533 results about "Missed diagnosis" patented technology

The invention discloses an electromechanical system fault pre-diagnosis method and system based on digital twinning. The method comprises the following steps of obtaining multi-source data in an electromechanical system operation process; preprocessing the acquired multi-source data, wherein the preprocessing comprises data cleaning, normalization processing and feature extraction; and on the basis of the preprocessed multi-source data, an electromechanical system design drawing, a three-dimensional geometric model, material attributes and a kinetic equation are fused, and a digital twin model is constructed. According to the invention, through a digital twin model dynamic calibration and prediction algorithm, early abnormity of the equipment is identified in advance, the fault probability and the residual life are output, and non-planned shutdown is reduced; by constructing a cross-physical domain fault feature system and fusing model simulation and actual measurement data, the potential fault identification accuracy is improved, and the missed diagnosis rate is reduced; by calibrating parameters of the digital twin model in real time, the method adapts to nonlinear changes of equipment, ensures high-fidelity mapping of the model, and improves fault prediction precision.

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The present invention relates to the technical field of medical triage, and discloses an emergency patient triage system based on artificial intelligence, including: a data acquisition module: used to obtain the patient's identity and basic information by performing face recognition and fingerprint recognition on the patient, and receive health data uploaded by a wearable device, and transmit it to the patient's corresponding historical pathology library; a medical interaction module: used to provide an interactive platform for patients to remotely input pathological conditions based on 5G wireless communication technology; a multimodal data fusion module; a pathological feature matching module; a triage matching module; and a system integration and display module. Through the convolutional neural network model combined with pathological feature matching, based on the patient's question reply content, the condition and the judgment of the severity, a rapid judgment of the possibility of the disease, the generation of consultation content and the optimization of triage results are achieved, thereby improving the accuracy and efficiency of triage and reducing misdiagnosis and missed diagnosis.

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

The invention relates to the technical field of intelligent medical treatment, and discloses an AI-based severe patient sepsis early warning method and system. According to the method, a multi-organ interaction mechanism is deeply analyzed by dynamically constructing an organ-level causal network, and early-stage accurate early warning of sepsis is realized: high-frequency physiological waveforms, asynchronous laboratory indexes and treatment intervention data are fused, and the capture ability of microcirculation failure and autonomic nerve decline is improved; the early warning threshold value is dynamically adjusted based on the treatment coverage degree, and the delay and false alarm defects of a fixed threshold value mechanism are effectively overcome; a pathogen targeted therapy map and an organ function support scheme are automatically generated by a graded triggered clinical action chain, and the clinical decision response time is shortened; according to the method, the risk of missed diagnosis is reduced while the early warning sensitivity is improved, and an earlier and more reliable intervention window is provided for critical patients.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention discloses a failure restoration retreatment decision-making method based on dynamic occlusion analysis, and the method comprises the steps: constructing a four-dimensional virtual biomechanical model which comprises a patient anatomical structure and a dynamic function through multi-modal data fusion, and enabling the model to be embedded into a real three-dimensional mandibular movement track of a patient to simulate functional movement, thereby breaking through a static analysis blind area, and achieving the real three-dimensional mandibular movement of the patient. The missed diagnosis rate of dynamic interference is reduced; based on quantitative indexes and thresholds such as occlusal contact displacement, occlusal contact distribution symmetry and adjustment quantity of the maximum tooth tip staggering position and the median relation position, experience judgment of doctors is replaced, objectivity, repeatability and accuracy of failure repair and retreatment decision are improved, and excessive or insufficient adjustment is avoided; the treatment effect rehearsal is realized through virtual presetting, the irreversible operation risk is avoided, and the number of times of re-visit and the trial and error cost of a patient are reduced; and meanwhile, the four-dimensional virtual model can be connected with transition and design and manufacturing of a final restoration, so that the accuracy, safety and efficiency of retreatment of a failed restoration are improved.

