92 results about "Medication dose" patented technology

The invention relates to application of a combined pharmaceutical composition in preparation of a medicine for resisting transforming follicular lymphoma. Active components in the combined pharmaceutical composition comprise a first component and a second component, the first component is selected from any one or a combination of at least two of sitagliflozin, pharmaceutically acceptable salt, isomer, solvate and metabolite of sitagliflozin; and the second component is selected from any one or a combination of at least two of chidamide, pharmaceutically acceptable salts, isomers, solvates and metabolites of the chidamide. Research finds that the combined use of the two active components not only can reduce the dosage of each drug and improve the medication safety, but also has the effect of improving or treating convertible follicular lymphoma more remarkably than that of a single drug active component, and plays a role in synergistic promotion. The invention provides an effective drug combination strategy for improvement or treatment of convertible follicular lymphoma, and has very significant significance.

The invention relates to a double-active-component acute myelogenous leukemia resisting drug and application thereof. The active components of the double-active-component acute myelogenous leukemia resisting drug are composed of a first active component and a second active component, the first active component is selected from any one or a combination of at least two of simvastatin, pharmaceutically acceptable salt, isomer, solvate and metabolite of simvastatin; and the second active component is selected from any one or a combination of at least two of Selanisole, pharmaceutically acceptable salts, isomers, solvates and metabolites of the Selanisole. Research finds that combination of simvastatin and Senanisole not only can reduce the dosage of simvastatin or Senanisole and improve the medication safety, but also has the effect of improving or treating acute myelogenous leukemia more remarkably compared with single simvastatin or Senanisole, and plays a synergistic promotion effect.

The invention provides a drug dosage adjusting system and method based on blood concentration prediction, and belongs to the field of medical artificial intelligence, and the method comprises the steps: obtaining a standard structured data code based on a preset historical clinical database; performing standard structured data coding based on the predicted feature factor set to obtain a predicted feature factor set and a time point combination; constructing a classification prediction model and a regression prediction model based on the prediction feature factor set; a to-be-predicted blood sample is collected in a prediction characteristic factor set time point combination mode, and a blood concentration prediction result is obtained based on the to-be-predicted blood sample of the prediction characteristic factor set, the prediction characteristic factor set classification prediction model and the regression prediction model; and adjusting the dosage according to the blood concentration prediction result. According to the invention, individual differences are analyzed individually, personalized medication dosage adjustment suggestions are generated, and the medication dosage adjustment reliability is improved.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.

Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and / or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and / or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented.

Dose guidance systems and methods for titrating medication doses are described. The dose guidance system may receive glucose data from a continuous glucose monitor and may receive medication data related to medication administered by the user. The dose guidance system may initialize dose guidance parameters, recommend medication doses, titrate medication doses, and provide alerts based on the glucose data and medication data.

The invention relates to the technical field of medical health and artificial intelligence, and discloses an AI reminding method for a gastroenterology department enteroscope patient to take a laxative, comprising the following steps: S1, guiding the patient to enter an AI interaction interface through a two-dimensional code or an applet entry; s2, generating a personalized medication scheme according to the basic information and the real-time data input by the patient; s3, ensuring that the patient completes an intestinal tract preparation process through a staged guidance and real-time reminding function; s4, performing abnormal reaction identification according to real-time feedback data input by the patient, and adjusting subsequent operation suggestions; and S5, screening potential drug interaction through the integrated drug database, and providing a safe drug use prompt. Personalized data such as age, weight, basic diseases, daily medication and daily work and rest of a patient are collected through the personal information input module, the medication scheme generation module calls a preset algorithm to calculate the optimal medication dosage, medication time nodes are determined, and diet suggestions are generated.

The present disclosure provides a caregiver system and method for interfacing with and controlling an oral medication dispensing device. The system registers the caregiver and provides the caregiver a list of patients under the caregiver's care, as well as associated medication dose schedules for each of the one or more patients. The caregiver is provided reminders regarding doses and allowed to modify the dose schedule to generate a modified dose schedule. The modified does schedule is pushed to a patient system associated with a medication dispensing device.

