1250 results about "Medication use" patented technology

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention provides a medical drug management system and an optimization method, and relates to the technical field of medical information management, the system comprises a medicine management platform, a medicine database and an information technology support module; wherein the medicine management platform adopts a modular design and comprises a medicine inventory management module, a patient information management module, a medicine use process management module and a report statistics module, and information interaction among the modules is realized through interfaces; the medicine database is used for storing medicine information, medicine inventory information and patient medication information; the information technology support module comprises a bar code scanning module, an RFID technology module and a cloud computing module. By integrating the advanced information technology and the medical management concept, the medical drug management system which is comprehensive in function, efficient and safe is provided, the management process is optimized, the drug use safety is improved, the management cost is reduced, and personalized drug use suggestions and decision support are provided.

The invention discloses development, preparation and application of a carbapenemase immunochromatography kit, and belongs to the technical field of biological medicines. According to comparison of amino acid sequences of five common carbapenemase variants of KPC, IMP, NDM, VIM and OXA48 types, the immunogen is prepared from conserved epitope sequences obtained respectively. A mouse is immunized, and a specific antibody pair which is strong in affinity and can be combined with multiple variants is obtained through multi-platform screening. On the basis, the carbapenemase quintuplet detection kit is developed by utilizing an immunochromatography method, has the advantages of high sensitivity, good specificity, capability of covering various drug-resistant variants, capability of detecting multiple targets at one time and rapidness and convenience in detection, and provides important help for clinical auxiliary diagnosis and medication guidance.

The invention relates to the technical field of medical data processing and prediction, and discloses a chemotherapy adverse reaction prediction and intervention system based on big data. The system integrates an unstructured disease course text and structured inspection data of a patient, generates a time-series symptom event, and performs time window alignment and fusion on the time-series symptom event, a medication record and a physical sign monitoring stream to form a multi-dimensional time-series data block. The system is combined with an external medical knowledge base to construct a dynamic association network among symptoms, medicines and physiological indexes, and dynamically calculates the confidence coefficient of an adverse reaction mode according to real-time data. By using a predictive model of the timing attention mechanism, the system can output a continuous curve of patient risk over time. The system automatically matches and generates a personalized intervention instruction sequence containing specific measures and execution time windows according to key time points and modes when the risk curve exceeds a threshold value. According to the invention, dynamic and advanced early warning and accurate intervention of adverse reaction risks of chemotherapy are realized.

The invention discloses a drug use dynamic risk assessment method and system for an elderly patient, and relates to the technical field of drug use risk assessment, and the method comprises the steps: collecting static factor data and dynamic factor data of the elderly patient, carrying out the preprocessing of the collected static factor data and dynamic factor data, and generating a feature vector for risk assessment; calculating a comprehensive risk score through a preset evaluation rule based on the feature vector; the comprehensive risk score is compared with a preset risk grading threshold value, the medication compliance risk grade of the elderly patient is determined, and the system comprises a data collection module, a preprocessing module, a risk assessment module and a grading module. The comprehensiveness and timeliness of medication compliance risk assessment of elderly patients are improved, and technical support is provided for medication intervention.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention relates to the technical field of clinical medical data visualization, in particular to a visualization system for clinical internal medicine and a visualization method thereof. The method comprises the following steps: acquiring basic information of a clinical patient; according to the basic information of the clinical patient, performing medical treatment and medication event analysis, and performing triple entity relationship processing to generate patient medical record relationship map data; performing patient drug tolerance analysis based on the patient medical record relation map data to generate patient drug tolerance characteristic data; performing clinical physiological index risk probabilistic reasoning according to the drug tolerance characteristic data of the patient to generate individual clinical medication risk data; according to the individual clinical medication risk data, individual characteristic medicine dosage adjustment is carried out, medicine dosage curve visual dynamic display is carried out, and visual clinical medication response data is generated. According to the method, personalized and visual medication decision support is provided for clinicians through patient medication conditions and real-time physiological index analysis.

The invention discloses medical medicine medication auxiliary equipment, and particularly relates to the technical field of medical instruments.The medical medicine medication auxiliary equipment comprises an anti-skid handle, the lower end of the anti-skid handle is fixedly connected with an equipment shell, and a medicine squeezing structure communicating with the rear end of an inner cavity of the equipment shell is arranged on the rear portion of the inner cavity of the equipment shell; the front portion of an inner cavity of the equipment shell and the lower end of the equipment shell are jointly provided with a smearing structure, and the rear portion of the lower end of the equipment shell is rotationally connected with auxiliary wheels. According to the medical medicine medication auxiliary equipment, ointment and the external environment are sealed under the action of the shielding assembly, pollution of the external environment to medicine is reduced, meanwhile, the medicine is extruded out of the ointment tube under the action of the extrusion driving assembly and the extrusion assembly, and therefore the medicine is extruded out of the ointment tube. The smearing structure is synchronously driven to operate through transmission of the extrusion driving assembly and the smearing structure, ointment fed by the connecting base is evenly smeared to the skin of a patient, operation steps are reduced, smearing uniformity is improved, medicine residues in an ointment tube are reduced, and the utilization efficiency is improved.