The invention relates to the technical field of medical evaluation, and provides an evaluation system and method for potential complications of the department of cardiology, and the method comprises the following steps: S1, collecting information data of a patient, and carrying out the data verification and data preprocessing; s2, based on the obtained information, classifying types of complications which may occur on the patient; and S3, based on a complication type classification result, risk factors are extracted, and a risk factor network is constructed. By classifying the complication types, constructing the risk factor network and comprehensively considering multiple factors to calculate the risk index, compared with a traditional assessment mode, the risk of potential complication of a patient can be assessed more comprehensively and accurately, misdiagnosis and missed diagnosis caused by incomplete assessment are reduced, and the risk assessment efficiency is improved based on the risk score, the risk index and the risk type. The drug dosage or the measure intensity is adjusted through the patient specificity index, the prevention measures are dynamically adjusted in combination with the treatment time, and the pertinence and effectiveness of the prevention scheme are improved.

The invention discloses an orthopedic patient data management and remote monitoring platform, and relates to the technical field of monitoring management, the orthopedic patient data management and remote monitoring platform comprises a monitoring management platform, the monitoring management platform is in communication connection with a data acquisition module, a data processing module, an abnormity monitoring module, an abnormity early warning module and a remote guidance module, all the modules are connected through electric signals. According to the orthopedic patient data management and remote monitoring platform, various monitoring devices and wearable devices are integrated, physiological parameters and motion parameters of a patient are collected, a comprehensive patient health condition view is provided for medical staff, and the medical staff can find abnormal physiological and motion characteristics of the patient in time through real-time monitoring, so that the medical staff can monitor the abnormal physiological and motion characteristics of the patient in time. In addition, the abnormal trend in the patient data is identified, targeted diagnosis and treatment reminding is provided for medical staff through setting of the abnormal early warning coefficient and the abnormal early warning level, and the risk of missed diagnosis and misdiagnosis is remarkably reduced.

The invention discloses an image acquisition processing method and system based on an AR technology, relates to the technical field of augmented reality, and effectively solves the problems of image noise and artifacts through an image enhancement module and a dynamic artifact correction module. The image enhancement module highlights key areas, adjusts details and weakens metal artifacts in combination with an artifact removal algorithm, and the artifact correction module captures tiny movement of a patient in real time by using a posture tracking algorithm, calculates a correction coefficient Jyz, compensates and eliminates motion artifacts according to the correction coefficient Jyz and outputs a high-quality medical image; multi-frame fusion and detail compensation are carried out by adopting a super-resolution reconstruction algorithm of an AR technology, the resolution is improved, recognition of tiny lesions is enhanced, and missed diagnosis is avoided; the data standardization processing module unifies different device formats through a data bridging interface to ensure compatibility; whether image enhancement is triggered or not is determined by gradually calculating the image quality Ylzz and the image anomaly evaluation index Yjz, it is ensured that the image meets the diagnostic standard, and cross-platform sharing is achieved.

The invention relates to the technical field of test result abnormal mode intelligent early warning systems, in particular to a multi-mode biochemical test result abnormal mode intelligent early warning system and method, and the system comprises a dynamic threshold module, a multivariable mode module, a time evolution module and a self-adaption module. Through fusion of a dynamic threshold technology, multivariable pattern analysis, time sequence modeling and an interpretability mechanism, comprehensive, dynamic and intelligent analysis of biochemical test results is realized. According to the method, the accuracy and timeliness of anomaly detection are remarkably improved, and visual and credible decision support is provided for doctors. The innovative solution is expected to play an important role in the aspects of improving medical diagnosis efficiency, reducing missed diagnosis and misdiagnosis rates, promoting precise medical treatment and the like, and substantive improvement is brought to health management of patients.

