51 results about "Patient age" patented technology

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

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 invention discloses a prediction method and a prediction system for fracture risk of an osteoporosis patient. The method comprises the following steps: firstly, collecting a plurality of parameters such as age, bone mineral density, vitamin D level and the like of the patient; constructing a bone time-varying factor and dynamic prediction model, and determining the influence weight of each parameter on the bone condition at different time nodes; then, a three-dimensional mechanical model is constructed through mechanical simulation of the skeleton microstructure, stress distribution, strain states and structural stability of the skeleton under different loads are simulated, and the model is corrected by fusing parameters to influence weights; then the fracture probability in a future preset time period is calculated; and finally, a result is presented through a nomogram visualization model. The system realizes the above method, can predict the fracture risk more accurately, and provides powerful support for clinical diagnosis and treatment.

The invention relates to the technical field of information technology management, and discloses an intelligent medical quality monitoring and case analysis system and method, and the system comprises an interface layer which is used for accessing an HIS system, an inspection image device and a patient terminal, and supports the real-time collection of multi-mode medical data of texts, images and audios; the intelligent agent layer is used for deploying functional modules including pre-inquiry, intelligent hospital guide and family doctor question and answer, the intelligent agent layer is provided with a personalized analysis module, and the personalized analysis module is used for analyzing personalized elements including the age, gender, living environment and growth experience of a patient through a self-adaptive algorithm; the core layer integrates a medical record reduction engine and a medical hidden danger identification module, and constructs a dynamic health record based on the whole course of disease data of the patient; and the model layer comprises a large language model, a multi-modal model and an inference model. According to the invention, the accuracy of medical record analysis and the pertinence of medical hidden danger identification can be improved.

The invention discloses a children propofol anesthesia depth monitoring method based on permutation entropy and heart rate change, and belongs to the technical field of anesthesia depth monitoring, and the method comprises the following steps: extracting a children electroencephalogram, and carrying out preprocessing and spectral analysis on the children electroencephalogram to obtain permutation entropy; child heart rate data are extracted and processed to obtain the heart rate change degree; inputting the permutation entropy, the heart rate change degree and the age into the mixed prediction model to obtain a composite index, and indicating the narcotic depth of the propofol for the children. According to the children propofol anesthesia depth monitoring method based on the permutation entropy and the heart rate change, the mixed prediction model is constructed by combining the electroencephalogram permutation entropy, the heart rate change degree and the child patient age, and higher accuracy, real-time performance and reliability are shown in children propofol anesthesia depth monitoring; the anesthesia state change can be quickly responded, and the problem of misjudgment caused by age dependence and insufficient linear processing in the prior art is effectively solved.

Provided herein are methods and compositions for the treatment of ALS using chlorite or a pharmaceutical composition comprising sodium chlorite, for a target ALS patient population. Also provided herein are methods for identifying a target ALS patient population likely to be responsive to treatment with chlorite or a pharmaceutical composition comprising sodium chlorite, the methods including identifying ALS patients with elevated plasma C-reactive protein (CRP) levels and / or patients age 40-65, sporadic ALS pathology, or combinations thereof.

The invention discloses a respiratory medicine vital capacity training device, and belongs to the technical field of vital capacity training, the respiratory medicine vital capacity training device comprises a shell, a sliding plate located in a shell cavity, an air blowing structure located at the bottom of the shell and a second stop block used for limiting the position of the air blowing structure, and outer fixing plates are fixed to the top and the bottom of one side of the outer wall of the shell; a limiting block is fixedly installed at the position, between the two outer fixing plates, of the outer wall of the shell, scale marks facilitating reading are marked on one side face of the limiting block, a threaded rod is rotationally installed between the two outer fixing plates of the outer wall of the shell, and a movable sleeve is in threaded connection with the threaded rod; a limiting sliding block slidably connected to the limiting block is fixedly installed on the side face, close to the center of the shell, of the movable sleeve. The vital capacity training device for the respiratory medicine department can control the amount of gas exhaled by a patient according to the age and the body shape of the patient in the using process of the patient in the respiratory medicine department, and the patient can train according to the amount of gas.

