8922results about "Medical data mining" patented technology

A point-of-care computer system includes a display positioned in a point-of-care location, a computer coupled to the display, and a network coupled to the computer to enable the computer to access information stored in a remote location.

The method of the present invention derives the illness probability of any selected person from a database of people stored in a computer and / or on a computer network using collected relational data from every person in the database, including whether a person has a contact relationship with another person in said database and utilizes a database of illnesses infection probability functions given different illnesses and states of nature including data relating to social relationship; type of disease; probability function of infection given a time unit; length of contact of the particular contact relationship link; and calculates at least one relational path between said person and each person in the data base with whom there is a contact relationship, direct or via other persons in the said database for deriving the illness probability of the selected person. In addition. the method of the present invention permits selecting the optimum treatment for a patient with an infectious disease based upon recommending a drug or drugs deemed optimum for treating the patient and permits generating alerts for the detection of emergency events such as the outbreak of an infectious disease or a biological, chemical or nuclear attack and for diseases management. Moreover, in accordance with the method of the present invention a given patient may compare his or her medical record with summary information of patients with similar defined criteria.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.

A system and method for providing goal-oriented patient management based upon comparative population data analysis is presented. At least one therapy goal is defined to manage a disease state. A patient population is selected sharing at least one characteristic with an individual patient presenting with indications of the disease state. One or more treatment regimens associated with the patient population are identified as implementing actions under the at least one therapy goal. The implementing actions are followed through one or more quantifiable physiological indications monitored via data sources associated with the patient.

The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

The present invention is directed to improved systems and methods for processing respiratory signals derived generally from respiratory plethysmography, and especially from respiratory inductive plethysmographic sensors mounted on a garment for ambulatory recording. The systems and methods provide improved signal filtering for artifact rejection, improved calibration of sensor data to produce outputs indicative of lung volumes. Further, this invention provides improved systems and methods directed to processing lung volume signals, however measured or derived, to provide improved determination of respiratory parameters and improved recognition of selected respiratory events.

Methods and systems for automatically establishing an enhanced electronic health record (EHR) for a patient include an automatic data collection facility that collects data of a medically related event in proximity to a patient upon occurrence of the event. The collected data may include medication administration data such as medication, time of administration, administration of a dosage of medication, reaction data, and the like. The collected data is communicated to a real-time data integration facility that automatically integrates the data with a patient's electronic health record to establish an enhanced electronic health record.

Disclosed herein are systems and methods for syndication and management of structured and unstructured data to assist institutional healthcare delivery, healthcare providers' practices, healthcare providers' group practices, collaborative academic research and decision making in healthcare, including through the utilization of medical devices and healthcare pools.

The information management system disclosed enables caregivers to make better decisions, faster, using aggregated genetic and phenotypic data. The system enables the integration, validation and analysis of genetic, phenotypic and clinical data from multiple subjects who may be at distributed facilities. A standardized data model stores a range of patient data in standardized data classes that encompass patient profile information, patient symptomatic information, patient treatment information, and patient diagnostic information including genetic information. Data from other systems is converted into the format of the standardized data classes using a data parser, or cartridge, specifically tailored to the source system. Relationships exist between standardized data classes that are based on expert rules and statistical models. The relationships are used both to validate new data, and to predict phenotypic outcomes based on available data. The prediction may relate to a clinical outcome in response to a proposed intervention by a caregiver. The statistical models may be inhaled into the system from electronic publications that define statistical models and methods for training those models, according to a standardized template. Methods are described for selecting, creating and training the statistical models to operate on genetic, phenotypic and clinical data, in particular for underdetermined data sets that are typical of genetic information. The disclosure also describes how security of the data is maintained by means of a robust security architecture, and robust user authentication such as biometric authentication, combined with application-level and data-level access privileges.

A digital computer and method for processing data indicative of images of facial and head movements of a subject to recognize at least one of said movements and to determine at least one mental state of said subject is provided. The outputting instructions for providing to a user information relating to at least one said mental state. A further processing data reflective of input from a user, and based at least in part on said input, confirming or modifying said determination and generating with a transducer an output of humanly perceptible stimuli indicative of said at least one mental state.

A technique is provided for enhancing performance of computer-assisted data operating algorithms in a medical context. Datasets are compiled and accessed, which may include data from a wide range of resources, including controllable and prescribable resources, such as imaging systems. The datasets are analyzed by a human expert or medical professional, and the algorithms are modified based upon feedback from the human expert or professional. Modifications may be made to a wide range of algorithms and based upon a wide range of data, such as available from an integrated knowledge base. Modifications may be made in sub-modules of the algorithms providing enhanced functionality. Modifications may also be made on various bases, including patient-specific changes, population-specific changes, feature-specific changes, and so forth.