The invention provides a liver echinococcosis ultrasonic image intelligent processing method based on deep learning, electronic equipment and a storage medium. The method comprises the following steps: data collection: collecting liver ultrasonic image data; carrying out image preprocessing on the collected liver ultrasonic image data by using an adaptive denoising and enhancement model, carrying out classification labeling on the data after image preprocessing, establishing a classification data set, and labeling the images as cystic echinococcosis, alveolar echinococcosis and other liver lesions; and based on the classification data set, training an optimized VIT classification model DFEV-VIT. The beneficial effects of the invention are that the method carries out the image preprocessing of the collected data through employing the optimized CLAHE algorithm ADEN, and carries out the training of the classification model DFEV-VIT based on the classification data set after the image preprocessing, can greatly improve the diagnosis efficiency and accuracy, and reduces the missed diagnosis and misdiagnosis of the echinococcosis.

The invention relates to the technical field of osteoporosis diagnosis, and discloses an osteoporosis diagnosis method based on image recognition. The method comprises the following steps: acquiring bone density image data of a target object and bone structure image data of a target area based on medical imaging equipment, and determining a bone trabecula distribution condition of the target area according to the bone structure image data; constructing a three-dimensional bone mineral density model of the target object according to the bone mineral density image data, and determining an image analysis strategy by combining the three-dimensional bone mineral density model and the bone trabecula distribution condition; and an image analysis module acquires bone structure feature data of the target area according to the strategy, and identifies the bone density grade and the osteoporosis degree of the target area according to the feature data to obtain diagnosis data. According to the method, related data of the bone mineral density and the bone structure are integrated, comprehensive analysis is carried out in combination with the image recognition technology, an efficient and reliable new mode is provided for clinical diagnosis, the accuracy and objectivity of osteoporosis diagnosis are improved, and misdiagnosis and missed diagnosis are reduced.

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.

The invention belongs to the technical field of chronic wound depth analysis, and relates to a chronic wound depth analysis system based on image recognition. According to the method, more comprehensive and richer wound information is provided by acquiring the chronic wound image of the target patient, misdiagnosis and missed diagnosis can be reduced, the depth of the chronic wound can be judged more accurately, and by analyzing the initial depth level of the chronic wound image of the target patient, the accuracy of the chronic wound is improved. The chronic wound in-depth detection method of the target patient is determined, the in-depth detection requirement of the chronic wound of the target patient is judged, a more personalized detection scheme can be formulated, unnecessary examination and treatment are reduced, the economic burden and psychological stress of the patient are reduced, and the detection accuracy is improved by determining the chronic wound in-depth detection method of the target patient and performing detection and correction. The final depth grade of the chronic wound image of the target patient is analyzed, interference factors in the image can be recognized and corrected, the diagnosis accuracy is improved, and an important basis is provided for formulating a personalized treatment scheme.

The invention discloses an artificial intelligence auxiliary medical decision-making system, and relates to the technical field of medical assistance. Through the detection suggestion module, preliminary reasoning and observation index judgment can be performed according to patient chief complaint in combination with basic information, and detection suggestions are given in combination with detection cost budget of a patient; through the diagnosis module, preliminary judgment can be made according to detection abnormity, disease classification is judged based on the current diagnosis result, further detection suggestions are given, and finally a doctor makes further diagnosis or treatment suggestions; through the treatment suggestion module, disease causes can be judged according to the diagnosis result, and detection cost budget of a patient is combined, so that the diagnosis and treatment efficiency is improved. Giving out treatment suggestions; the detection suggestion module, the diagnosis module and the treatment suggestion module jointly act to form a medical decision-making system, non-common diseases can be found easily, the problem of cross-department missed diagnosis is solved, the medical budget planning function is achieved, and doctors and patients can provide accurate treatment suggestions easily.

The invention discloses a case analysis method and system based on a large language model, and belongs to the technical field of case analysis, and the method comprises the following steps: S1, patient information input, S2, examination item recommendation and determination, S3, preliminary diagnosis generation, S4, case screening and diagnosis and treatment suggestion generation, S5, diagnosis and treatment process monitoring and adjustment, and S6, case report generation and viewing. By means of the powerful knowledge integration and reasoning ability of a large language model, in combination with massive historical case data and professional medical database information, multi-dimensional diagnosis thought reference is provided for doctors, the misdiagnosis and missed diagnosis risks caused by personal experience limitation are reduced, the health indexes of patients can be monitored, and the diagnosis accuracy is improved. Personalized adjustment is performed on the diagnosis and treatment plan according to the disease change and the diagnosis and treatment condition of the patient; the medical database can dynamically expand and correct contents; diagnosis and treatment plans and suggestions can be given according to diagnosis and treatment passes of previously cured patients with similar conditions.

