73 results about "Clinical decision making" patented technology

The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.

A method and system are provided for facilitating decision making, such as in a clinical setting. In accordance with this technique, a set of encoded guidelines are executed to identify information that may be used to generate patient management options. The information is acquired, if available. Based on the encoded guidelines and the acquired information, a set of patient management options are generated and provided to one or more reviewers for review and selection of a patient management option.

A system processes medical information of multiple patients to provide data for use in supporting clinical decision making. The system includes an interface for receiving, from a remote source, data representing patient non-specific medical information excluding patient specific information. The patient non-specific medical information comprises medical information of multiple individual patients treated at one or more healthcare organization sites. A data processor automatically, parses the received data, identifies, within the parsed received data, site specific information associated with a particular healthcare provider organization site and facilitates identification of the particular healthcare provider organization site. The data processor removes the identified site specific information from the data representing patient non-specific medical information to provide both patient and site non-specific, patient medical information representative data and stores both the patient and site non-specific, patient medical information representative data in a repository.

A handheld device, such as a personal digital assistant ("PDA"), can be used at the point-of-care to find an appropriate pair of diagnosis code and procedure code for use in writing an order for further medical procedures for a particular patient. The choice of diagnosis code and procedure code can be checked for conformance with the requirements set forth in a particular set of medical necessity policy rules. In a preferred embodiment, the codes and rules are aggregated by medical specialty so that a specialist can work with solely those codes and rules that are relevant to that particular medical specialty. This abstract is provided as a tool for those searching for patents, and not as a limitation on the scope of the claims.

The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.

A highly-usable multidimensional data analysis method for performing interactive analysis on, for example, medical / administrative data stored in a hospital information system to support knowledge discovery about clinical decision-making is proposed. A multidimensional data analysis apparatus (200) includes: a database (201) separately holding an interval table I indicating intervals and a hierarchy table T indicating a hierarchical structure of each dimension of multidimensional data; an interval selection operation unit (202c) selecting an interval I′ having a user-requested property c from the interval table I, using an interval selection operation g; a join operation unit (202b) joining a set of intervals with a join operation (3 in the interval selected by the interval selection operation unit (202c), using the join operation β; and an aggregation operation unit (202a) generating a multidimensional cube from a result in the join operation unit (202b), using an aggregation operation α.

A system, method, and computer-readable media are provided for facilitating clinical decision making, and in particular, decision making based on a third party's clinical situation by determining and providing useful, up-to-date information, such as patient-related information to a decision maker. In one embodiment, a user first identifies an information item concerning a patient. Based on that item, a set of related information items is determined and prioritized, and a reference pointer, which identifies the set of related information, is generated. The reference pointer is communicated to the user's mobile device. Subsequently, the user's mobile device requests information from the set of information items associated with the reference pointer, and provides information authorization information. Following authentication of the user's credentials, updates of information from the set of information items is communicated to the user's mobile device as they become available.

Systems and methods for optimizing the decision to perform additional clinical testing are provided. A model of cutoff values, associated with the initial clinical test and representing a tradeoff between a plurality of factors, is generated. Each of the cutoff values define a boundary within a range of results of the initial clinical test delineating results that provide a medical evaluation and results that do not provide the medical evaluation. At least one optimized cutoff value associated with the initial clinical test is determined from the cutoff values by optimizing the model based on the tradeoff between the plurality of factors. It is determined whether to perform the additional clinical test based on a result of the initial clinical test performed on the patient and the at least one optimized cutoff value.

The present invention provides a unique clinical workflow, scoring system, and system architecture for sleep specific applications. The present invention provides a unique logic system, identifying factors of sleep disorders and a sleepiness scale. The present invention comprises a logic system that incorporates the patient's i) medical history, ii) clinical evaluation, and iii) diagnostic testing data for directing patient care and clinical decision making. The system of the present invention incorporates new scoring systems to drive diagnostic testing and decision making. The present invention can also suggest and drive treatment options based on evidence based medicine, society guidelines, standards, consensus statements and clinical expertise.

