51 results about "Delirium" patented technology

The invention relates to a perioperative delirium risk prediction method and device and a storage medium, and belongs to the technical field of big data processing. The method comprises the steps of obtaining to-be-predicted multi-modal data; encoding the multi-modal data, and extracting global state features; fusing information of different modes through a multi-mode cross attention mechanism to generate fusion features; the fusion features are combined with coding information to be input into a recursive reasoning module, and global state features at future time points are gradually reasoned; inputting the global state features at the future time point into a perioperative period delirium risk prediction model, and generating a perioperative period delirium risk probability at the future time point; and generating a perioperative period delirium risk probability sequence in a time sequence form through recursive reasoning. According to the method, multi-dimensional perioperative delirium risk factors are covered; the capturing capability of feature interaction and association between modals is improved; the change trend of the perioperative period risk can be dynamically predicted, and the interpretability and practicability of the model are enhanced.

The utility model discloses a safe special bed for delirium patients, and relates to the technical field of sickbeds. A safe special bed for delirium patients comprises armrest assemblies arranged on the two sides of a bed body, and the armrest assemblies are rotationally arranged towards the outer side of the bed body; the first rotating arms are symmetrically and rotationally arranged on one of the armrest assemblies; a second rotating arm; the first rotating arm and the second rotating arm are pulled out of the containing groove in the armrest assembly, the first rotating arm and the second rotating arm are connected, the first rotating arm and the second rotating arm form transverse constraint on a patient on the bed body, the constraint positions of the arms and the feet can be adjusted through the connecting sleeve which is arranged in a sliding mode, and therefore the patient can be conveniently and rapidly bound. And meanwhile, the first rotating arm and the second rotating arm are transversely arranged on the bed body, so that the patient can be further constrained, and the arranged cross arm can be used for re-constraining the whole body of the patient, so that the constraining effect is further improved.

The invention discloses a fusion method for balancing medical text positive and negative sample training, belongs to the field of medical text classification crossing natural language processing and medical informatics, and solves the problems of long text information loss, labeling noise interference and positive and negative sample imbalance in the prior art. The method comprises the steps of preprocessing a medical text, defining five types of medical entities such as disease diagnosis and the like, and labeling and establishing entity association; segmenting a long text by using a sliding window, extracting text features based on BioBERT, adding a gradient inversion layer, and setting a classifier and a discriminator; in positive and negative training, a weighted cross entropy loss function is used for learning real label mapping of a text in positive training, and a noise suppression loss function is used for reducing the influence of noise on model learning in negative training. According to the method, the delirium-symptom-containing medical record can be accurately identified, and the accuracy and robustness of medical text classification are improved.

Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and / or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and / or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

The application discloses a kind of elderly hip fracture surgery perioperative adverse event real-time monitoring system and method, it is related to perioperative monitoring technical field.The method includes: real-time acquisition perioperative monitoring data, carries out data pre-processing, and extracts benchmark feature;Identify the decline segment of cerebral oxygen saturation below benchmark feature, jointly assess the decline amplitude and persistence, obtain the weak trend characterization of brain oxygen;Analysis of the relative change characteristics of serum inflammatory markers, form the weak trend characterization of inflammation;Assess the deviation degree of vital signs relative to benchmark, obtain the vital signs fluctuation characterization, and further judge the delirium precursor risk, output risk prompt;Visual display various weak trend characterizations and delirium precursor risk, link label risk trigger information and contribution source.The present application solves the technical problems that the prior art is difficult to extract and identify early weak abnormal trend in perioperative multi-source physiological signals.

The invention relates to an ICU patient delirium risk early screening system based on AI electrocardiogram analysis. The system comprises a neural signal extraction module, a component mode determination module, a delirium type identification module, a risk trajectory prediction module and a risk alarm generation module, wherein the neural signal extraction module extracts neuromodulation related signal fragments based on an electrocardiogram signal sequence and a preset neuromodulation specificity index; a component mode determination module extracts time sequence features from the fragments and determines an abnormal component distribution mode; the delirium type identification module is used for matching potential delirium types through the feature database; the risk trajectory prediction module predicts a risk trajectory in combination with a long-short-term memory network; a risk alert generation module evaluates a risk level and generates an alert. With the adoption of the system, early and dynamic screening of delirium risks can be realized, the screening accuracy and timeliness are improved through multi-module collaborative analysis, and accurate monitoring support is provided for ICU (Intensive Care Unit) patients.