The present invention discloses an AI-based automated liver cancer tissue recognition and diagnosis system, which relates to the field of medical impact processing technology, including a data upload terminal, an image processing terminal, a liver cancer tissue algorithm recognition terminal, a liver cancer automatic diagnosis terminal and a user interface terminal; the data upload terminal is responsible for uploading the patient's liver image data to the cloud or local analysis platform of the system through a secure transmission channel for processing; the image processing terminal is responsible for preprocessing the acquired image data, including denoising, contrast enhancement and artifact correction operations; using a filtering algorithm to remove noise in the image; and using adaptive histogram equalization to increase the image contrast. The present invention can automatically extract the subtle features of liver cancer lesions and improve the recognition accuracy of different lesion morphologies; compared with the traditional subjective judgment that relies on the doctor's experience, the system shows higher sensitivity in detecting small lesions and early liver cancer, reducing the probability of misdiagnosis and missed diagnosis.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

The invention relates to the technical field of medical image processing, in particular to a method and system for identifying odontogenic jaw cyst and tumor based on deep learning, and the method comprises the steps: obtaining original jaw CBCT image data, carrying out the preprocessing, and generating a combined annotation data set containing lesion types, tooth root absorption form annotations and CT value annotations; constructing a multi-branch neural network model, and training the multi-branch neural network model by using the joint labeling data set to obtain an odontogenic jaw lesion recognition model; and obtaining jaw CBCT image data of a target patient, inputting the jaw CBCT image data into the odontogenic jaw lesion recognition model, and outputting lesion type classification results, tooth root absorption forms and bone destruction volumes. The diagnosis capability of minimal lesions can be improved, missed diagnosis is reduced, whole-process auxiliary diagnosis from qualitative diagnosis to quantitative analysis is realized, and the diagnosis speed is increased.

The invention relates to the technical field of orthodontic abnormality diagnosis, in particular to an orthodontic abnormality diagnosis intelligent prompt system based on deep learning, which comprises a multi-modal data acquisition module used for transmitting a patient ID (Identity) through an API (Application Program Interface), automatically importing image data, image data, facial three-dimensional data and dentition three-dimensional data of a patient, and transmitting the image data to the multi-modal data acquisition module; meanwhile, medical record text information is obtained in real time through an EMR interface; according to the method, collected data are diversified in form and comprehensive in content, anatomical structure information is provided for patient photos, skull side position X-rays, CBCT, 3dMD, IOS, medical record reports, image data, image data, three-dimensional data and the like, symptom description and medical history are supplemented through text data, multi-person data fusion is achieved, potential association which is difficult to discover through single-mode data can be captured, facial features of a patient are comprehensively evaluated, and the method is suitable for clinical application. The comprehensiveness and the accuracy of diagnosis are improved, more abundant diagnosis basis can be provided for complex mismatching deformity and multi-modal data fusion, and missed diagnosis and misdiagnosis are reduced.

The invention discloses a Parkinson's disease early diagnosis system based on eyeball movement characteristic analysis, and relates to the technical field of Parkinson's disease diagnosis. In order to solve the problems that although eye and face movement parameters possibly change in Parkinson's disease, the change of the parameters also possibly occurs in other neurodegenerative diseases or normal aging processes, and high-precision eye and face movement capture data is complex, so that early diagnosis characteristics of Parkinson's disease cannot be accurately captured, and the Parkinson's disease cannot be accurately diagnosed. And the accuracy and efficiency of diagnosis are influenced. By accurately capturing and analyzing eyeball movement data and facial expression video images of a patient, the Parkinson's disease can be effectively distinguished from other diseases possibly causing similar symptoms, misdiagnosis and missed diagnosis are reduced, the standardization and comparability of data are ensured by combining pupil distance information and diopter information, the consistency and reliability of diagnosis are improved, and the diagnosis accuracy is improved. Therefore, the patient can be diagnosed at the early stage of the disease and is treated as soon as possible, and the treatment effect and life quality are improved.