The invention discloses a multi-center knowledge graph joint decision support method and system, which utilizes a medical knowledge graph technology and a block chain technology to realize local semantic reasoning and on-chain result summarizing of clinical data through a mode of combining a local knowledge graph and an on-chain synchronous graph, and therefore, under the condition that original medical data is not out of a hospital, interpretable clinical decision support containing complete clinical evidences of the patient is given based on deductive reasoning and evidence-based medicine by integrating fragmented cross-institution medical data of the patient through the knowledge graph technology. Patient identity information is subjected to anonymous comparison, complete encryption of data in an out-of-hospital link and a matching link is guaranteed, triple node structure information is subjected to coding mapping and asymmetric encryption, the safety of the data in the transmission process is guaranteed, only a participating center with permission can decrypt the data, and the data security and privacy guarantee in the multi-center joint reasoning process are effectively ensured.

The invention relates to the technical field of medical auxiliary recognition and relates to a focus recognition method for ischemic stroke magnetic resonance images. The method comprises an image input step, a DWI image processing step, an ASL image processing step, an infusion calculation step, a low perfusion calculation step, a DWI three-dimensional reconstruction step, an ASL three-dimensional reconstruction step, and an ischemia semi-dark zone identification step and the like. In the DWI image processing step and the ASL image processing step, a morphological method is adopted to enhance the characteristics of the image. Focus segmentation and direct volume calculation are realized based on a segmentation method of an adaptive threshold value. Ischemia semi-dark band recognition is realized through an image registration algorithm, and then a Mismatch value is calculated. The cerebral arterial thrombosis lesion position can be accurately recognized, a specific lesion volume value is provided, three-dimensional visual data display is provided, an accurate reference basis is provided for diagnosis and treatment of doctors, and clinical decision making of the doctors is assisted.

Methods are provided for validating theoretical improvements in the decision-support processes facilitating surveillance and monitoring of a patient's risk for developing a particular disease or condition and detecting the disease or condition. Patient information is received from a source and populated into an active risk assessment that monitors the patient's risk for developing Sepsis. At least a first and second set of actionable criteria for determining a patient's risk for developing sepsis are received. For each set of actionable criteria, it is determined that actionable criteria have been met. In some embodiments, software agents, operating in a multi-agent computing platform, perform each determination of whether actionable criteria are met. In some embodiments, in response to actionable criteria being met, a notification or alert is provided, and in some embodiments the results of the determinations of each set of actionable criteria are provided to facilitate validation of theoretical improvements.

The invention relates to a medical index missing data complementing method for patients with peptic ulcer. According to the method of the invention, an original sparse matrix is decomposed into the product of two matrices with a correlation relation; the drawbacks of a method which complements missing data with only one dimension considered in the prior art can be eliminated; not only is the dimension of correlation relationships between patients considered, but also the dimension of the correlation relationships between time series is considered; a matrix decomposition method is adopted to complement the missing data of the measurement indexes of the patients with peptic ulcer within one year; and therefore, the accuracy of data is improved, references can be provided for the clinical decision-making of doctors, the conditions of the patients can be timely monitored through completed medical data, and the cure rate of the patients can be improved.

The present invention involves a system and method of providing decision support for assisting medical treatment decision-making. A patient agent software module processes information about a particular patient. A doctor agent software module processes information about a health status of a particular patient, beliefs relating to patient treatments, and the actual effects of treatment decisions. By filtering information over time from the patient agent into the doctor agent, a plurality of decision-outcome nodes are created and formed into a patient-specific outcome tree with the plurality of decision-outcome nodes. An optimal treatment is determined by evaluating the plurality of decision-outcome nodes with a cost per unit change function to output the optimal treatment. When additional information is available from at least one of the patient agent and the doctor agent, the filtering, creating, and determining steps are repeated thus allowing for the system to “reason over time”, continuously updating and learning as new information is received.

The invention provides an image omics analysis model construction method. The image omics analysis model construction method comprises the steps that image data of different types of patients before and after treatment are acquired; Dividing a region of interest from the image data; Carrying out image feature extraction on regions of interest in the image data before and after treatment respectively; And establishing an image omics analysis model based on the extracted image features. According to the method, the image omics model developed by the pre-processed and post-processed CT data is incorporated in a non-invasive manner, and the column chart easy to use can predict the profit degrees of different clinical treatment modes of the non-small cell lung cancer patient in a personalized manner, so that an effective tool is provided for clinical decision making.