Adaptive system for medical monitoring distributes data processing among computing devices connected to a network to optimize usage of computational resources, network communication speed and user experience. Data processing is distributed into several levels with bi-directional communication between the levels (computing devices) to coordinate and adjust data compression, filtering, and analysis, as well as the size of buffered data available for transmission and / or receiving.

A medical digital expert system to predict a patient's response to a variety of treatments (using pre-treatment information) is described. The system utilizes data fusion, advanced signal / information processing and machine learning / inference methodologies and technologies to integrate and explore diverse sets of attributes, parameters and information that are available to select the optimal treatment choice for an individual or for a subset of individuals suffering from any illness or disease including psychiatric, mental or neurological disorders and illnesses. The methodology and system can also be used to determine or confirm medical diagnosis, estimate the level, index, severity or critical medical parameters of the illness or condition, or provide a list of likely diagnoses for an individual suffering / experiencing any illness, disorder or condition.

Digitized image data are input into a processor where a detection component identifies the areas (objects) of particular interest in the image and, by segmentation, separates those objects from the background. A feature extraction component formulates numerical values relevant to the classification task from the segmented objects. Results of the preceding analysis steps are input into a trained learning machine classifier which produces an output which may consist of an index discriminating between two possible diagnoses, or some other output in the desired output format. In one embodiment, digitized image data are input into a plurality of subsystems, each subsystem having one or more support vector machines. Pre-processing may include the use of known transformations which facilitate extraction of the useful data. Each subsystem analyzes the data relevant to a different feature or characteristic found within the image. Once each subsystem completes its analysis and classification, the output for all subsystems is input into an overall support vector machine analyzer which combines the data to make a diagnosis, decision or other action which utilizes the knowledge obtained from the image.

Some of these new methods harness distributed computing capabilities to integrate biometric abstraction processes, wireless network connectivity, and specialized software for mediation, to produce a unified result set. A mediator that enables rapid health care support is used in a computer system having a database of information to be shared with authorized requesters, under privacy rules or other pre-defined constraints. The methods, using specialized software for mediation, are preferably enabled to process a securely transmitted remote data request as a query to verify or identify an individual, whether it is sent via a network that is wireless in any part, or not. Upon receiving a remote request, typically sent by a requesting application other than one operated by the individual who is the subject of the request, the mediator uses the individual's transformed biometric data of the request to rapidly advance query resolution, under all query preprocessing rules, and query results post-processing rules, that apply.

The present invention generally relates to methods and systems for (i) skin assessment based on the utilization of bioimpedance and fractional calculus and implementation of methods for skin hydration assessment based on the utilization of bioimpedance and fractional calculus and systems thereof, (ii) an Opto-Magnetic method based on RGB and gray images data as “cone-rods” principles with enhanced qualitative and quantitative parameters for analyzing water based on Opto-Magnetic properties of light-matter interaction and systems thereof, and (iii) imaging and analyzing skin based on the interaction between matter and electromagnetic radiation and implementation of an Opto-Magnetic method with enhanced qualitative and quantitative parameters for imaging and analyzing skin based on Opto-Magnetic properties of light-matter interaction and systems thereof.

A method and system for utilizing neurophysiologic information obtained by techniques such as quantitative electroencephalography (QEEG), electrode recordings, MRI in appropriately matching patients with therapeutic entities is disclosed. The present invention enables utilization of neurophysiologic information, notwithstanding its weak correlation with extant diagnostic schemes for mental disorders, for safer and expeditious treatment for mental disorders, discovering new applications for therapeutic entities, improved testing of candidate therapeutic entities, inferring the presence or absence of a desirable response to a treatment, and deducing the mode of action of one or more therapeutic entities. In particular, methods for effectively comparing neurophysiologic information relative to a reference set are disclosed along with database-based tools for deducing therapeutic entity actions on particular patients such that these tools are readily accessible to remote users.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

The availability of high quality imagers on smartphones and other portable devices facilitates creation of a large, crowd-sourced, image reference library that depicts skin rashes and other dermatological conditions. Some of the images are uploaded with, or later annotated with, associated diagnoses or other information (e.g., “this rash went away when I stopped drinking milk”). A user uploads a new image of an unknown skin condition to the library. Image analysis techniques are employed to identify salient similarities between features of the uploaded image, and features of images in this reference library. Given the large dataset, statistically relevant correlations emerge that identify to the user certain diagnoses that may be considered, other diagnoses that may likely be ruled-out, and / or anecdotal information about similar skin conditions from other users. Similar arrangements can employ audio and / or other physiologically-derived signals. A great variety of other features and arrangements are also detailed.