The invention belongs to the technical field of intelligent medicine taking, and particularly relates to an intelligent management system for medicine taking of old people, which comprises a medicine box, a system server and a mobile terminal, by means of the structural design of the medicine box, the functions of intelligent arrangement and reminding of medicine taking plans of old people are achieved, the problems of wrong medicine taking and missing medicine taking possibly occurring when the old people take medicine are solved, it is ensured that the old people take the medicine on time according to the quantity through intelligent arrangement and reminding of the medicine taking plans of the old people, and the problems of wrong medicine taking, missing medicine taking and the like are avoided; the safety and the standardization of medicine taking of the old people are improved; through the structural design of each module contained in the mobile terminal, the functions of medicine taking compliance monitoring and excitation are realized, the enthusiasm and compliance of the old in medicine taking can be effectively improved, the medicine box records the medicine taking time and the medicine dosage through the cooperative use of the timer and the detection module, accurate monitoring is realized, and the use experience of the old is improved. Meanwhile, a certain reward is given to the old with good compliance in cooperation with the excitation module, and the enthusiasm of the old to medicine taking is improved.

The invention discloses a method for adjusting administration dosage according to adverse reaction risk based on a joint model, and is applied to the field of intelligent medical treatment. In the prior art, the relationship between the sirolimus steady-state valley concentration and the specific adverse reaction cannot be quantified, so that the defect of lack of accurate guidance for clinical medication is caused. Through systematic research, the method determines the individual target valley concentration range through a BKMR model for the first time, obtains individual pharmacokinetic parameters of a patient through a group pharmacokinetic model, and finally determines the medication time and the medication dosage of the patient through Bayesian negative feedback simulation, and the medication time and the medication dosage are used for adjusting a medication scheme. According to the invention, prospective risk prediction of adverse reactions of sirolimus is realized, the treatment safety of children with vascular diseases is remarkably improved, and long-term prognosis of patients is improved.

The invention belongs to the field of intelligent medical treatment, and particularly relates to an individualized administration method for treating a PID child patient by combining sirolimus with voriconazole. The method comprises the following steps: acquiring the age of a PID child patient; based on the age of the PID child patient, determining the dosage of sirolimus when the voriconam is combined, and if the age of the PID child patient is in a [1, 6] year interval, the dosage is [0.100, 0.330] mg / QD; if the age is in the interval of [6, 13], the dosage is [0.280, 0.540] mg / QD. According to the method, the PPK model is formulated for the first time when the PID child patient is combined with the VRC in the SRL mode, the optimal administration dosage is explored based on PPK when the PID child patient is combined with the VRC in the SRL mode, and simple medication dosage guidance is provided for child patients of different age groups; individualized and customized SRL medication guidance can be provided for individuals through a posterior Bayesian method.

The present disclosure provides a caregiver system and method for interfacing with and controlling an oral medication dispensing device. The system registers the caregiver and provides the caregiver a list of patients under the caregiver's care, as well as associated medication dose schedules for each of the one or more patients. The caregiver is provided reminders regarding doses and allowed to modify the dose schedule to generate a modified dose schedule. The modified does schedule is pushed to a patient system associated with a medication dispensing device.

The invention relates to the field of data processing, in particular to a drug safety signal mining method and system based on multi-source data. The method comprises the following steps: firstly, constructing a longitudinal event sequence containing medication and diagnosis events for each patient; calculating a time sequence correlation factor of medication and subsequent adverse events, wherein the time sequence correlation factor is in negative correlation with the time interval of the two events and the number of hybrid diagnosis events in the interval; meanwhile, based on the matching degree of the event time interval and the known incubation period probability distribution, obtaining an incubation period rationality weight; combining the drug dosage, the time sequence correlation factor and the reasonable weight of the incubation period, accumulating the related drug event influence before the occurrence of the adverse event, and calculating the accumulated correlation energy of the specific drug and the adverse event; and finally, carrying out non-proportional analysis on the accumulated associated energy of the patient group, realizing drug safety signal mining, and improving the accuracy and reliability of signal identification.

