38 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.

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.

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 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 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.

An apparatus and computerized method classify a patient that has been previously diagnosed to have autosomal dominant polycystic kidney disease (ADPKD). A computing device having an input / output interface, one or more processors and a memory is provided. A total kidney volume (TKV), a patient height, and a patient age for the patient is received via the input / output interface. A height adjusted TKV (HtTKV) is determined based on the total kidney volume and the patient height using the one or more processors. An ADPKD classification for the patient is determined based on the height adjusted TKV and the patient age using the one or more processors. The ADPKD classification is provided for the patient via the input / output interface.

This disclosure relates to a method, apparatus, device, and medium for managing dynamic hemodynamic parameters. The method includes: monitoring hemodynamic parameters; wherein the hemodynamic parameters include at least: patient age, maximum pleural fluid level, cardiac index, maximum rate of increase of cardiac index, and maximum rate of increase of myocardial contractility; inputting the hemodynamic parameters into a preset decision tree to output mechanical ventilation time; wherein the decision tree is used to represent the decision relationship between various hemodynamic parameters and mechanical ventilation time; and sending the hemodynamic parameters and mechanical ventilation time to a terminal for display. This disclosure can significantly improve the efficiency of clinical decision-making.

The invention relates to a joint detection kit for predicting endoscopic response of inflammatory bowel disease in the technical field of medical examination, and solves the technical problem that the existing single biomarker is limited in detection sensitivity and specificity. Comprising an excrement calprotectin detection reagent and an excrement lactoferrin detection reagent, a monoclonal antibody sandwich ELISA method is adopted for calprotectin detection, and the detection range is 10-3000 [mu] g / g; the lactoferrin is detected by adopting a chemiluminescence immunoassay method, and the detection range is 5-500mu g / g. An endoscopic response prediction scoring model is established based on calprotectin concentration X and lactoferrin concentration Y: prediction score = alpha * ln (X + 1) + beta * ln (Y + 1) + clinical correction factor, alpha is 0.65-0.75, beta is 0.45-0.55, and the clinical correction factor integrates C-reactive protein, fecal occult blood and patient age. Compared with single detection, the sensitivity is improved to 85%-88%, and the specificity is improved to 83%-86%.

The invention relates to the technical field of cardiovascular surgery, and particularly discloses a method for predicting delirium after non-extracorporeal circulation coronary artery bypass surgery based on preoperative tissue oxygen saturation. Selecting a patient of which the age is greater than or equal to 60 years old and which is planned to be subjected to the selected non-extracorporeal circulation coronary artery bypass transplantation as a subject; the blood vessel or tissue of a patient does not need to be punctured, the risks of pain, infection and the like caused by invasive examination are avoided, and the comfort level and acceptability of the patient are improved; changes of tissue oxygen saturation can be continuously monitored in real time, oxygen supply balance abnormity of a patient can be found in time, and timely information is provided for clinical intervention; equipment operation is relatively simple, complex technical training is not needed, medical staff can rapidly master the using method, and wide clinical application is facilitated; the method is suitable for patients of different age groups and different illness states, and especially has important application value for some patients who cannot tolerate invasive examination.

The invention discloses an anesthesia depth monitoring method based on multivariate data, relates to the technical field of anesthesia depth monitoring, and solves the technical problems of interval distortion and inaccurate sudden change trend judgment and traceability caused by single hemodynamic index anomaly detection. Multi-dimensional information such as patient age, basic medical history and preoperative baseline is synchronously extracted, an index judgment interval is generated in combination with an improved algorithm, the monitoring accuracy is improved, temporary interference data such as pain and stress are removed in haemodynamic index processing, and then individual deviation degree detection and distribution rationality detection are carried out, so that the accuracy of monitoring is improved. The defect of only group adjacent difference detection is overcome, the index accuracy is further improved, sudden change authenticity verification is newly added, invalid alarms caused by equipment errors are effectively filtered, meanwhile, an emergency priority tracing mechanism is established, and emergency delay caused by no priority of tracing in the prior art is avoided.

The invention relates to the field of medical instruments, in particular to a laser millimeter wave therapeutic instrument for treating diabetic feet and a control method of the laser millimeter wave therapeutic instrument. The method comprises the following steps: acquiring an image of a diabetic foot part, and obtaining a Wagner grade of the diabetic foot; preliminarily determining an illumination intensity grade according to Wagner grading of the diabetic foot; adjusting the preliminarily determined illumination intensity according to the age of the patient so as to obtain the preliminarily adjusted illumination intensity; further adjusting the illumination intensity after the preliminary adjustment according to the disease duration of the patient so as to obtain the target illumination intensity of the laser therapeutic instrument, including: if the disease duration does not exceed 3 years, reducing the illumination intensity after the preliminary adjustment by 10%; if the illness duration exceeds 5 years, the illumination intensity after preliminary adjustment is increased by 10%; and during treatment, the laser irradiation intensity of the laser therapeutic instrument is adjusted to the target illumination intensity. By adopting the method, the treatment effect of the laser millimeter wave therapeutic instrument on the diabetic foot can be effectively improved.

A computer-implemented method of determining, for a patient, a risk of developing sepsis is described. The method comprises obtaining, at a computing device, a set of patient data indicative of at least one of: a patient age value, a patient heart rate value, a patient respiratory rate value, a patient diastolic blood pressure value, a patient systolic blood pressure value, a patient temperature value, and a patient oxygen saturation value; and at least one value of a Monocyte Distribution Width (MDW) of the patient. Further, the method comprises determining, based on processing the obtained set of patient data with the computing device, at least one sepsis indicative of the risk for a sepsis event occurring at the patient.

The invention relates to an AI-assisted bladder cancer diagnosis method, device and equipment based on urine multi-omics data and a medium, and the method comprises the following steps: carrying out standardization processing on input urine genome, metabolome and proteome data, and generating a sample component distribution matrix after eliminating dimensional difference; key features are extracted through principal component analysis, and core biomarkers are screened in combination with a random forest algorithm; fusing metadata such as patient age and smoking history and core features by using a graph neural network, and constructing an individual pathological state model; and generating a risk probability and a confidence score based on a preset bladder cancer feature template. According to the method, the problems that in the prior art, multi-omics data preprocessing precision is insufficient, single omics analysis limitation is large, and clinical metadata integration and biomarker synergistic effect path mining are lacked are solved, diagnosis threshold values are dynamically adapted to individual characteristics, and the early tumor detection rate and the high-risk group screening applicability are effectively improved.