The invention relates to the technical field of medical management, and discloses a rehabilitation medical training management system which comprises a medical acquisition module and an intelligent evaluation module. According to the rehabilitation medical training management system, management data and rehabilitation data of all patients and monitoring data of training environments where the patients are located are obtained through the medical collection module and classified to form a data set, and the intelligent evaluation module divides resistance groups and susceptible groups according to the ages of the patients and determines rehabilitation difficulty. Analyzing the severity of the physiological disorder of each patient, generating a disorder coefficient, setting a monitoring period, analyzing the fluctuation rate of the disorder coefficient of each patient and the variable quantity of each physiological parameter of the patient, generating a monitoring data set, analyzing the load coefficient during rehabilitation training, and comprehensively analyzing the training suitability of the patient; the intelligent evaluation module judges whether the patient has the physical state of rehabilitation training or not and whether the exercise intensity in the rehabilitation training process is overloaded or not and outputs corresponding training suggestions, and the rehabilitation effect is dynamically adjusted to be better.

Methods using and compositions including FSH for use in the treatment of infertility are described, wherein the dose is selected based on the patient's age to optimise cumulative efficiency and / or reduce OHSS risk.

The invention discloses an ACS patient prognosis prediction method, which is characterized in that ACS patient prognosis is predicted through a trained and verified ACS patient prognosis prediction model, and prediction factors of the prediction model comprise patient age, heart rate, heart function grading, urea nitrogen and glycosylated hemoglobin. The predictive factors are screened by adopting multi-factor Cox regression analysis. The prediction model comprises a column chart model, and a column chart scale comprises a scale score range of 0-100, a patient age range of 25-95, a heart rate range of 40-140, a heart function grading range of I-IV, a urea nitrogen range of 0-35, a glycosylated hemoglobin range of 4-15, a total score range of 0-240 and a survival rate range of 0.99-0.01.

A system and method is described that automatically collects health information from multiple health data provider sources, combines it into a database, then provides a view of the information on a body map. The body map can be a drawing, photograph, or other visual model, and can be changed over time as the patient advances in age. While the image may change over time, the system continues to plot the information in the correct body location.

Embodiments provided herein include systems and methods for determining extubation readiness. One embodiment of a method includes obtaining pulse oximeter data and ventilator data for a patient that has been intubated over a predetermined monitoring period, classifying a lung disease state of the patient as acute or chronic based on at least one of the following: an age of the patient or a clinical indicator, and providing the pulse oximeter data, the ventilator data, and a lung disease classification to a trained predictive model previously trained on training data. Some embodiments include calculating a likelihood of extubation success or failure within a clinically relevant time window, and generating at least one of the following: a positive predictive value (PPV) or a negative predictive value (NPV) and generating and presenting, by the computing device, a readiness output based on the likelihood of extubation success or failure for determining extubation timing.

The invention relates to a dynamic hemodynamic parameter management method and device, equipment and a medium. The method comprises the steps that hemodynamic parameters are monitored; wherein the hemodynamic parameters at least comprise the age of a patient, the maximum value of the chest fluid level, a heart index, the maximum increase rate of the heart index and the maximum increase rate of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used for representing a decision relation between each hemodynamic parameter and the mechanical ventilation time; the hemodynamic parameters and the mechanical ventilation time are sent to a terminal to be displayed. The clinical decision-making efficiency can be remarkably improved.

A group of marker sets capable of effectively predicting acute pain of a patient after cesarean section is screened out, and the marker sets comprise whether the patient has preoperative anxiety or not, whether the patient is gestational diabetes mellitus or not, the age of the patient, the Pittsburgh sleep quality index (PSQI) of the patient, the gestational week age of the patient and the abdominal circumference of the patient. On the basis, a visual and quantifiable column diagram prediction model is constructed, the method can be used for evaluating the risk probability of acute pain occurring after a cesarean section patient is operated clinically before an operation, targeted and individualized pain management is provided for the patient subsequently, the pregnancy safety of the patient is improved, and physical and psychological pain of the patient after the operation is relieved.