The invention discloses a bedridden patient sign measurement and medication assisting system based on multi-sensor fusion, which belongs to the technical field of medical instruments and comprises a multi-sensor fusion measurement module, a self-adaptive calibration compensation module, an intelligent evaluation decision module, a medication assisting calculation module and a closed-loop feedback optimization module. A pressure sensor array, an ultrasonic sensor and an acceleration sensor are used for cooperatively measuring the height and weight of a patient, a deformation compensation model and a body position correction model are adopted for accurate calibration, a BMI index is automatically calculated, a VTE risk score, a falling risk score and a self-care ability score are generated, and the medication dosage is intelligently recommended according to medicine characteristics and patient physique. And continuously optimizing system parameters through an incremental learning algorithm to form a measurement-evaluation-medication-optimization deep coupling closed-loop system, so that the physical sign measurement precision, clinical evaluation efficiency and medication safety of the bedridden patient are remarkably improved, and the nursing labor intensity and the medical error risk are reduced.

The utility model relates to an ointment dosing device capable of dosing quantitatively, which solves the problems that the dosage can not be stably controlled, the ointment can not be daubed uniformly and the sanitation can not be guaranteed in the existing ointment daubing mode. The device comprises a front-back through outer sleeve, a push rod is arranged in the outer sleeve in a sliding and penetrating mode, an elastic buckle is arranged on one side of the rear end of the outer sleeve, a plurality of clamping steps corresponding to the elastic buckle are arranged on the rear portion of the push rod, and a silicon rubber sleeve is arranged at the front end of the push rod in a sleeved mode. The gear of the push rod can be adjusted according to different drug dosages, quantitative drug delivery is achieved, in the drug delivery process, a very small amount of ointment adheres to the inner wall of the outer sleeve, waste is reduced, finally, the silica gel sleeve is adopted for isolation to evenly smear the ointment, and cross infection is avoided.

The invention belongs to the technical field of biological medicine, and particularly relates to a pharmaceutical composition of an HDAC6 inhibitor and a BTK inhibitor and application of the pharmaceutical composition. According to the present invention, the BTK inhibitor and the HDAC6 inhibitor are combined for use to provide the synergistic effect, such that the tumor inhibition effect is significantly enhanced, the medication dosage and the medication period can be improved, the side effect of the drug can be easily reduced, and the compliance of the patient on the drug can be easily improved.

The invention provides a risk determination method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence. In some embodiments of the invention, health state information, medication information, time biological characteristics and drug molecular map structural characteristics are input into a health risk prediction model to obtain a health risk prediction result; under the condition that the health risk prediction result is that the risk exists, determining medication adjustment information according to medication information and a medication dosage rule; sending the health risk prediction result and the medication adjustment information to an early warning terminal, so that the early warning terminal displays the risk prediction result and the medication adjustment information; according to the invention, based on the health state information, the medication information, the time biological characteristics and the drug molecular map structural characteristics of the user, the health risk prediction model is utilized to accurately predict the health risk, the health risk prediction efficiency is improved, and the health risk prediction accuracy is improved.

This invention belongs to the field of intelligent healthcare, specifically relating to a personalized dosing method for sirolimus combined with voriconazole in the treatment of PID in children. The method includes: obtaining the age of the PID child; determining the sirolimus dosage when combined with voriconazole based on the PID child's age; if the PID child's age is in the range of [1, 6) years, the dosage is [0.100, 0.330] mg / QD; if the age is in the range of [6, 13) years, the dosage is [0.280, 0.540] mg / QD. This application is the first to develop a PPK model for PID children using sirolimus combined with voriconazole (VRC), and based on the PPK, explores the optimal dosage for PID children using sirolimus combined with VRC, providing concise dosage guidance for children of different age groups; and through posterior Bayesian methods, it can provide individualized sirolimus medication guidance.

The invention relates to the technical field of drug information, in particular to a method for predicting the accurate dosage of polymyxin B for a multi-drug-resistant severe infection patient, which comprises the following steps: acquiring model establishment data, establishing a basic model, incorporating covariables, evaluating the model, performing external verification and simulating based on the model. According to the method provided by the invention, accurate prediction of medication of the polymyxin B is realized, and death of a patient with severe infection caused by anti-infection failure due to too low dosage of the polymyxin B is reduced; the situation that the health of a severe patient is seriously damaged due to serious renal toxicity and neurotoxicity side effects caused by overlarge dosage of the polymyxin B is avoided, and the method has important significance on precise medication of clinically refractory severe infection patients.

