33 results about "Delirium" patented technology

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 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.

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 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 relates to the technical field of patient restraint devices, and discloses a rapid unfolding type delirium patient cooperative restraint device which comprises a sickbed, shaft bodies connected to the two sides of the sickbed and first rotating seats rotationally connected to the two ends of the shaft bodies. An upper limb constraining unit and a lower limb constraining unit are further arranged; linkage rods are arranged between the upper limb binding units and the lower limb binding units, and detachable binding belts are connected between the upper limb binding units; according to the intelligent delirium restraint device, the upper limb restraint units are rapidly rotated to the arms of the patient, the lower limb restraint units are synchronously driven to rotate to the legs of the patient through the linkage rods, finally, the chest and abdomen of the patient are restrained through the restraint belts, the upper limb restraint units and the lower limb restraint units can be rapidly unfolded, timely response can be achieved, and the delirium patient can be restrained; and the binding work of the upper limb binding unit and the binding work of the lower limb binding unit can be coordinated through a linkage rod, and the overall binding effect is improved.

Disclosed herein are methods of administering dexmedetomidine or a pharmaceutically acceptable salt thereof to a human subject. The disclosed methods are particularly suitable for the treatment of agitation, especially when associated with neurodegenerative and / or neuropsychiatric diseases or disorders such as dementia and delirium.

According to at least one embodiment, a method of determining a predictive index indicating a level to which a human subject is at risk of developing hypoactive delirium includes: extracting first features of first audio content and first video content continuously capturing the human subject in a setting over a first period; establishing a behavioral baseline specific to the human subject based on the extracted first features; providing the established behavioral baseline to a neural network; extracting second features of second audio content and second video content continuously capturing the human subject in the setting over a second period subsequent to the first period; providing the extracted second features to the neural network for determining the predictive index based on the established behavioral baseline and the extracted second features; and outputting an alert based on the determined predictive index being above a threshold value.

The utility model belongs to the technical field of restraint gloves, and particularly relates to a delirium patient restraint glove which comprises a restraint glove body for a delirium patient, the restraint glove body is composed of a base, a wrist fixing belt, a grip ball and a warm keeping cover, a lithium battery, an electric heating element and a thermistor are arranged in the base, and the wrist fixing belt is arranged on the base. A penetrating hole and a connecting groove are formed in the base, a first hook-and-loop fastener and a first adhesive layer are arranged on the wrist fixing belt and used for restraining and fixing the wrist of the delirium patient, a finger ring, a connecting base and an air nozzle are arranged on the grip ball, the connecting base is connected with the connecting groove in a matched mode, a zipper and a second adhesive layer are arranged on the warm keeping cover, and the air nozzle is connected with the air nozzle. In the using process, medical staff can directly observe key indexes such as the skin color and temperature of the hands of a patient conveniently, meanwhile, the hand training, hand blood circulation promoting and hand warm keeping can be facilitated for the patient, and therefore the nursing quality and the rehabilitation effect of the delirium patient can be improved.

There is provided an aqueous solution comprising a quetiapine salt and either or both of a benzoic acid salt and a citric acid salt. Preferably the concentration of the quetiapine salt is at least 0.1 mg / ml to about 5 mg / ml. The solutions are useful for the prevention and treatment of delirium, particularly in an intensive care setting.

Disclosed is a method of determining a subject's susceptibility to by collecting a biological sample from the subject; determining the methylation status of at least one CpG sequence in the biological sample from the subject; and comparing the methylation status in the biological sample to an established threshold and determining whether or not the subject is likely to suffer delirium. In certain aspects, the at least one CpG sequence is located in a gene selected from TNF-alpha, BNDF, GDNF, LDLRAD4, DAPK1, and IRF8

The invention provides an ICU multi-injury patient delirium risk real-time hierarchical management method and system, and the method comprises the steps: obtaining the multi-modal data of a patient, carrying out the preprocessing and standardization processing of the multi-modal data, and obtaining the standardized multi-modal data; performing feature extraction on the standardized multi-modal data to obtain a structured data set of the standardized multi-modal data; inputting the structured data set into a multi-algorithm parallel model, integrating output results of all algorithm models based on a soft voting strategy, and taking the model output result with the optimal comprehensive score as a prediction result; based on the prediction result, matching hierarchical levels meeting the prediction result; a personalized auxiliary intervention scheme is generated on the basis of the hierarchical level of the patient, and through risk hierarchical management, medical resource allocation is optimized, unnecessary excessive intervention is reduced, and it is ensured that the high-risk patient gets enough attention; according to the intelligent nursing and intervention system, personalized nursing and intervention schemes are provided, the nursing quality is effectively improved, the intervention schemes are adjusted in time through real-time feedback and dynamic updating, and the delirium occurrence rate is reduced.