4 results about "Dose interval" patented technology

The invention discloses an anesthesia depth accurate prediction system and method based on time sequence alignment. The method comprises the steps that a cross-modal joint time sequence characteristic data matrix is acquired and generated; outputting a time sequence alignment feature sample; outputting a future advanced prediction label set; obtaining a first-stage anesthesia depth prediction model; in the first anesthesia depth prediction model training stage, a logic auditing link is introduced, and a self-adaptive online updating model is formed; starting an anti-fact dose simulation module, and outputting a candidate anti-fact administration scheme set; and inputting the candidate anti-factual administration scheme set into a dynamic security constraint optimizer, rejecting non-compliant schemes by the dynamic security constraint optimizer according to single maximum dose limitation, single additional dose and administration times in a minimum administration interval, and outputting an optimal administration suggestion. According to the method, combined classification judgment is carried out on the anesthesia state grade and the change trend of each prediction step, and the sensitivity of clinical risk identification and abnormal situation early warning is effectively improved.

The invention discloses a patient historical state-based anesthetic dose simulation evaluation system, which is characterized by comprising a multi-source data integration and preoperative physiological feature quantification layer, a dose effect relationship simulation layer and a risk evaluation and recommendation output layer. The method has the following advantages and effects that the personalized pharmacokinetics-pharmacodynamics model is constructed by integrating multi-source historical data of a patient, vital sign change tracks under different anesthetic doses are simulated, the recommended dose interval is generated based on multi-objective optimization, the change from experience dependence to accurate quantification is realized, and the accuracy of the recommended dose interval is improved. The problem of inaccurate anesthetic dosage prediction caused by individual differences is effectively solved, and the safety and effectiveness of an anesthesia scheme are remarkably improved.

In the treatment of patients with tumors (especially malignant tumors), the substances phenoxythiamine (B-OT) and / or oxothiamine (OT) are administered as active ingredients concurrently with ongoing radionuclide therapy (RNT) (particularly radioimmunotherapy (RIT) and / or radiopeptide or radioligand or radiopeptide ligand therapy (RLT)). B-OT and / or OT administration is cyclical, meaning that dosing phases of B-OT and / or OT (“B-OT / OT dosing cycles”) alternate with treatment intervals (dosing intervals). During each B-OT / OT dosing cycle, B-OT and / or OT are administered at a predetermined dose (mg / kg patient body weight) for one or more consecutive days. This is followed by a treatment interval during which neither B-OT nor OT is administered, before the next B-OT / OT dosing cycle begins.

This invention relates to the field of medical information management technology, specifically to an intelligent verification method and system for vaccine administration processes. In this invention, isolated operation logs during the vaccination process are reconstructed into a complete sequence of recipient vaccination behaviors based on their inherent temporal correlation and the homology of the executing entities. A Hidden Markov Model is then used to perform in-depth analysis of this sequence, inferring the underlying vaccination stage states that conform to clinical logic from the observed specific behaviors. This elevates verification from static data checking to a dynamic process review. Furthermore, the inferred actual stage sequence is rigorously compared with the standardized process in three dimensions: stage completeness, sequential accuracy, and dose interval. Innovatively, a probability confidence level is calculated for any identified process deviations, achieving a quantitative assessment of abnormal behavior.