This invention relates to a highly active effective fraction of the Epiglottis-Clearing Decoction and its application in the preparation of antitussive drug compositions, belonging to the field of traditional Chinese medicine extract technology. The effective fraction contains hydroxysaffron yellow A, paeoniflorin, naringin, and neohesperidin, with the total content of these four components not less than 20% of the total mass of the effective fraction. The effective fraction of this invention is prepared by purification with macroporous resin. Compared with the freeze-dried powder of the traditional Epiglottis-Clearing Decoction, the total content of the four active ingredients is increased from 3.32% to 20.87%, achieving a more than 6-fold high-efficiency enrichment. Animal pharmacodynamic experiments show that the antitussive effect of a low dose (275.5 mg / kg) of this effective fraction is comparable to that of the freeze-dried powder of the original decoction (1928 mg / kg), reducing the dosage to about 1 / 7 of the original formula, demonstrating a significant "synergistic effect with reduced dosage" technical effect. The effective active ingredients provided by this invention have a clear content and stable quality, and can be made into modern preparations such as tablets, capsules or granules, solving the problems of large volume, inaccurate dosage and poor patient compliance of traditional decoctions.

The application relates to a zero-character full-symbol medication guidance system based on a fool-proof design, and belongs to the technical field of medical safety. The system comprises seven pure graphical symbol modules of medication frequency, medication time, medication dose, medication route, contraindication warning, adverse reaction treatment and missed medication treatment. The modules are visually distinguished by unified color, shape and line type, and are combined into a complete information chain in a fixed order of frequency, time, dose, route, contraindication, adverse reaction and missed medication, and are printed or displayed on a medicine box, a medication guidance sheet or a prescription carrier. The application migrates the picture priority and fool-proof and error-proof operation national standard system in the construction industry to a medical medication scene, fundamentally eliminates medication misreading risks caused by ambiguous characters, character ambiguity and cultural level differences, realizes the instinctive recognition and accurate execution of the whole population such as illiterates, the elderly with low vision and cognitive impairment, and has the remarkable technical effects of zero learning cost, full-process coverage and low cost and easy popularization.

The invention provides a method for predicting the dosage of an antituberculous drug, which comprises the following steps of: establishing a group pharmacokinetic model of the antituberculous drug by adopting a nonlinear mixed effect model, a two-atrioventricular model and a mixed residual model through plasma concentration analysis, and calculating the pharmacokinetic parameters of the corresponding model; and the established model is verified based on a graphical method, a visual prediction test method and a nonparametric bootstrap method, and the steps of stability and prediction capability evaluation and the like are completed. According to the anti-tuberculosis drug population pharmacokinetic model established by the invention, individual pharmacokinetic parameters can be estimated, a specific population administration scheme is optimized, the drug curative effect is improved, the adverse reaction risk is reduced, and individualized administration of the anti-tuberculosis drug is realized.

A system and method are provided for remotely supervised preparation, verification, and controlled dispensing of medication doses at geographically distributed remote dispensing units. A recipient and a supervisor are authenticated. A real-time audiovisual session is established between the recipient and at least the supervisor, and additional authenticated participants may join without authority to actuate dose release. The system receives an electronic medication order and corresponding label data originating from an external clinical information system and authorizes preparation of doses at the remote dispensing unit based on the order. Prior to any release, verification data is presented to the supervisor. A controlled mechanical transfer mechanism is actuated to release a prepared dose to the recipient only after verification and affirmative authorization by an authenticated supervisor. The disclosed systems and methods are applicable to controlled substances and other medications requiring supervised dispensing.