The invention relates to the field of medical health, in particular to a perioperative delirium personalized intervention method for old orthopedic patients. The method comprises the following steps: acquiring data of an old orthopedic patient and evaluating a delirium occurrence risk to obtain a delirium risk score; based on the old orthopedic patient data and the delirium risk score, evaluating the applicability of intervention measures, selecting the intervention measure with the highest applicability, and outputting an optimal intervention combination scheme; calculating an intervention effect expected gain of the optimal intervention combination scheme based on the delirium risk score and the applicability score of the intervention measure; data of the old orthopedic patients are obtained again, the delirium occurrence risk is evaluated, and the optimal intervention combination scheme is updated. The problems that traditional delirium risk assessment neglects individual differences of patients, which may cause inaccurate risk assessment and improper intervention measures are solved; the technical problems that a traditional intervention scheme often cannot accurately adjust mutual influence among measures and is difficult to flexibly adjust according to actual response of a patient are solved.

This invention discloses a hand restraint system suitable for patients with delirium, comprising an outer sleeve shell, a protective layer, and a pressure control and monitoring system. The pressure control and monitoring system is disposed within the outer sleeve shell and is communicatively connected to a central monitoring system located at the nurses' station. This invention effectively combines the outer sleeve shell, the protective layer, and the pressure control and monitoring system to not only effectively fix the wrist of a delirium patient but also significantly improve the comfort of the fixation, making operation more convenient. Furthermore, it allows for real-time monitoring of the restraint force on the delirium patient's wrist. When abnormalities in the restraint force occur, medical staff can promptly detect and take appropriate action, greatly facilitating hand restraint procedures for delirium patients.

The utility model discloses a wearing mask for monitoring a neurological critical patient. A window opening and closing part for adjusting the monitoring condition of the critical patient is formed on a mask body; a body temperature detection sensor is arranged in the area, corresponding to the forehead of a monitored object, of the mask body and used for measuring the body temperature of the monitored object. A pupil light reflex monitoring assembly is arranged in the mask body, a light source module of the pupil light reflex monitoring assembly is used for emitting light, and a camera module is used for shooting a pupil light reflex image; a pulse sensor and a blood pressure and blood oxygen sensor are arranged at the tail end of the expansion part below the mask body, and the pulse sensor is used for conducting pulse monitoring on the carotid artery of a monitored object; the blood pressure and blood oxygen sensor is used for conducting blood pressure and blood oxygen monitoring on the carotid artery of the monitored object. The intelligent monitoring device solves the problems of sleep stripping and delirium of a neurological critical patient caused by environmental interference of an intensive care unit, is comfortable to wear, realizes humanized monitoring at the same time, reduces the workload of neurological critical medical care, and improves the working efficiency.

The invention discloses a perioperative delirium risk classification method and system for unbalanced medical data, and relates to the field of computer data processing and machine learning. Comprising the following steps: extracting relevant data of examination and surgical anesthesia of a patient, and marking relevant data corresponding to delirium; constructing a delirium risk classification model, inputting the marked related data into the delirium risk classification model, and training the delirium risk classification model by utilizing a method for dynamically adjusting a category balance factor and a classification difficulty factor; and inputting to-be-evaluated perioperative period patient data into the trained delirium risk classification model, and performing classification prediction on the delirium risk in the perioperative period. The gradient calculation process is reconstructed on the basis of the gradient boosting tree, the problem of model training deviation caused by scarcity of positive samples in medical data is solved, and doctors are assisted in efficiently completing scientific research and clinical work.

