9 results about "Evidenced based" patented technology

The invention relates to a factual reasoning-based large model answer generation method and system. According to the method, after the answer draft is generated, an auditing process is carried out, firstly, a question is put forward for the answer draft, then fact checking is carried out based on the question, evidence is provided, finally, the answer draft is decided based on the evidence and the question, a deciding result is obtained, and if the deciding result comprises an accepting instruction, the current answer draft is used as a final draft; if the judgment result comprises a rejection instruction, modifying the current answer draft and re-executing the reviewing process; if the judgment result comprises a re-planning instruction, regenerating an answer draft, and performing an auditing process; and finally, optimizing the final draft, and obtaining and outputting an answer text. Compared with the prior art, the method has the advantages of remarkably improving the fact accuracy and logic preciseness of the output content and the like.

This invention presents a passive domain adaptive fundus segmentation method based on evidence representation and marginal screening. Under the condition that the source domain training images and their pixel-level annotations are inaccessible, a Student network and a Teacher network are constructed to learn segmentation of the target domain fundus image. The network outputs positive foreground evidence and negative background evidence for each segmentation channel, and constructs a pixel-level Beta distribution. Based on the Beta distribution, foreground probability, evidence margin, and evidence strength are calculated. Reliable positive pseudo-labels are obtained through joint screening, and a weighted segmentation loss is constructed by combining pseudo-label weights. Simultaneously, regularization loss and evidence strength constraint loss are introduced to optimize the Student network. Finally, the exponential moving average coefficient is dynamically adjusted based on the amount of evidence to update the Teacher network parameters. This method can effectively suppress the propagation of pseudo-label noise and improve the accuracy, stability, and cross-domain adaptability of optic cup and optic disc segmentation in the target domain fundus image.

The invention discloses a decision-making method and system based on multi-agent review. Three types of agents are established, and prior probability distribution is configured; carrying out multiple rounds of auditing interaction based on the evidence data, which comprises the following steps of: calling a conservative agent and a radical agent in parallel, and respectively generating structured arguments; performing Bayesian updating on respective prior probability distribution based on the structured discussion to obtain respective posterior probability distribution; calling a neutral agent to analyze the structured arguments, generating a conservative challenge list and a aggressive challenge list to drive the conservative agent and the aggressive agent to correct the respective structured arguments, and recalculating the respective posterior probability distribution; judging whether a preset convergence condition is met or not after each round of auditing interaction is finished; if yes, fusion processing is carried out, and fusion probability distribution used for representing the decision result is generated; if not, continuing the next round of auditing interaction; and outputting a decision result based on the fusion probability distribution or the total divergence.

The invention discloses an AI online education intelligent question and answer information processing method, and the method comprises the steps: carrying out the time alignment of a cold start interaction set, mapping the cold start interaction set into a knowledge point index, and completing the first-repair dependence binding in combination with a teaching structured constraint set to form a cold start sequence; based on the evidence group identifier, returning the evidence list, performing effective evidence screening, calculating a sample support degree index, comparing the sample support degree index with a support degree threshold value, and outputting a to-be-verified label set; obtaining a question discrimination level and a source reliability level, executing supervised learning regression to deduce and output a wrong marking risk upper bound value, comparing the upper bound value with a wrong marking threshold to determine a stable tag, and adding the stable tag into a recommendation set; an alternative label is generated for a solidification forbidding result, an interaction task set is retrieved and distinguished, a verification evidence sequence is collected, a consistency index is calculated and compared with a consistency threshold value, a pseudo-stable label mark is output, a label decision result is output, a recommendation set is updated, and the recommendation path locking risk is reduced.

This invention provides a multi-view collective medical decision-making assessment method and system based on dual-concept learning, comprising: constructing a two-level decision interpretation mechanism to obtain decision concepts; modeling individual decisions of each view, inputting the decision concepts into an evidence-based neural network to obtain evidence quantity, and quantifying individual uncertainty; performing consensus judgment on individual decisions of each view, outputting a consistent collective opinion result, or obtaining prior importance and the importance of specific instances and modeling to obtain dynamic priorities, fusing each view according to the dynamic priorities to obtain a collective viewpoint and outputting it. This invention utilizes decision trajectory backtracking to achieve high transparency and interpretability in the decision-making process; it can proactively identify and warn of high-risk decisions, significantly improving the reliability of the system; thus, while retaining the differentiated information of the views, it achieves more logically reasonable and robust collective decision-making.

Described herein are methods, systems, and techniques for tailoring an Evidence based therapy (EBT) curriculum, said system comprising: one or more processors configured to receive demographic and psychological information of a user; a machine learning algorithm trained to analyze the received information and generate a series of tool kits forming a tailored curriculum based on the user's specific therapeutic needs and understanding of psychological principles; high-value items selected to reinforce self-worth and act as learning artifacts; and a series of educational materials configured to guide the user in understanding foundational principles of Evidence based therapy (EBT) and engage in therapeutic activities; wherein the generated series of tool kits are delivered over an extended period to provide an ongoing, adaptive, and sequential user support.

The application discloses an AI online education intelligent question and answer information processing method, which aligns and maps a cold start interaction set time to a knowledge point index, combines a teaching structured constraint set to complete prerequisite dependency binding to form a cold start sequence; based on an evidence group identifier, retrieves an evidence list and performs effective evidence screening, calculates a sample support degree index and compares it with a support degree threshold to output a to-be-verified label set; obtains a question discrimination degree level and a source reliability level and performs supervised learning regression inference to output an upper bound value of a wrong label risk, compares the upper bound value with a wrong label threshold to determine a stable label and adds the stable label to a recommendation set; generates an alternative label for a forbidden solidification result, retrieves a differentiated interaction task set, collects a verification evidence sequence, calculates a consistency index and compares the consistency index with a consistency threshold to output a pseudo-stable label mark, outputs a label decision result and updates the recommendation set, and reduces a recommendation path locking risk.

The invention belongs to the technical field of evidence grouping, and particularly discloses a litigation case-oriented evidence grouping method and system, a terminal and a medium. Comprising the steps of obtaining multi-category evidences in a case, and performing content analysis on the evidences to generate uniform feature representation; performing fact element identification on the unified feature representation to obtain event features, time features and main body features, and constructing a graph structure for reflecting an association relationship between evidences; performing feature learning on the graph structure to generate a preliminary evidence grouping result; performing iterative optimization on the preliminary grouping result to obtain a stable optimized group; and reasoning and analyzing the time sequence, causal orientation and content correlation among the evidences based on the optimized grouping, and generating explanation information for representing evidence grouping logic. According to the method, multiple types of evidences in complex cases can be subjected to structured expression, association modeling and logically consistent evidence grouping, and technical support is provided for case fact carding and evidence chain construction.