The invention relates to a traditional Chinese medicine preparation, in particular to application of a compound live brain soothing capsule in preparation of a medicament for treating insomnia. The dosage of the medicine is 200 mg / kg. Experiments show that the sleep improvement effect of the compound Shuonaoshu capsule on an anesthesia reviving experiment evaluation test medicine, a high-dose group (200 mg / kg) of the compound Shuonaoshu capsule can effectively prolong sleep time, dose dependence exists, sleep quality can be moderately improved, the higher the concentration is, the more obvious the improvement effect is, and the more obvious the effect is. The invention reveals that the compound Liangjinshu capsule has a treatment effect on insomnia, and is expected to provide a certain experimental basis for the development of new drugs for insomnia.

The present application relates to a kind of combined drug composition for improving or treating follicular lymphoma and its application, the active component of the combined drug composition is composed of first active component and second active component;The first active component is selected from any one or combination of at least two of ritanserin or its pharmaceutically acceptable salt, isomer, solvate, metabolite;The second active component is selected from any one or combination of at least two of donepezil or its pharmaceutically acceptable salt, isomer, solvate, metabolite.The combined drug composition has excellent improvement or treatment effect of follicular lymphoma, two active components not only can reduce the dosing of ritanserin or donepezil when inhibiting follicular lymphoma, improve drug safety, but also have more significant inhibiting effect of follicular lymphoma than using single active component, reach the effect of synergistic promotion.

The invention discloses a pregnancy individualized medication dosage prediction factor screening method, a prediction model construction method and a prediction system, and relates to the technical field of medicine. Factors which are obviously related to blood concentration and have no obvious multicollinearity among variables are screened out from factors influencing pregnancy drug dosage to serve as drug dosage prediction factors, and a corresponding prediction model and system are constructed. According to the screening method, the prediction model construction method and the prediction system provided by the invention, auxiliary decision making can be provided for individualized drug dosage during pregnancy.

The invention belongs to the technical field of biological medicine, and particularly relates to a novel medicine composition and application thereof. According to the present invention, the HDAC6 inhibitor, the ribonucleotide reductase inhibitor and the anti-CD20 antibody are combined for use to achieve the synergistic effect, such that the tumor inhibition effect is significantly enhanced, the medication dosage and the medication period can be improved, the side effect of the drug can be easily reduced, and the compliance of the patient on the drug can be easily improved.

The invention belongs to the technical field of pharmaceutical co-crystals, and particularly relates to a synthetic method and application of an amantadine-butylphthalide co-crystal. Butylphthalide reacts to obtain butylphthalide salt or butylphenyltitanic acid, the butylphthalide salt or butylphenyltitanic acid reacts with different substituted amantadine or hydrochloride thereof to synthesize the pharmaceutical co-crystal, and the co-crystal structure can adjust the crystal lattice energy of the pharmaceutical, improve the solubility of the pharmaceutical in water or physiological media, promote the rapid dissolution and absorption of the pharmaceutical in vivo, improve the bioavailability and improve the bioavailability of the pharmaceutical. According to the eutecticum, the problem of solubility of butylphthalide is solved, the formed eutecticum shows certain neuroprotective activity, the biological activity of the eutecticum is remarkably superior to that of a monomer drug, the clinical medication dosage can be reduced while the same treatment effect is achieved, and the eutecticum has certain scientific research value and industrial popularization significance.

The invention relates to the technical field of drug side reaction risk prediction, in particular to a tumor patient drug side reaction risk prediction system based on multi-source data. The system comprises a data acquisition module used for integrating multi-source time series data, including medication dosage, pharmacokinetic parameters and clinical index changes, and constructing a multi-dimensional data set; the cumulative effect analysis module is used for calculating a drug attenuation factor, a binding dose and median lethal dose quantitative cumulative effect intensity based on first-order elimination kinetics, and capturing drug metabolic dynamics; due to the fact that the interaction influence effect possibly exists between the drugs, the influence coefficient analysis module is used for adopting elastic network regression and analyzing the drug main effect and the drug combination interaction effect, and determining the main influence coefficient and the interaction influence coefficient of the drugs on each clinical index; and the risk prediction module is used for weighting and optimizing decision tree sorting through the abnormal contribution degree, and outputting the risk probability of each side reaction in combination with a random forest algorithm.