6 results about "Clinical record" patented technology

This invention relates to the field of medical technology, specifically to a method for preoperative risk assessment and prediction in liver transplant patients with hepatocellular carcinoma, comprising the following steps: Sample collection: selecting plasma samples and corresponding clinicopathological information from liver transplant recipients of hepatocellular carcinoma, and clarifying the inclusion and exclusion criteria for samples; Plasma cell-free DNA extraction and whole-genome sequencing: extracting and quality-controlling cell-free DNA from the plasma samples collected in step S1, constructing a sequencing library, and performing low-coverage whole-genome sequencing. This invention utilizes plasma-extracted cfDNA for whole-genome sequencing, combined with clinical testing information, to construct a preoperative risk assessment and prediction model for postoperative recurrence in liver transplant recipients of hepatocellular carcinoma based on non-invasive testing. This model can be used to predict the probability of recurrence-free survival before liver transplantation. The model derivation cohort integrates clinical records and circulating tumor DNA data for preoperative recurrence risk prediction.

The invention provides an artificial intelligence-based system for generating personalized medical information through the integration of multi-modal data across pre-hospitalization, hospitalization, and post-hospitalization phases. The system employs encoder modules to process diverse input modalities, including audio recordings, video streams, biomedical images, text-based clinical records, and physiological signals. These encoded representations are integrated into a unified latent space using a large language model (LLM) trained on medical datasets comprising historical patient cases, anatomical knowledge, and treatment guidelines. The LLM enables cross-modal analysis to generate personalized outputs via decoder modules, which transform the latent space representation into actionable formats like text-based summaries, visualizations, audio explanations, and treatment suggestions. A key innovation is real-time intraoperative feedback via encoder-decoder modules detecting anatomical structures and deviations from standard protocols. The system also includes a federated learning module to aggregate model updates across medical centers while preserving patient privacy through deidentification protocols.

A hardware-anchored orchestration system for autonomous GenAI agents in clinical settings, implementable in ASIC or FPGA fabric to ensure deterministic enforcement independent of software execution layers. The system utilizes a hardware-isolated ethical supervisor—comprising a HSM or TPM—to monitor agentic workflows against human-configured safety thresholds stored in an ethical guardrail manifest in a silicon vault. Hardware-based logic gates detect statistically anomalous token-level entropy as a causal indicator of hallucination, and bias monitors evaluate equity thresholds against manifest-defined fairness indices. If a safety breach is detected, a hardwired interlock circuit asserts a non-maskable interrupt to block the agent's output before it is committed to the clinical record. The architecture supports multi-agent quorum verification and cryptographic provenance anchoring, ensuring autonomous agentic actions remain compliant with clinical regulatory standards via hardware-verified human oversight and zero-knowledge compliance verification.

The invention relates to an integrated multimodal artificial intelligence platform designed to function as an autonomous hospital system providing end-to-end health prevention, screening, diagnosis, treatment optimization, and longitudinal digital-twin-based monitoring. The platform introduces a closed-loop clinical architecture that continuously analyzes heterogeneous patient data including radiology, pathology, genomics, laboratory findings, longitudinal clinical records, wearable streams, and environmental exposures. A multimodal transformer (MT-X) generates a unified patient-specific representation, enabling high-precision diagnostic and prognostic inference. A novel Autonomous Screening Interval Generator (ASIG) dynamically determines individualized screening schedules based on calibrated risk models and temporal disease-evolution forecasting. A Digital Twin Engine (DTE) simulates tumor progression, metastasis probability, toxicity trajectories, and therapy response. An Adaptive Therapy Optimization Engine (ATOE), based on reinforcement learning, identifies optimal treatment strategies tailored to patient biology and system-level constraints. The invention is industrially applicable to hospitals, centers, national screening programs, tele-networks, and Al-enabled health systems. The integrated nature of the invention, the closed-loop framework, and the combination of digital-twin simulation with intelligent screening and therapy design constitute a substantial improvement beyond conventional medical Al solutions.

The application discloses an emergency trauma wound area intelligent measurement and recording system based on deep learning, relates to the field of computer vision, synchronously acquires a two-dimensional image sequence, a six-axis attitude vector and a time stamp, extracts a mask by using a segmentation network, and constructs a local curved surface geometric model in combination with prior parameters; a non-homogeneous weight compensation matrix is generated by calculating the included angle distribution of a space vector and an optical axis vector; target wound physical surface areas are acquired by performing pixel-by-pixel weighted integration on mask pixels based on the matrix, and the shrinkage caused by projection is compensated. According to the weight matrix gradient, confidence is evaluated, and dynamic acquisition guidance or structured data encapsulation is realized. The application effectively corrects non-homogeneous curved surface projection distortion, and improves measurement accuracy and the reliability of clinical records.

The invention relates to the technical field of medicine information, and discloses a system and a method for accurately selecting tacrolimus treatment dosage of a myasthenia gravis patient. The method comprises the following steps: acquiring multi-cycle administration records and corresponding blood concentration of a patient to form an initial sequence; establishing a theoretical contribution degree matrix through lagging compensation, and reversely decomposing a pure single administration concentration curve; matching the curve with an individual pharmacokinetic template, and identifying an abnormal metabolic segment; and associating the same-period clinical records to generate clinical metabolism association pairs, establishing a mapping relation between concentration fluctuation and clinical symptoms, and classifying abnormal drug effect modes. According to the method, the independent metabolism trajectory of single administration can be analyzed from the mixed monitoring data, and the drug metabolism dynamics and the clinical effect are accurately associated, so that a technical basis is provided for realizing prospective individualized accurate administration.