The invention belongs to the field of RCT (randomized controlled trial), and relates to a real-data-based simulative RCT analysis method. The analysis method is a data analysis method for comparative effectiveness researches, is used for analyzing result data generated by different interference, and comprises the steps of database creation, randomized grouping, statistical analysis, result obtaining and the like. The analysis method is used for performing comparative effectiveness analysis on clinically generated real data, confounding factors can be effectively controlled, an analysis result is high in extensionality, requirements on the sample size are low, and a plurality of result indexes can be simultaneously analyzed; the analysis method can be used for comparing the effectiveness and security of different clinical intervention methods or strategies, and new evidences for evidence-based medicine can be provided for clinical decision making.

Methods are provided for validating theoretical improvements in the decision-support processes facilitating surveillance and monitoring of a patient's risk for developing a particular disease or condition and detecting the disease or condition. Patient information is received from a source and populated into an active risk assessment that monitors the patient's risk for developing Sepsis. At least a first and second set of actionable criteria for determining a patient's risk for developing sepsis are received. For each set of actionable criteria, it is determined that actionable criteria have been met. In some embodiments, software agents, operating in a multi-agent computing platform, perform each determination of whether actionable criteria are met. In some embodiments, in response to actionable criteria being met, a notification or alert is provided, and in some embodiments the results of the determinations of each set of actionable criteria are provided to facilitate validation of theoretical improvements.

The invention discloses a stress injury clinical decision support system design method based on a knowledge base. The method is characterized in that a clinical message event is triggered, a user inputs symptom information to a system through a man-machine interaction mode, and sends the clinical message event to the knowledge inference engine of a system server end through network transmission, and the engine monitors in real time and accepts the clinical message event of a system client; the knowledge inference engine simply processes a received clinical message and then transmits to an interpreter so as to carry out the interpretation, the analysis, and the validity verification of the message; then, the engine sends the clinical message to an inference engine for clinical logical reasoning; during the period of the clinical decision-making reasoning work of the knowledge inference engine, knowledge and rules in a diagnosis and treatment rule base and a comprehensive database are organically called to complete the clinical diagnosis and treatment knowledge reasoning; and after the inference engine completes the reasoning, a result is returned to the client of a clinical diagnosis and treatment decision support system so that a clinician can take as a reference. Auxiliary diagnosis can be rapidly and accurately performed, subjectivity is effectively avoided and quality is high.

The invention discloses a lymphoma clinical decision-making, teaching and scientific research auxiliary support system and method. According to the system and method, a medical and pharmaceutical data storage unit, a diagnosis and treatment parameter storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit and an early warning and operation recording unit are adopted, feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support can be provided for related medical workers according to patient disease specificity information parameter values,the whole disease cycle of screening diagnosis, treatment, monitoring follow-up visit, out-of-hospital management and the like is covered, and doctors are assisted to quickly improve the clinical diagnosis and treatment level, learn and update diagnosis and treatment knowledge, master the research status and development direction of clinical research and real-world diagnosis and treatment, improve the scientific research level and solve the contradiction that time and energy of medical workers are difficult to follow the clinical research progress.

The invention relates to an intelligent decision-making and quality control system for continuous renal replacement therapy, which is characterized by comprising a CRRT clinical decision-making model, an MOVIE index tracking unit and a CRRT prescription adjusting unit. According to the method, the specific characteristic variables influencing the CRRT are obtained, the CRRT clinical decision-making model is established by adopting the artificial intelligence algorithm based on the characteristic variables, and the CRRT clinical decision-making model outputs the initial CRRT treatment scheme which is adaptive to the patient based on the data of the patient and has the curative effect according to the historical data. Therefore, the problem that the formulation of the conventional CRRT treatment scheme depends on clinical experience of doctors is solved, and the problem that the conventional CRRT treatment scheme is unchanged in the whole treatment process, so that the treatment effect is affected is also solved. In addition, the practical situations of shortage of doctors, uneven diagnosis and treatment levels, lack of confidence in CRRT application and the like can be relieved, and the ability of managing critical AKI patients is improved.

The invention provides an early prediction system for predicting the severity of an acute pancreatitis patient, and belongs to the field of prediction models. The prediction system is constructed by taking the age, respiratory rate, albumin, lactic dehydrogenase LDH, oxygen support and pleural effusion of a patient with acute pancreatitis as prediction indexes. The prediction system is simple in construction method and high in prediction accuracy and distinction degree, can accurately judge the early severity of acute pancreatitis patients, accurately judges the probability that the patients develop into severe acute pancreatitis, helps clinicians to obtain the maximum income when making clinical decisions, helps guide the clinicians to make individual treatment decisions, improves the survival rate of patients and is wide in application prospect.