Disclosed are systems and methods that provide a novel computerized framework for a closed-loop, decision-intelligence (DI)-based computerized framework for automatically and dynamically managing and controlling a medical procedure, inclusive of administered medication and / or anesthesia to a patient and an intraoperative level of consciousness of the patient. The disclosed framework provides an improved electroencephalography (EEG) indices that adapts to specific patient needs, and dynamically adapts to factors of an ongoing procedure to ensure that the proper levels of anesthesia are administered, required and / or maintained. This provides computerized capabilities to maintain safe levels of the patient's consciousness, such that post-operative patient health is preserved and maintained. Thus, the disclosed framework provides an effective anesthesia management framework that can be leveraged to safely manage a patient's health during and after a medical procedure for which anesthesia is used.

Methods and compositions for treating a patient suffering from Huntington's disease or treating symptoms thereof are disclosed. The methods comprise administering to the patient an effective amount of a heteroaryl ketone fused aza-naphthane glucocorticoid receptor modulator (GRM) or an octahydro fused aza-naphthane GRM. In an embodiment, the GRM is Dargencolan. In some embodiments, the GRM is zaltile blue. In embodiments, the GRM is administered orally. In some embodiments, the GRM is administered daily; in other embodiments, the GRM is administered in a regimen, such as once every day, once every three days, once per week, or other administration regimen. The symptoms of Huntington's disease that can be treated by the methods described herein include, but are not limited to, motor symptoms (e.g., muscle weakness, posture abnormalities, walking difficulty, dysphagia) and neurological or psychological symptoms (e.g., seizure, amnesia, consciousness disorder, impaired verbal ability, delirium, depression, anxiety).

The invention discloses a delirium patient self-adaptive pressure adjusting and restraining glove and a using method thereof, and belongs to the technical field of medical auxiliary instruments, the delirium patient self-adaptive pressure adjusting and restraining glove comprises a glove body, the glove body is composed of a wrist part, a palm part and a finger part, the glove body is divided into an outer protective shell, a middle pressure sensing adjusting layer and an inner skin-friendly contact layer, the wrist part is provided with a detachable wrist self-locking fastening mechanism, and the wrist self-locking fastening mechanism restrains the glove body on the wrist of the patient. Aiming at the core problem that the delirium patient moves restlessly and is easy to pull out the catheter, through the design of a finger constraint structure, self-adaptive pressure adjustment and the like, the finger grasping action is accurately blocked, accidental pull-out of the diagnosis and treatment catheter is avoided, serious complications such as airway obstruction, bleeding and infection are reduced, the life risk of the patient is reduced, and the hospitalization time is shortened.

The invention discloses a shielding self-adaptive delirium monitoring system and method for an ICU (Intensive Care Unit) patient, and relates to the technical field of visual processing, and the method comprises the following steps: collecting continuous video streams and clinical metadata in a delirium monitoring process of the ICU patient; extracting a regional feature vector of the patient from the continuous video stream, wherein the region comprises a face region and a limb region; coding the clinical metadata into semantic vectors; performing interaction enhancement on the regional feature vector and the semantic vector to obtain a fusion feature vector; and outputting a delirium stage identification result according to the fusion feature vector. According to the invention, by constructing the occlusion perception adaptive fusion mechanism and dynamically adjusting the fusion weight of the multi-region features, stable monitoring in a complex ICU environment is realized, the delirium risk can be early warned, a key window is provided for clinical early intervention, and the problem that the face of a patient is seriously occluded by medical equipment, so that the face of the patient is affected is solved. The visual monitoring method is poor in robustness, and continuous and effective monitoring cannot be realized.

Exemplary system, method and computer-accessible medium can be provided to monitor electroencephalography (EEG) data from the patient during an emergence from a general anesthesia previously provided to the patient, and predicting neurocognitive impairment based on a slope of EEG power. The neurocognitive impairment predicted can be delirium or it may be a predictor of long-term impairment such as Alzheimers. Further, the system and method can be used to direct a medical intervention based on the preditcted neurocognitive impairment.

