6 results about "Intervention measures" patented technology

The invention discloses a depression auxiliary evaluation method based on a large language model agent. The depression auxiliary evaluation method is characterized by comprising the following steps: a) performing structured processing on a Hamilton depression scale; b) constructing a dynamic questioning agent based on a large language model, and realizing dynamic selection and adjustment of theme questions; c) constructing a multi-dimensional scoring agent to obtain sub-item scores of the depressive symptoms; and d) constructing a clinical decision-making agent, generating risk early warning, giving personalized diagnosis and treatment and intervention suggestions and the like. Compared with the prior art, the method has the advantages that real-time interaction in a natural language form is realized, a real interview process is simulated, the method is closer to psychological consultation practice, user experience and evaluation reliability and validity are enhanced, user language behaviors and scoring results can be analyzed in real time, high-risk signals such as self-injury tendency and severe depression can be identified, and the method is suitable for popularization and application. Personalized diagnosis and treatment suggestions and intervention measures are output, intelligent assistance is provided for clinical decision making, and the intelligent level of mental health screening and management is improved.

The invention provides a decision supervision support method based on evidence-based medicine knowledge management, and relates to the technical field of evidence-based medicine, and the method comprises the following steps: obtaining an evidence set for describing an entity relationship between exposure factors and / or intervention measures and an outcome index, using the evidence set to construct a first entity relationship graph as quantitative representation of the entity relationship; when the new evidence appears, updating the edge weight according to the quality score of the new evidence by adopting an incremental learning algorithm, and generating a second entity relation graph; the hospital system uses the second entity relation graph to carry out diagnosis and treatment to obtain real-time clinical feedback information; and adjusting the edge weight of the second entity relation graph according to real-time clinical feedback information of the multi-party hospital system to obtain a comprehensive decision support quantitative relation. According to the method, the problem that the medical decision supervision support is lagged due to the fact that the relation between the exposure factor and / or the intervention measure and the outcome index is difficult to dynamically update according to the retrieved new evidence in the prior art is solved.

The invention discloses a nerve-mediated syncope risk prediction method and system, and relates to the technical field of medical evaluation.The method comprises the steps that firstly, units are divided according to patient identity types and syncope scenes in a two-dimensional mode, and then a scene-syncope association model is constructed according to historical syncope records; through multi-dimensional data acquisition, matching of inducement features corresponding to user identity types, locking of associated suspicious syncope scenes, prediction of the risk of user syncope, when the risk exists, intervention measures are provided for the user, the intervention condition is monitored, and corresponding feedback is carried out at the same time, the method can identify multiple syncope scenes and multiple identity types, and the safety of the user is improved. The collaborative risk is quantified, the accuracy of risk level judgment is guaranteed, the prediction precision is greatly improved, early-stage and high-accuracy risk prediction is achieved, scene-identity-intervention association rules are established, the individuation and pertinence of intervention measures are improved, the intervention effect is guaranteed, and therefore the safety of a user is improved.

The present application belongs to the technical field of pressure injury, and particularly relates to a new-born skin injury comprehensive prevention and management system. The present application generates a personalized risk threshold by fusing clinical data and multi-modal physiological parameters, avoids single risk judgment, improves the pertinence and accuracy of prediction, collects data only during a physiological steady state period, effectively reduces noise caused by disease fluctuations or interference factors, improves the stability and reliability of risk parameter calculation, comprehensively automatically executes a risk assessment process, triggers a graded early warning and intervention push in real time when the score exceeds the threshold, greatly shortens the response time from risk identification to intervention, verifies the real-time effect of the intervention measures through an intervention feedback module, realizes closed-loop management of risk discovery, intervention, verification and optimization, reduces missed alarms and invalid interventions, and automatically generates a monitoring curve and historical trends to help medical staff intuitively judge risk change trends and support long-term management and scientific research analysis.

The application discloses a VTE real-time monitoring and intelligent prevention and treatment system and belongs to the technical field of intelligent prevention and treatment, comprising: a baseline construction module, which is used for collecting multi-dimensional VTE parameters, calculating the normal fluctuation range of each parameter to form an initial individual baseline, and constructing an adaptive individual baseline through threshold calibration; a trend identification module, which is used for dynamically setting the length of a sliding window, calculating the trend slope and VTE accumulation bias of VTE, constructing trend constraints and accumulation bias constraints, and determining that there is a continuous abnormal deviation when both constraints are not met at the same time and the length of continuous deviation exceeds a certain time; a time sequence risk prediction module, which is used for calculating the deviation value of each parameter of a target patient, constructing a space-time fusion feature matrix, inputting a time sequence risk prediction model, and outputting a VTE risk probability; and an early warning intervention module, which is used for double determination intervention, setting three-level early warning and intervention measures, calculating an improvement rate to verify the prevention and control effect in real time, and realizing early identification and intelligent prevention and control of VTE.

The invention discloses a neonatal asphyxia risk prediction model construction method and device, which are applied to the field of computer models for disease prediction, and are used for acquiring clinical feature data of a neonatal asphyxia group and a healthy neonatal group, including pregnant mother information, fetus information and other information; performing single-factor analysis on clinical risk factors between the two groups of data, and screening out single-factor predictive variables; and by taking the screened variables as independent variables and taking whether suffocation occurs as dependent variables, carrying out binary Logistic regression analysis by adopting a forward stepwise method, and establishing a risk prediction model. A visual column diagram is drawn according to the prediction model, and the prediction model has good distinction degree and calibration degree through evaluation. According to the method, key independent risk factors are screened from multiple factors through a scientific statistical analysis method, a prediction model beneficial to early clinical recognition of neonatal suffocation high-risk groups is constructed, a basis is provided for timely making individual intervention measures, and the method is of great significance in preventing neonatal suffocation and improving the survival rate of neonates.