The invention discloses a digital-intelligence tumor screening platform and method, and relates to the technical field of digital-intelligence tumor screening, and the method comprises the steps: constructing a multi-modal fusion model of a fusion convolutional neural network and a recurrent neural network based on a deep learning framework, and outputting tumor comprehensive correlation features; a machine learning algorithm is utilized, large-scale tumor data are combined for training to construct a tumor risk prediction model, tumor comprehensive association features are input, and different tumor incidence probabilities are output. By means of the multi-modal fusion model, multi-source data such as images, clinic, genes and living habits are integrated, tumor characteristics are comprehensively captured, and the early-stage missed diagnosis rate of common tumors can be greatly reduced. Multi-source data are accessed in real time through a standardized interface, the multi-modal model completes feature extraction and risk prediction in a short time, and the time of the whole screening process is greatly shortened. The result of the visual report is presented by a visual chart, and by matching with popular character interpretation, the understanding degree of the patient on the screening result is improved, and the doctor-patient communication efficiency is remarkably improved.

The invention discloses an image recognition system for constructing osteoporotic vertebral fracture healing based on artificial intelligence, and particularly relates to the technical field of orthopedic treatment, which comprises a data preprocessing module for extracting and optimizing data from an original medical image. According to the method, the advantage of discipline crossing is developed, a recognition system on medical imaging is developed based on deep learning, the advantage that the recognition system can discover detail features and hidden rules which cannot be found by human beings is utilized, and then a large amount of structured data is utilized to automatically learn and train visual feature expression of abstract data, so that the clinical missed diagnosis rate is reduced, and the clinical diagnosis efficiency is improved. The artificial intelligence architecture is trained by a rigorous and scientific means, so that the artificial intelligence architecture has clinical experience and knowledge of high-age dominant doctors and is popularized in local hospitals to achieve an auxiliary diagnosis effect, the difference of medical differentiation is reduced, the technical and experience defects of local medical personnel are made up, and the technical and experience defects of the local medical personnel are overcome. The aim of improving the definite diagnosis rate of diseases and reducing the missed diagnosis rate is one of the purposes of the invention.

The invention discloses a sugar net recognition system based on a block chain and federal learning, and the system comprises a data collection module which is used for collecting fundus image data and related medical data; the federal learning module is connected with the data acquisition module, the federal learning module comprises a plurality of participant nodes, and each participant node performs model training based on local data; the block chain module is connected with the federated learning module and used for storing model parameters, training data abstracts and data access record key information in the federated learning process; the sugar network identification module is used for receiving the global sugar network identification model trained by the federal learning module; and the result output and feedback module is connected with the sugar net identification module. According to the method, the data collaborative training model is integrated through federal learning, so that the sugar net recognition accuracy is improved, misdiagnosis and missed diagnosis are reduced, the block chain ensures the data to be safe and credible, the innovative formula assists optimization, the system application is wide, clinical and remote medical treatment is facilitated, and the sugar net diagnosis service quality is improved.

The invention discloses a fault diagnosis method and device and a computer readable storage medium. The fault diagnosis method comprises the steps that hardware layer data, system layer data and application layer data are acquired; based on the hardware layer data, the system layer data and the application layer data, constructing fusion feature information; performing fault diagnosis based on the fused feature information to obtain fault diagnosis information; and generating a fault processing strategy based on the fault diagnosis information. According to the method, the multi-dimensional data of the hardware layer, the system layer and the application layer are acquired, the fusion feature information is constructed to perform fault diagnosis and generate the processing strategy, so that the performance of the fault at different levels can be comprehensively captured, the accuracy of positioning the fault reason is improved, missed diagnosis and misdiagnosis are reduced, the comprehensiveness and accuracy of fault diagnosis are improved, and the fault diagnosis efficiency is improved. Meanwhile, a user can conveniently and autonomously solve faults.