The application relates to the technical field of patient accompanying, and specifically provides an intelligent multi-dimensional management system for critical patients, which comprises an intelligent display terminal, an accompanying program is built in the intelligent display terminal, a fingertip controller, the accompanying program is controlled by the fingertip controller through acquisition of finger actions to select corresponding functions, wherein the fingertip controller comprises a direction control module, finger actions are acquired to control a cursor in the intelligent display terminal to move up, down, left and right along a screen, and a determination button module is used for acquiring actions of another finger to input confirmation and power-on / off instructions; the accompanying program in the intelligent display terminal enables the critical patient to make a video call with family members, watch videos and play small games, a rehabilitation mode with full participation of the family members is created to relieve the fear and anxiety of the critical patient, and thus problems such as anxiety, panic and delirium of the patient in an intensive care unit are prevented.

The utility model belongs to the technical field of medical supplies, and particularly discloses a constraint robe for delirium patients. The robe comprises a robe body, a lining is arranged in the robe body, fixing assemblies are arranged on the two sides of the robe body and the two sides of the lining, the lining is located in the robe body, the two sides of the robe body are connected with gloves, and restraining assemblies are arranged in the two gloves. The situation that a patient takes off a pulse oxygen clamp in gloves and digs out the net face end is avoided, the nursing problem after a delirium patient is covered with a quilt is solved, the observation blind area of a nurse is reduced, the patient is prevented from rubbing off a pipeline in the quilt, stimulation caused by the fact that the patient lifts the quilt due to body temperature changes after the operation is avoided, and therefore the adverse effect on postoperative recovery is reduced. Meanwhile, nurses can conveniently observe the condition of the patient, unnecessary stimulation of the patient is reduced, more comprehensive and effective constraint and protection are provided for the delirium patient, and the nursing quality and the rehabilitation effect of the patient are improved.

The present invention provides a novel prophylactic or therapeutic agent for neurodegenerative diseases or delirium. The prophylactic or therapeutic agent for neurodegenerative diseases or delirium contains a heterocyclic compound represented by formula (I) or a salt thereof as an active ingredient (in the formula, ring A is a 5- to 7-membered heterocycle containing one or two heteroatoms selected from among a nitrogen atom, an optionally oxidized sulfur atom, and an oxygen atom; R1, R2, R3, and R4 are each independently a hydrogen atom, a C1-6 alkyl group, a halogen atom, an amino group, -SH, a C1-6 alkylthio group, a C2-6 alkenyl-thio group, a C1-6 alkyl-carbonyl group, a formyl group, a C6-10 aryl group, a C1-6 alkoxycarbonyl group, a 5- or 6-membered heteroaryl group, or an oxo group; R1 and R2 may be bonded to each other to form an optionally substituted 5- or 6-membered ring; and n is 0, 1, or 2).

The invention discloses an application of an mGluR5 antagonist MPEP in preparation of a sepsis-related delirium product, and relates to the technical field of biomedicine.According to the application, hippocampus mGluR5 signal transduction and cone neuron excitability are adjusted in an endotoxin-induced sepsis-related delirium mouse model to relieve delirium behaviors and recover inhibitory synaptic input, so that the sepsis-related delirium product can be prepared, and the delirium behavior can be relieved; neuron damage is reversed.

The invention provides a delirium risk assessment method and system, a terminal and a medium, and the method comprises the steps: firstly obtaining multi-dimensional clinical data of a critical skin disease patient, carrying out delirium influence feature importance sorting on the multi-dimensional clinical data to determine delirium influence key features, and then obtaining delirium risk assessment training data; then adopting the training data to adjust the CHAID decision tree model, and constructing to obtain a first delirium risk assessment model; optimizing a terminal node of the first delirium risk assessment model through a Bayesian network framework to obtain a second delirium risk assessment model; and finally, performing delirium prediction on the critical skin disease patient by adopting the second delirium risk assessment model, and outputting a delirium risk assessment result of the patient, so that association logic of key features and delirium can be presented, medical personnel can intuitively trace risk sources, the model interpretability is met, and the risk assessment efficiency is improved. And the risk classification result of the CHAID can be converted into an accurate delirium occurrence probability, so that the prediction accuracy is improved.