An integrated medical software system with embedded transcription functionality is disclosed. The system comprises a clinical module for capturing clinical data for a patient in a first electronic document and a communication component that communicates the clinical data to a rule-based clinical decision support (CDS) system and receives at least one of an alert, warning, reminder, and recommendation back from the CDS system based on the clinical data. The CDS system is configured to compare the clinical data against a knowledge base to identify the at least one of an alert, warning, reminder, and recommendation; the clinical data is serialized into a standardized database language and placed into a first electronic clinical document defined by a clinical document exchange standard before being communicated to the CDS system; and the at least one of an alert, warning, reminder, and recommendation is provided in a second electronic clinical document defined by the clinical document exchange standard when received back from the CDS system.

Devices and methods are disclosed that treat a medical condition, such as migraine headache, by electrically stimulating a nerve noninvasively, which may be a vagus nerve situated within a patient's neck. Preferred embodiments allow a patient to self-treat his or her condition. Disclosed methods assure that the device is being positioned correctly on the neck and that the amplitude and other parameters of the stimulation actually stimulate the vagus nerve with a therapeutic waveform. Those methods comprise measuring properties of the patient's larynx, pupil diameters, blood flow within an eye, electrodermal activity and / or heart rate variability.

A computerized system for monitoring and maintaining the health of a person comprising the steps of (a) obtaining parameter data from the patient and inputting the parameter data into a computer database; (b) analyzing the parameter data using a computerized statistical modeling technique module and a computerized adaptive expert system shell for the prediction of a health event in the lifetime of the patient; (c) using the analyzed data to developed a health maintenance schedule for the patient, and (d) embedding and / or linking such data onto a microprocessor powered smart card.

Systems and methods for determining ICP based on parameters that can be measured using non-invasive or minimally invasive techniques are provided, wherein a non-linear relationship is used to determine ICP based on one or more variable inputs. The first variable input relates to one or more properties of a cranial blood vessel and / or blood flow, such as acoustic backscatter from an acoustic transducer having a focus trained on a cranial blood vessel, flow velocity in a cranial blood vessel, and the like. Additional variables, such as arterial blood pressure (ABP), may be used in combination with a first variable input relating to one or more properties of a cranial blood vessel, such as flow velocity of the middle cerebral artery (MCA) to derive ICP using a non-linear relationship. Methods and systems for locating target areas based on their acoustic properties and for acoustic scanning of an area, identification of a target area of interest based on acoustic properties, and automated focusing of an acoustic source and / or detector on a desired target area are also provided. Acoustic transducer assemblies are described.

Described herein are methods and systems useful for characterizing clinical outcomes of a subject. Provided herein includes computer-assessed methods, medical information systems, and computer-readable instructions that can aid an end-user in diagnosis, prognosis, and treatment of a clinical outcome.

The present invention relates to systems and methods for monitoring patient support exiting and initiating a response. Movement data is accessed from sensors (e.g., cameras) that are monitoring a patient resting on a support platform. A motion capture pattern summary is generated from the accessed movement data. The motion capture pattern summary is compared to one or more movement pattern data sets in a library of movement pattern data sets. It is determined that the motion capture pattern summary is sufficiently similar to one of the one or more movement pattern data sets in the library of movement pattern data sets. From the determined similarity it is determined that the patient is attempting to exit the support platform. Remedial measures are initiated to prevent the detected platform support exiting attempt.

Data from a plurality of sensors representing a patient's condition, including measurement signals and also secondary parameters derived from the measurement signals, are displayed in a simple way by calculating a novelty index constituting a one-dimensional visualization space. The novelty index is based on the distance of the current data point in a multi-dimensional measurement space, whose coordinates are defined by the values of the measurement signals and secondary parameters, from a predefined normal point. This may be achieved by using a suitably trained artificial neural network to sum the distance between the current data point in the measurement space and a plurality of prototype points representing normality.

One embodiment discloses a computerized method of facilitating cardiac intervention. The method may include inputting patient data and creating a computerized interactive model of a heart based on the patient data. The model may include features. The model may simulate at least one proposed cardiac intervention by adding or deleting features to the model, and determining the effects of the proposed cardiac simulation upon the entire model. Simulations may be repeated to allow the user to determine an optimal cardiac intervention. A template and / or patient specific instrument may be created from the model to use as a guide during the cardiac intervention.

The invention describes a process for determining a biological state through the discovery and analysis of hidden or non-obvious, discriminatory biological data patterns. The biological data can be from health data, clinical data, or from a biological sample, (e.g., a biological sample from a human, e.g., serum, blood, saliva, plasma, nipple aspirants, synovial fluids, cerebrospinal fluids, sweat, urine, fecal matter, tears, bronchial lavage, swabbings, needle aspirantas, semen, vaginal fluids, pre-ejaculate.), etc. which is analyzed to determine the biological state of the donor. The biological state can be a pathologic diagnosis, toxicity state, efficacy of a drug, prognosis of a disease, etc. Specifically, the invention concerns processes that discover hidden discriminatory biological data patterns (e.g., patterns of protein expression in a serum sample that classify the biological state of an organ) that describe biological states.