The invention relates to an antibody detection kit for neuroparatoma syndrome and a preparation method of the antibody detection kit. The antibody detection kit for neuroparatoma syndrome comprises an antibody detection strip, a cleaning solution, a buffer solution and an auxiliary agent, wherein the antibody detection strip is a nitrocellulose membrane which is coated with antigen proteins containing Amphiphysin, CV2, GAD65, Hu, PNMA2 (Ma2 / Ta), Recovery, Ri, SOX1, Titin, Tr (DNER), Yo and Zic4. The invention further discloses a detection method of the antibody detection strip. The antibody detection kit disclosed by the invention can be used for simultaneously detecting 12 autoantibodies related to the neuroparatoma syndrome. The design of simultaneous detection of multiple antibodies greatly improves the comprehensiveness of detection, so that multiple possible pathogenic factors can be covered in single detection, the kit can be used for differential diagnosis of suspected neuroparatoma syndrome patients in professional laboratories, and the problem of missed diagnosis caused by single antibody detection is avoided.

The invention provides a time sequence-based blood transfusion prediction method for postoperative blood conditions of patients, which belongs to the technical field of medical treatment, and comprises the following steps of: firstly, collecting basic information, past history, operation diagnosis information, blood information and curve information of a plurality of patients, screening out patients with normal data, and determining whether the patients are normal or not; the method comprises the following steps: firstly, fitting a postoperative hb recovery curve model of normal people, and then, combining the basic information and blood information of the patient collected before after each patient; according to the method, the evaluation of the postoperative condition of the patient is more targeted and accurate through the calculation of the deviation degree, a doctor can be helped to more accurately judge the postoperative condition of the patient, the risk of misdiagnosis or missed diagnosis is reduced, and more information can be provided to help the doctor to formulate a personalized treatment scheme; and meanwhile, manual participation is effectively reduced, the working efficiency is improved, the concept of the deviation degree also provides a new research view angle for researchers, and the method has wide application prospects.

The invention relates to the technical field of medical treatment, and discloses a patient cognitive impairment recognition method and system and electronic device.The method comprises the steps that patient original data are obtained, and the patient original data comprise clinical biochemical data, image data and cognitive behavior data; preprocessing the original data of the patient to obtain preprocessed data; feature extraction is conducted on the preprocessed data, key features are obtained, and the key features comprise clinical biochemical features, image features and cognitive behavior features; fusing the clinical biochemical features, the image features and the cognitive behavior features to obtain fused features; and determining a cognitive impairment recognition result based on the fusion features and the cognitive impairment recognition function. According to the embodiment of the invention, the early detection rate of cognitive impairment of the pituitary adenoma patient is improved, missed diagnosis and misdiagnosis are reduced, and the early warning cognitive risk when obvious clinical symptoms do not appear in cognitive impairment recognition of the pituitary adenoma patient is reduced.

The invention discloses an emergency rescue room multi-parameter collaborative diagnosis and treatment system based on intelligent chip operation, and relates to the technical field of emergency rescue. The system comprises a data acquisition and preprocessing module, an intelligent chip operation module, a diagnosis decision support module, a multi-department collaboration module, a data storage and management module and a user interface module. According to the system, multi-parameter data is collected and integrated in real time, analysis is carried out by utilizing the rapid operation capability of the intelligent chip, accurate diagnosis suggestions and treatment schemes can be provided for medical staff in a short time, the diagnosis and treatment decision time is greatly shortened, and the emergency rescue efficiency is improved. Various physiological parameters are comprehensively analyzed, the limitation of single parameter analysis is avoided, the illness state of a patient can be judged more comprehensively and accurately, and misdiagnosis and missed diagnosis are reduced.