating a customized set of possible medical conditions, thereby providing access to medical information relevant to the user's state of health. The methods and systems involve at least some of the following steps: receiving a list of symptoms specified in terms selected from a first language set; translating the list of symptoms into a translated list of symptoms specified in terms selected from a second language set; using the translated list of symptoms to generate possible medical conditions, the possible medical conditions described in terms of the second language set; and translating the possible medical conditions into descriptions that are specified in terms selected from the first language set.

A system and method for providing computerized, knowledge-based medical diagnostic and treatment advice. The medical advice is provided to the general public over a telephone network. Two new authoring languages, interactive voice response and speech recognition are used to enable expert and general practitioner knowledge to be encoded for access by the public. “Meta” functions for time-density analysis of a number of factors regarding the number of medical complaints per unit of time are an integral part of the system. A semantic discrepancy evaluator routine along with a mental status examination are used to detect the consciousness level of a user of the system. A re-enter feature monitors the user's changing condition over time. A symptom severity analysis helps to respond to the changing conditions. System sensitivity factors may be changed at a global level or other levels to adjust the system advice as necessary.

The present invention relates to a movement disorder monitor, and a method of measuring the severity of a subject's movement disorder. The present invention additionally relates to a drug delivery system for dosing a subject in response to the increased severity of a subject's symptoms. The present invention provides for a system and method, which can accurately quantify symptoms of movements disorders, accurately quantifies symptoms utilizing both kinetic information and electromyography (EMG) data, that can be worn continuously to provide continuous information to be analyzed as needed by the clinician, that can provide analysis in real-time, that allows for home monitoring of symptoms in subject's with these movement disorders to capture the complex fluctuation patterns of the disease over the course of days, weeks or months, that maximizes subject safety, and that provides remote access to the clinician or physician.

The system and process of the present invention employs encrypted data input from both users and physicians to identify disturbances in human biologic function. Physical and metabolic characteristics, historical data and current symptoms are compiled through use of a computerized expert system that recognizes patterns predictive of metabolic dysfunction or insufficiency. Functional capabilities of all bodily systems are simultaneously evaluated at each input session through expert system analysis to create science-based remedial plans exemplifying methods of lifestyle alteration and biologic response modification through nutrition and other means. Enhancement of metabolic activity is presumed on the basis of improving symptom, metabolic, and laboratory scores. Lifestyle, fitness, nutritional, and dietary plans are formatted for uncomplicated implementation and professional guidance in applying all interventions to uniquely integrate the offerings of medical, healthcare and lifestyle professionals.