This application relates to a method and system for generating AMH test reports using multi-dimensional data fusion. The method includes extracting AMH samples from women of a specific age group and establishing a regression standard curve between age and the mean AMH value based on statistical distribution. The theoretical AMH value is obtained from the standard curve based on the patient's age, and combined with the actual test value to calculate the AMH standard offset reflecting the degree of deviation. Based on the statistical distribution of the sample set, a preset interval threshold reference range for the offset is set and simultaneously presented in the report as an evaluation benchmark. When the offset exceeds a preset risk threshold, multi-dimensional clinical data from the patient's historical diagnosis, hormone levels, and ultrasound are automatically collected and entered into the analysis process. The offset index, reference range, and multi-dimensional data mining results are fused to obtain structured and personalized report suggestions to assist in precise clinical decision-making.

The goal is to generate appropriate ultrasound image data based on the patient's age. [Solution] The ultrasound diagnostic device comprises a transmitting / receiving unit, a generating unit, an image processing unit, an acquisition unit, and an output unit. The transmitting / receiving unit causes an ultrasound probe, which transmits and receives ultrasound from the patient, to transmit and receive ultrasound based on the transmission / receiving conditions. The generating unit generates ultrasound image data based on the received signal from the ultrasound probe. The image processing unit processes the ultrasound image data based on the image processing conditions. The acquisition unit acquires information related to the patient's age. The output unit derives at least one of the transmission / receiving conditions for the ultrasound probe and the image processing conditions based on the age-related information.

The invention provides a pressure adjusting method and system for an intelligent enema device, and relates to the technical field of medical treatment, and the method comprises the steps: obtaining basic data such as age, height and weight of a patient, calculating a minimum pressure threshold value, a maximum pressure threshold value and a deformation rate threshold value, obtaining real-time pressure data in intestines and a deformation rate, comparing the data with the deformation rate, and adjusting the pressure according to the pressure. The flow rate and the liquid amount of the clysis liquid are synchronously adjusted by adopting a pressure grading progressive method. By calculating the threshold value based on the basic data of the patient, monitoring the pressure and the deformation rate in real time and dynamically adjusting the pressure and the deformation rate, accurate regulation and control of the enema pressure are achieved, safe and effective enema is guaranteed, and the enema effect is improved.

The invention provides a control method for reducing needle leakage and liquid leakage of an indwelling needle and an indwelling needle infusion monitoring system.The control method comprises the following steps that the maximum tolerance pressure of a blood vessel punctured by the indwelling needle is obtained according to the age and blood pressure of a patient punctured by the indwelling needle and the fullness and size of the punctured blood vessel; according to the maximum tolerance pressure of the blood vessel, the maximum flow speed of the conveyed medicine is obtained; according to the maximum flow speed of the medicine, the actual flow speed of conveyed medicine is adjusted. For a patient subjected to emergency rescue, the maximum tolerance pressure of the blood vessel punctured by the indwelling needle is obtained, then the maximum flow speed of the conveyed medicine is obtained according to the maximum tolerance pressure of the blood vessel, and then the actual flow speed of the conveyed medicine is adjusted according to the maximum flow speed of the medicine. According to the change of the fullness degree of the blood vessel lumen, the actual flow speed of the conveyed medicine is adjusted, and the probability of needle leakage and liquid leakage of the remaining needle is reduced.

The invention discloses an intelligent terminal and system for evaluating the consciousness state of an unconscious patient based on neurology department monitoring, and belongs to the technical field of medical treatment. According to the method, feature sampling is carried out on the collected neurology department monitoring real-time data of the coma patient, the preset evaluation index of the patient is obtained and analyzed, the key consciousness state risk evaluation parameter is marked as the patient consciousness state basic score of the current monitoring period, and the comprehensive evaluation result of the consciousness state of the coma patient is obtained. An evaluation model is constructed by combining monitoring data collection with key feature extraction, a convolutional neural network of a multi-feature extraction layer and a recurrent neural network of time sequence learning, weights are dynamically corrected by combining individual factors, intelligence and individuation of consciousness state evaluation are achieved, feature vectors of four types of indexes are optimized through the multi-feature extraction layer, and the evaluation accuracy of the consciousness state evaluation is improved. Dynamic correction is carried out by combining the age of the patient, the cause of coma and the monitoring period, so that the evaluation result can reflect the overall consciousness state and highlight the key risk point of the individual.