A method and system are provided for facilitating decision making, such as in a clinical setting. In accordance with this technique, a set of encoded guidelines are executed to identify information that may be used to generate patient management options. The information is acquired, if available. Based on the encoded guidelines and the acquired information, a set of patient management options are generated and provided to one or more reviewers for review and selection of a patient management option.

The invention discloses an esophageal cancer clinical decision-making, teaching and scientific research auxiliary support system and method. According to the system and method, a medical and pharmaceutical data storage unit, a diagnosis and treatment parameter storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit, and an early warning and operation recording unit are adopted, feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support can be provided for related medical workers according to patient disease specificity information parameter values, the whole disease cycle of screening diagnosis, treatment, monitoring follow-up visit, out-of-hospital management and the like is covered, and doctors are assisted to quickly improve the clinical diagnosis and treatment level, learn and update diagnosis and treatment knowledge, master the research status and development direction of clinical research and real-world diagnosis and treatment, and improve the scientific research level, thereby solving the contradiction that time and energy of medical workers are difficult to follow the clinical research progress.

The invention discloses a parturition navigation curve generation method. The method comprises the following steps that coordinates of the upper margin and lower margin of the pubic symphysis on an ultrasound image are obtained; according to a predictive constraint relationship, coordinates of guiding points on the ultrasound image are calculated in sequence; the guiding points on an ultrasonic scanning plane are connected in sequence to generate a parturition navigation curve. The invention discloses a fetal head position calculating method. The method comprises the following steps that the parturition navigation curve is generated on the ultrasound image; scale values of the guiding points are distributed in sequence; in the ultrasound image, the scale corresponding to the intersection point of the outline of the fetal head and a delivery guiding curve is the position of the fetal head. The invention also provides a fetal head position calculating device. The device is characterizedby comprising an ultrasonic imaging acquiring module, a feature point marking module and a data processing module. The fetal head position calculating method and device can more conveniently and objectively calculate the position of the fetal head and provide a basis for clinical decision-making.

A system (100) includes a processor (102) that executes a clinical decision support application (202). The system further includes a module store (1 12) that includes one or more decision support modules (206) for use by the CDS application. The application includes one or more interfaces (204) that provide at least one interface between the CDS application and the one or more decision support modules. The one or more interfaces includes an interface that supports one or more newly added specific decision support modules, and the system is extendable by adding the one or more specific decision support modules.

The invention relates to a clinical decision-making auxiliary method and system. The method comprises the following steps: extracting clinical manifestation features and animal information features; according to the clinical manifestation characteristics and the animal information characteristics, obtaining at least one suspected disease and a suspected probability corresponding to each suspected disease through a disease prediction model; generating recommended examination items according to the clinical manifestation features, the animal information features and the suspected diseases, wherein the suspected diseases and the suspected probabilities corresponding to the updated suspected diseases are updated through the disease prediction model according to the obtained examination data of the at least one recommended examination item; the recommended examination items are updated according to the updated suspected diseases, and the suspected diseases are updated according to examination data of the updated recommended examination items until predicted diseases for selection are obtained from the suspected diseases. According to the scheme provided by the invention, the diagnosis and treatment efficiency and accuracy of the user can be improved, and the medical accident risk is reduced; meanwhile, the treatment cost of a pet owner is saved, and unnecessary inspection on the pet is reduced.

The invention discloses a head and neck tumor clinical decision-making, teaching and scientific research auxiliary support system and method. According to the system and method, a medical and pharmaceutical data storage unit, a diagnosis and treatment parameter storage unit, an information input unit, a comparison processor unit, a diagnosis and treatment result output unit and an early warning and operation recording unit are adopted, feasible clinical diagnosis and treatment scheme suggestions and corresponding clinical research and real world research evidence support can be provided for related medical workersaccording to patient disease specificity information parameter values,the whole disease cycle of screening diagnosis, treatment, monitoring follow-up visit, out-of-hospital management and the like is covered, and doctors are assisted to quickly improve the clinical diagnosis and treatment level, learn and update diagnosis and treatment knowledge, master the research status and development direction of clinical research and real-world diagnosis and treatment, improve the scientific research level and solve the contradiction that time and energy of medical workers are difficult to follow the clinical research progress.