The disclosed apparatus, systems and methods relate to predicting, screening, and monitoring for delirium. Systems and methods may include receiving one or more signals from one or more sensing devices; processing the one or more signals to extract one or more features from the one or more signals; analyzing the one or more features to determine one or more values for each of the one or more features; comparing at least one of the one or more values or a measure based on at least one of the one or more values to a threshold; determining a presence, absence, or likelihood of the subsequent development of delirium for a patient based on the comparison; and outputting an indication of the presence, absence, or likelihood of the subsequent development of delirium for the patient.

The present invention relates to a system and a method for creating a simulated environment for a subject undergoing and waking up from anesthesia. The system comprises a stimulation module (12), a subject monitoring module (14), and a controller (16). The stimulation module (12) comprises one or more sensory stimulation devices configured to apply at least one sensory stimulus over a subject to simulate an environment that will be experienced by the subject undergoing and waking up from anesthesia. The subject monitoring module (14) comprises one or more sensors configured to acquire sensor data from the subject. The controller (16) is configured to determine a sedation state of the subject based on the acquired sensor data and to control at least one of the one or more sensory simulation devices to modify the simulated environment based on the sedation state of the subject. The system may be used to create an ambient experience for a gentle transition into and / or from anesthesia. For example, the system may create an adaptive elements in the story to prevent or reduce the subject's mental confusion, thereby increasing temporal structure, so that the risk for getting delirium becomes smaller. In addition, the system may reduce anxiety upon the waking up. Furthermore, by reducing the subject's mental confusion and anxiety, the length of stay in the hospital may also be reduced.

The present invention discloses a method for constructing a risk prediction model for post-stroke delirium, which relates to the technical field of big data processing. By integrating different types of research and individual data of patients, a comprehensive risk factor data set D is established, covering key information of general information of patients, past medical history and symptoms after admission. This multi-dimensional data collection method avoids neglecting individual characteristics of patients and solves the defect that individual differences cannot be accurately adapted in existing models. By calculating the risk factor association matrix A and the matching factor Mm, it is ensured that the association between different symptoms and risk factors is accurately captured and analyzed, especially by screening out the gradient marking state, the prediction flexibility and accuracy of the model are further enhanced. The complication manifestation set M_Fi is matched with the patient data, and the personalized delirium risk Pd is calculated. By comparing with the preset risk assessment threshold Td, a personalized risk assessment strategy is generated.

The invention relates to the technical field of clinical nursing decision making, and discloses an auxiliary decision making method and system for delirium nursing, and the method comprises the steps: obtaining delirium-related multi-dimensional data; normalizing the multi-dimensional data to obtain normalized data; performing feature mining and correlation analysis on the standardized data through a preset analysis model to obtain a feature correlation analysis result; generating a preliminary nursing decision suggestion through a feature correlation analysis result; carrying out adaptability verification on the preliminary nursing decision suggestion to obtain a verification result; and optimizing the nursing decision suggestions in combination with the verification result to obtain a final delirium nursing aid decision scheme. According to the method, a complete technical path of multi-source data standardization integration, feature association intelligent analysis, adaptability verification and closed-loop optimization is constructed, so that the core problem that an existing method is lack of multi-source data standardization integration, dynamic adaptation optimization and closed-loop mechanisms is solved; the delirium nursing decision is converted from single data support and static generation to full-dimensional data drive and dynamic adaptation.

Disclosed herein are methods of administering relatively high doses of dexmedetomidine or a pharmaceutically acceptable salt thereof to a human subject, without also inducing significant sedation. The disclosed methods are particularly suitable for the treatment of agitation, especially when associated with neurodegenerative and / or neuropsychiatric diseases such as schizophrenia, bipolar illness such as bipolar disorder or mania, dementia, depression, or delirium.

