5 results about "Medical advice" patented technology

This invention relates to the field of biological data processing technology, and in particular to an artificial intelligence-based intelligent fetal heart rate monitoring and risk warning system. The system collects multidimensional fetal heart rate information and performs noise reduction and quality assessment on this information using a data quality evaluation module. It also constructs a spatial-temporal heart rate feature fusion model using an intelligent heart rate monitoring module, obtains a medical advice level based on the fetal heart rate feature vector using a heart rate risk warning module, and provides tiered medical advice warnings. Furthermore, the system updates the collection frequency based on the heart rate pattern status using a heart rate monitoring calibration module, thereby improving the accuracy and stability of fetal heart rate monitoring at the data level.

A system for generation of medical recommendation based on patient-related data, including a processor of a medical recommendations server (MRS) node configured to host a machine learning (ML) module and connected to a patient entity node and to at least one medical emergency entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sensory data from a plurality of biosensors encapsulated into a patient wearable device; acquire patient data from a mobile device of the patient, wherein the patient data may include video data and audio data; parse the sensory data to derive a plurality of key classifying features; extract a plurality of indicators from the patient data based on the plurality of key classifying features; query a local patients'database to retrieve local historical patients'-related data related to previous patients'engagements associated with previous medical recommendations based on the plurality of key classifying features and the plurality of indicators; generate at least one feature vector based on the plurality of key classifying features and the plurality of indicators and the local historical patients'-related data; and provide the feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of medical recommendation parameters from a medical recommendation predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate medical recommendations based on the plurality of the medical recommendation parameters.

The present application relates to the field of artificial intelligence and medical information processing technology, in particular to a dialect medical question and answer method based on multi-modal base model adaptation, which comprises converting original dialect signals into recognized text through a voice model enhanced by low-resource data and optimized parameters; then analyzing the intent and entity, and generating candidate replies and their confidence scores based on the dialogue state; through comparing the scores with the preset threshold, realizing double-track intelligent switching. When the bottom link is triggered, the system retrieves knowledge fragments from the medical knowledge base combined with the context, and injects them into a large language model fine-tuned by medical reasoning chain and consultation mixed data to generate text replies. Finally, the replies after security review are converted into matching dialect voice output. The present application effectively solves the shortcomings of traditional medical question and answer systems in dialect recognition, complex medical reasoning and long-tail consultation processing, and significantly improves the professionalism and controllability of medical advice through RAG retrieval enhancement and double-track collaborative strategy.

The application discloses a personalized nursing plan automatic generation method based on standardized nursing grades and health records, and relates to the technical field of intelligent medical treatment. A deep embedding clustering network is used to process multiple source health records of patients, a dynamic record-grade basic correspondence relationship is constructed, and a random forest algorithm is used to mine non-explicit information such as behavior track and missed medical advice, so as to identify key features and implicit nursing needs. The change of the key features is continuously monitored, the corresponding relationship is updated through incremental learning, and an enhanced index record mapping model is constructed, the change of the disease condition is monitored in real time, and an adjustment trigger signal is automatically generated. An adaptive optimization algorithm is used to iteratively fine-tune the nursing plan parameters, and the correspondence relationship between the needs and the nursing components is continuously optimized through a matching degree evaluation and a feature re-clustering mechanism, so that a closed-loop adaptive intelligent engine is formed, and dynamic personalized management and continuous adaptation of the whole life cycle of the nursing plan are realized.