The invention relates to the technical field of chronic disease information management, in particular to a chronic disease information management method which comprises the following steps: S1, acquiring physiological index data, medication record data and behavioral habit data of a patient through a multi-source data acquisition module; s2, performing feature extraction on the multi-source data by using a data analysis module to generate multi-dimensional health assessment parameters; and S3, dynamically comparing the health assessment parameters with a preset assessment value through a risk early warning module, triggering hierarchical early warning when an abnormal parameter combination is detected, realizing multi-modal data deep mining by using an intelligent analysis module, performing association analysis on wearable device sensor data, an electronic medical record and a medication compliance record, and determining whether the health assessment parameters are abnormal or not. An individualized medication portrait of the chronic disease patient is constructed, parameters such as gene detection and metabolic indexes are combined, through medicine-disease conflict detection and dose self-adaptive calculation, an accurate medication adjustment strategy and a chronic disease patient health behavior guidance scheme are generated, and individual differences are fully reflected.

The embodiment of the invention discloses a medication answering method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-answered medication question, a knowledge base associated with the medication question and a large language model which is obtained by learning at least one candidate prompt word in advance and has a medication answering function; identifying a question category to which the medication question belongs, and determining a preliminary cue word matched with the question category in the at least one candidate cue word; according to the knowledge base and the medication problem, adjusting the preliminary cue word to obtain a target cue word; and inputting the target cue word into the large language model, so as to generate a drug use answer for solving the drug use problem according to the large language model and based on the target cue word. According to the technical scheme provided by the embodiment of the invention, the knowledge base is embedded into the large language model, so that the professionality of the medication solution generated by the large language model is improved by utilizing the knowledge base, and the accurate solution of the medication problem is ensured.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

The invention provides a medication guidance generation system, and relates to the technical field of medical informatization. Comprising a knowledge base construction module used for constructing a medication guidance knowledge base; the graph term construction module is used for constructing a knowledge graph and a standard term set according to the medication guidance knowledge base; the index construction module is used for splitting the medication guidance knowledge base and constructing a vector index and a full-text index; the personal information processing module is used for acquiring personal information of a patient and preprocessing the personal information to obtain preprocessed personal information; the mixed detection module is used for performing retrieval by utilizing a knowledge graph, a vector index and a full-text index according to the preprocessed personal information to obtain a retrieval result set and obtain an intersection result according to the retrieval result set; and the generation module is used for generating medication guidance corresponding to the target patient according to the intersection result. The problems that traditional medication guidance mainly depends on experience and professional knowledge of medical staff, and is low in efficiency, prone to errors and difficult to personalize are solved.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

The invention discloses an internal medicine drug curative effect evaluation method based on big data analysis of an electronic health record, and the method comprises the steps: extracting basic health data, diagnosis and treatment time sequence data and drug intervention data from the electronic health record, and carrying out the time-space alignment to generate a dynamic feature set; subgroups are obtained based on disease typing standard hierarchical clustering, and historical data and real world data are fused through transfer learning to construct a subgroup curative effect reference matrix; collecting data after medication in real time, and generating an evaluation vector containing short-term physiological response, middle-term symptom improvement and long-term prognosis risk through deep learning; dynamically matching the evaluation vector with the reference matrix, and introducing an individual weight coefficient to correct deviation; taking the deviation correction value as input, constructing a self-adaptive evaluation model through reinforcement learning, and performing iterative optimization; and generating an individualized report containing the curative effect level, the medication suggestion and the risk early warning, and quantifying the curative effect level through a fuzzy comprehensive evaluation method. According to the method, individual differences are accurately captured, full-cycle dynamic evaluation is realized, and the curative effect evaluation accuracy and the clinical decision-making efficiency are improved.