The application discloses a nursing device for a critical delirium patient, and belongs to the technical field of medical nursing, which comprises a support plate for the patient to lie on, a fence plate, a fixed column, an extension rod, a first driving mechanism and a second driving mechanism; the side end of the fence plate and the side end of the support plate are slidably connected in the vertical direction; the extension rod is hollow and one end of the extension rod is fixed with the fixed column, and the other end of the extension rod is fixed with the lower end of the support plate; the first driving mechanism comprises a first transmission rod, a fixed seat, a first connecting rod, a buffer, a second connecting rod, a transmission plate, a first transmission wheel, a first sliding rail rod and a sliding rod. The device has the advantages of preventing the delirium patient from falling off the sickbed when the delirium patient has an attack and improving the comfort of the patient.

The invention discloses a closed-loop controlled intelligent sedation depth and delirium detector, and relates to the field of clinical assistance, the closed-loop controlled intelligent sedation depth and delirium detector comprises a head-mounted mechanism, the head-mounted mechanism comprises a cap body, and a semi-arc ring is arranged below the cap body; according to the intelligent sedation depth and delirium detector, the electrode plates are pre-arranged in the containing groove and then are led out in order through the signal line, traditional scattered pasting is avoided, and the wearing efficiency and the positioning consistency are improved; the supporting mechanism realizes rapid height adjustment and locking by virtue of a vertical rod, a limiting rod, a limiting hole and a reset spring, and a camera can be accurately mounted and firmly fixed without a tool in cooperation with a rapid clamping structure consisting of an arc-shaped clamp, a spring damper and a deflector rod and a horizontal fine adjustment capability provided by a magnetic block and an adaptive groove; the electrodes and the camera share the same head-mounted platform, and the spatial relationship of the electrodes and the camera is fixed, so that the relative position can be kept stable even if the patient slightly moves.

The application discloses an ICU patient risk early warning monitoring method and system based on time series data analysis, relates to the technical field of medical information processing and artificial intelligence, and takes synchronously collected electroencephalogram time series signals and electrocardiogram time series signals as original inputs, extracts an autonomic nervous regulation signal from the electrocardiogram signals through heart rate variability analysis, carries out individualized multi-scale decomposition on the electroencephalogram signals and the heart rate variability signals by using adaptive wavelet packet decomposition, separates out a nervous frequency band component and an autonomic nervous frequency band component, extracts the instantaneous phase of each frequency band component by using Hilbert transformation, calculates a cross-frequency band phase locking index to quantize the brain-heart cross-system phase synchronization strength, continuously generates brain-heart coupling features along a time axis sliding window, compares the brain-heart coupling features with an established personalized baseline reference mode, identifies a coupling mode collapse sequence feature, generates an early warning signal when the feature is identified, and realizes early warning of delirium risk of ICU patients.

Exemplary system, method and computer-accessible medium can be provided to monitor electroencephalography (EEG) data from the patient during an emergence from a general anesthesia previously provided to the patient, and predicting neurocognitive impairment based on a slope of EEG power. The neurocognitive impairment predicted can be delirium or it may be a predictor of long-term impariment such as Alzheimers. Further, the system and method can be used to direct a medical intervention based on the predicted neurocognitive impairment.

The invention provides a wearable sensor alarm apparatus for automatically monitoring delirium, and relates to the field of medical apparatuses and instruments. The wearable sensor is used for automatically monitoring the delirium alarm apparatus and comprises a controller, a wearable sensor, a photoelectric sensor and a detection electrode, the outer side of the wearable sensor is connected with the detection electrode, the photoelectric sensor is arranged in the wearable sensor, the photoelectric sensor and the wearable sensor are both connected with the controller, and the controller is connected with the controller. The output end of the controller is connected with a receiving terminal. The alarm apparatus for monitoring the delirium is applied to early discovering the delirium of the patient, the prognosis of the patient can be improved, the hospitalization time can be shortened, the medical cost can be reduced, the death rate can be reduced, and the occurrence of bed falling and falling caused by abnormal cognition due to the delirium can be avoided.