The invention discloses an outpatient department accurate shunting and appointment guiding method based on AI pre-inquiry analysis. The method comprises the following steps that S1, a patient initiates pre-inquiry: the patient initiates a pre-inquiry request through a hospital APP, a self-service machine or an applet terminal; and S2: pushing the standardized inquiry questionnaire: the hospital APP, the self-service machine or the applet end pushes the standardized inquiry questionnaire through the shunting reservation system. By integrating multi-dimensional information such as texts, images, audios, patient ages and medical histories and by means of BERT semantic understanding, a medical knowledge graph and an XGBoost algorithm, the department matching accuracy is larger than or equal to 95%, the wrong number hanging rate is reduced to 3% or below, the limitation of single symptom matching is broken through, the cross-specialist differential diagnosis ability is improved, AI model driving replaces a rigid rule base, and the accuracy of diagnosis is improved. And iterative optimization is carried out through 1-20 thousand newly added data in each month, and rapid updating of the adaptive medical knowledge is realized.

The present invention discloses a method for early warning of patients' medication risks, which relates to the technical field of medication risk assessment. The method obtains multi-dimensional data such as the patient's age, past medical history, allergy history, clinical examination data, and prescription data, extracts chronic disease diagnosis information from them, and generates co-morbidity combination labels through standardized coding; calls a preset disease weight system, and calculates the co-morbidity severity score in combination with age factors; extracts metabolism-related indicators to construct a feature vector of the patient's drug metabolism ability; maps the drug combination, co-morbidity label, and metabolism feature vector to a clinical medication knowledge base to generate a comprehensive conflict detection result; generates corresponding early warning information based on the conflict detection result and a preset early warning rule base, and the rule base contains the mapping relationship between the conflict type and the early warning level. This method can achieve early warning of medication risks for the state of co-existence of multiple diseases in patients and improve medication safety.

We provide the system. [Solution] A system for generating rehabilitation plans for the elderly, A means of inputting the patient's age, sex, physique, athletic ability, symptoms, personality, and goals, A server that generates a rehabilitation plan based on the input information, A means for displaying the generated rehabilitation plan and allowing the user to review and adjust it, A system that includes this.

This invention discloses a urodynamic intelligent diagnostic method based on a multi-tree model fusion of prior regularization and an age-modulated network, belonging to the field of medical intelligent diagnostic technology. This method integrates four tree models—XGBoost, LightGBM, Random Forest, and CatBoost—combining five-fold cross-validation and multiple random seeds to obtain feature prior weights, and assigns positive or negative weights using the Spearman correlation coefficient. The age-modulated sub-network dynamically adjusts the feature weights according to the patient's age. The main network with prior regularization constraints uses a cosine similarity regularization term to guide the direction of learnable weights, improving diagnostic accuracy and interpretability. This invention achieves an average AUC of over 0.94 for diagnosing bladder outlet obstruction, with significantly better accuracy, recall, and F1 score than existing methods. It effectively solves the problems of poor interpretability, insufficient integration of individual age factors, and unreasonable feature weight design in existing diagnostic models, thus adapting to the needs of clinical urodynamic diagnosis.

A system and method is described that automatically collects health information from multiple health data provider sources, combines it into a database, then provides a view of the information on a body map. The body map can be a drawing, photograph, or other visual model, and can be changed over time as the patient advances in age. While the image may change over time, the system continues to plot the information in the correct body location.

The invention belongs to the technical field of intelligent medical treatment, and discloses a dynamic risk assessment method and system for light-chain amyloidosis. A traditional staging system does not consider age factors and does not consider interaction between hematology and organ remission, so that obvious defects exist in the aspect of prognosis personalized risk prediction. According to the method, a 6-month risk scoring model and a 12-month risk scoring model are created for the first time, the age, hematological remission and heart and kidney organ remission depth of a patient are subjected to weighted integration through a multivariable Cox proportional risk model, and risk scores are generated and divided into corresponding risk grades. According to the method, the independent contribution and the interactive influence of each factor on the death risk are quantified, the method is closer to the complicated biological nature of the disease, continuous monitoring, dynamic evaluation and accurate prediction of prognosis are realized, the clinical use threshold is greatly reduced, the treatment decision can be directly linked, and an objective basis is provided for the treatment decision.