The invention discloses a diabetic nephropathy risk assessment and prediction system based on platelet indexes, and particularly relates to the technical field of disease risk prediction, which comprises a platelet index acquisition module, a drug use data acquisition module, a drug intervention assessment module, an index correction module, a clustering analysis module and a diabetic nephropathy risk assessment module, the method comprises the following steps: acquiring platelet related detection indexes and historical medication data of a patient, calculating a drug intervention influence index and generating a correction factor, realizing platelet index reverse reduction correction by adopting a convolutional neural network model, determining a similar group in combination with clustering analysis, and dynamically evaluating the risk of diabetic nephropathy based on the average morbidity of the group. Personalized accurate prediction is realized; the platelet index correction accuracy is improved, individualized drug intervention elimination is achieved, the similar population clustering analysis precision is enhanced, diabetic nephropathy risk prediction is dynamically adjusted in combination with comprehensive scores, and the early screening and individualized intervention effects are improved.

The invention relates to the field of medical health and artificial intelligence, and discloses a cancer patient low-molecular heparin individualized dose dynamic optimization method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional clinical data of a cancer patient, carrying out the preprocessing, carrying out the enhancement of the preprocessed data, and generating time sequence synthesis data; s2, based on the multi-dimensional clinical data and the time sequence synthesis data, constructing a reinforcement learning environment, including defining a state space, an action space and a reward function; s3, constructing an environment adaptive deep reinforcement learning EA-DRL model as a dose optimization model; performing model training and optimization in the reinforcement learning environment until convergence and obtaining an optimal dose decision strategy; and S4, inputting real-time dynamic clinical data of a patient to be decided into the trained dosage optimization model, and outputting individualized low molecular heparin LMWH administration dosage recommendation. Individualized precise administration is realized, and the treatment effect and the clinical medication safety are improved.

A medication container encoding, verification and identification method is provided that includes receiving data characterizing a medication, generating an identifier encapsulating the data and applying an identifier to a medication container such that it is automatically readable by a medication device. Related apparatus, systems, methods and articles are also described.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

The invention provides a whole-process targeting polypeptide, a drug compound thereof, a drug delivery system and application of the whole-process targeting polypeptide. The whole-process targeting polypeptide comprises pHA and VAP which are covalently connected through a bridging structure; the VAP is LVAP, a reverse sequence polypeptide of the LVAP, DVAP or SVAP; wherein the amino acid sequence of the LVAP is as shown in SEQ ID NO: 1; the SVAP is a D-configuration polypeptide of the LVAP; the DVAP is a D-configuration polypeptide of the LVAP inverted sequence polypeptide; the bridging structure is amino-monoethylene glycol-carboxylic acid or a derivative thereof. The whole-process targeting polypeptide not only can cross a blood-brain barrier, target tumor neovascularization, cross a blood-tumor barrier, target tumor mimicry vessels, tumor cells and stem cells thereof, but also has better tumor cell affinity, anti-tumor treatment and in-vivo safety effects. Meanwhile, the drug compound or the drug delivery system has a synergistic effect after being combined with a chemotherapeutic drug, and the anti-tumor drug effect can be remarkably improved.

The invention discloses a liver transplantation post-operation intelligent management system and method based on multi-modal monitoring and AI. The method comprises the following steps that S1, physiological parameters, biomarker data, iconography data and self-reporting data of a patient are collected in real time; s2, carrying out preprocessing, feature extraction and fusion on the collected data, and predicting the postoperative complication risk of the patient by using a risk prediction model; s3, generating a personalized management strategy by using a reinforcement learning algorithm according to a risk prediction result, and providing early warning information and intervention suggestions for medical personnel and patients; s4, according to the postoperative management plan and the doctor's advice of the patient, a fixed-point medication reminding function is set, a personalized medication scheme is generated, and medication compliance of the patient is monitored; and S5, the patient makes an appointment for remote medical services, selects appropriate doctors and time to carry out video inquiry or online consultation, and records inquiry content.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention provides a voice interaction-based intelligent medicine use reminding system and method for the elderly department. The voice interaction-based intelligent medicine use reminding method comprises the following steps of: extracting phoneme characteristics of a target elderly in each historical dialogue round from each interaction voice; determining the pronunciation loss of the target old person in each historical dialogue round according to the phoneme similarity between the interactive voices in each historical dialogue round and the rhythm feature of each interactive voice, and further determining the semantic constraint condition of the target old person in each historical dialogue round; performing multi-intention recognition on the interaction voice of the target old person in the current dialogue round to obtain different semantic intentions of the target old person in the current dialogue round; further determining the semantic integrity of the target old person in the current dialogue round; and correcting the voice content of the target old person in the current dialogue round through the semantic integrity and the intention confidence of each semantic intention. By adopting the scheme of the invention, the fuzzy and distorted voice content of the elderly can be corrected in the voice interaction process.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The invention discloses a low-water-solubility medicine crystal as well as a preparation method and application thereof. The compound Z pamoate crystal form I has large characteristic diffraction peaks at one or more positions with diffraction angles 2 theta of 9.9 + / -0.2 degrees, 10.7 + / -0.2 degrees, 12.1 + / -0.2 degrees, 14.9 + / -0.2 degrees, 17.3 + / -0.2 degrees, 19.4 + / -0.2 degrees, 20.7 + / -0.2 degrees and 23.1 + / -0.2 degrees. Compared with an amorphous compound Z pamoate, the crystal provided by the invention has the advantages of good stability, no crystal transformation phenomenon, no obvious increase of related substances and low solubility; the production process is simple, only water is used as a solvent in the whole process, and no organic solvent is used; when the long-acting slow-release anti-Parkinson medicine composition is prepared from the compound Z pamoate crystal form I, the medicine encapsulation efficiency is high, the burst release is low, the composition continuously releases the medicine in vivo for more than 2 weeks, the administration frequency of a patient can be reduced, and the medication compliance is improved.