The application discloses a kind of acne diagnosis and treatment prediction method, system, medium and computer program product, it is related to artificial intelligence and medical health technical field, its technical scheme main point is: obtaining multi-view facial image data, extracting features by multi-branch CNN architecture, each branch extracts single-view feature, the single-view feature extracted is fused, obtain the feature after fusion, the feature after fusion is input into classification model, and obtain acne severity classification result;Obtain patient age, gender and prescription information, input patient age, gender and prescription information and acne severity classification result into trained prediction model, and obtain treatment prediction result;Using SHapley algorithm, establish prediction model interpreter, and explain treatment prediction result.The framework proposed in the application bridges the gap between acne diagnosis and personalized treatment planning, and provides a reliable and interpretable clinical tool for improving patient outcomes and optimizing treatment strategies.

The invention relates to a multi-factor analysis-based postoperation bleeding risk early warning and evaluation method and system for pancreaticoduodenectomy, and belongs to the technical field of data analysis. The method comprises the steps of obtaining the age and gender of a patient, presetting an age threshold value, dividing the age according to the age threshold value to obtain age group data, obtaining physiological data, and calculating a pancreatic fistula estimated value through a pancreatic fistula prediction model according to the physiological data and the age group data; obtaining abdominal cavity physiological data, and calculating to obtain an abdominal cavity infection estimated value through the abdominal cavity infection prediction model according to the abdominal cavity physiological data; and obtaining early warning physiological data, and according to the early warning physiological data, the pancreatic fistula estimation value, the abdominal cavity infection estimation value and the gender, performing calculation through the bleeding risk early warning evaluation model to obtain bleeding estimation grade data. The hemorrhage risk early warning evaluation after the pancreaticoduodenectomy based on multi-factor analysis is realized.

The invention discloses an intelligent decision support system for ear-nose-throat disease nursing, relates to the technical field of medical services, and solves the technical problems that effective integration and utilization of a large amount of medical data are lacked, an accurate quantitative risk prediction model is lacked, and targeted prevention and nursing measures cannot be taken in advance. Multi-dimensional information of a patient is comprehensively obtained through the user information collection module, deep integration analysis is conducted through the medical history record analysis module, more accurate disease diagnosis is achieved in combination with symptoms, medical history, examination results and the like, a solid foundation is provided for subsequent nursing decisions, medical big data is utilized, and the medical big data is analyzed. Nursing records are screened and matched according to patient age classification and pathological characteristics, further optimization is performed in combination with living habits, a highly personalized medical history-free nursing decision is generated, the nursing records and patient basic information are comprehensively analyzed according to different risk signals, and comprehensive and highly targeted nursing information is generated.

The invention discloses a hysteroscopic surgery whole-process information management system, and relates to the technical field of hysteroscopic surgery. Comprising an information registration module, a pre-operation evaluation module, a scheme recommendation module, an intra-operation collaboration module, a post-operation management module, a follow-up management module, a data security module, a consumable management module, a doctor-patient interaction module and a scientific research analysis module, and all the modules interact in real time through a data bus to form an evaluation-execution-management-scientific research closed loop. And the information management efficiency and the medical quality are obviously improved. According to the system, through a machine learning algorithm of the preoperative evaluation module, the age, medical history and laboratory indexes of a patient can be automatically analyzed, the risk probability of bleeding, infection and the like in the operation can be predicted, a personalized preparation scheme including preoperative medication and fasting time can be generated, and the scheme recommendation module supports automatic matching of an operation style and an instrument list. A standardized operation process is generated in combination with historical operation data, and the decision-making difficulty of novice doctors is reduced.

The invention discloses a power control system and method for a semiconductor laser therapeutic instrument, and particularly relates to the technical field of laser equipment control. According to the power control system and method, starting, continuing and ending stages are divided based on treatment time, and the strategies of initial penetration, continuous adaptation and gradual power reduction are correspondingly adopted; in the continuous treatment stage, the skin impedance amplitude and tissue temperature biological parameters of a patient are collected, and in combination with personalized dynamic reference values generated by the age, gender and pre-treatment initial parameters of the patient, a state index is calculated and power is dynamically adjusted, so that individual differences and treatment requirements of different patients are met, the curative effect is considered, and excessive treatment is avoided; the problem that in the prior art, power control lacks individualized and dynamic self-adaptive capacity is solved.