The invention discloses a multi-source feature fusion-based traditional Chinese medicine prescription intelligent recommendation method, medium and equipment, and the method integrates the advantages of content recommendation, collaborative filtering and a large-scale language model, and comprehensively considers the symptoms and physical characteristics of a patient, the historical medication records of the patient and other information. And generating a more personalized recommendation prescription based on a final recommendation score obtained after weighted averaging. According to the multi-source fusion mode, the multi-dimensional information of the patient can be fully utilized, and the recommendation accuracy is improved. And a large language model is introduced to carry out deep semantic analysis on symptom text information described by the patient, so that hidden demands and potential symptoms of the patient can be captured, and the recommended prescription is more accurate.

The invention discloses a postoperative block analgesia curative effect evaluation system based on multi-modal data analysis, particularly relates to the technical field of medical information processing, and is used for solving the problem of poor analgesia evaluation standardization. An evaluation path of analgesia deviation quantification, state time sequence judgment and strategy generation is realized by constructing a structured comparison mechanism of an analgesia observation frame and an individualized analgesia baseline trajectory, pain, medication and activity deviations are dynamically calculated by fusing an anesthesia record, an analgesia pump log and clinical monitoring data, and an analgesia result is obtained. Typical states such as insufficient retardation effect are identified in combination with a time sequence inference model, a curative effect level and an early warning mark are automatically output, additional retardation, parameter adjustment and medicine remedy suggestions are generated according to a credibility level, and a state, disposal and result chain is constructed by continuously learning historical case data, so that the treatment efficiency is improved. The adaptive optimization of the baseline trajectory, the state threshold and the adjustment strategy is realized, and the individual matching performance, the response timeliness and the risk control capability of the analgesia strategy are improved.

The invention discloses a chronic disease management system for hospitals, which relates to the technical field of chronic disease management systems and comprises a patient file module, a risk assessment module, an intervention scheme module, a follow-up management module, a monitoring and early warning module and a decision support module. The patient archive module is used for collecting and sorting personal information and medical history information of patients by adopting an automatic data integration method to obtain a complete patient health archive; the risk assessment module is used for analyzing the health information in the patient health record by adopting an intelligent assessment algorithm to obtain a chronic disease risk assessment result; the intervention scheme module is used for formulating a personalized health intervention scheme according to the risk assessment result, and the personalized health intervention scheme comprises diet, exercise and medication suggestions; and the follow-up management module is used for executing the follow-up plan in the intervention scheme through multiple communication modes, tracking the rehabilitation condition of the patient, recording and feeding back, and generating a follow-up report.

The invention belongs to the technical field of pharmaceutical analysis, and particularly relates to a method for determining a residual solvent in LXH-2103 by headspace gas chromatography. The determination method comprises the following steps: preparing a blank solution, a reference substance solution and a sample solution, respectively injecting the blank solution, the reference substance solution and the sample solution into a headspace for balancing, injecting the solutions into a gas chromatograph in a headspace sample injection manner, recording a chromatogram, and calculating the content according to a peak area by an external standard method; the residual solvent is absolute methanol, absolute ethyl alcohol, isopropanol, dichloromethane and methylbenzene. The method for determining the residual solvents in the LXH-2103 by using the headspace gas chromatography, provided by the invention, is high in sensitivity, good in repeatability and high in accuracy, and can be used for simultaneously detecting various residual solvents in the LXH-2103, so that the quality of the LXH-2103 is ensured, and the safety of clinical medication is improved.