A comprehensive monitoring and emergency care system for emergency department patients

Abstract

This invention relates to the field of emergency medicine technology, specifically to a comprehensive monitoring and emergency care system for emergency department patients. The system includes a patient information management unit, a real-time monitoring unit, a behavioral feature recognition unit, an intelligent early warning and decision support unit, and a remote collaboration and communication unit. Through a trauma-specific multimodal monitoring architecture and dynamic anti-interference mechanism, this invention achieves precise and continuous monitoring of hemodynamics and vital signs in acute trauma patients, effectively identifying critical conditions such as occult shock. Furthermore, by utilizing non-contact multimodal sensing and intelligent behavioral analysis technology, it transforms the patient's surface temperature, subtle body movements, and voiceprints into clinical risk indicators, providing early warnings of potential risks. This offers multi-dimensional, high-precision monitoring and early warning for emergency trauma patients, significantly improving the timeliness and accuracy of emergency diagnosis.

Description

Technical Field

[0001] This invention relates to the field of emergency medicine technology, specifically to a comprehensive monitoring and emergency care system for emergency department patients. Background Technology

[0002] Emergency room patients are those whose conditions are critical or potentially life-threatening due to sudden illness, accidental injury, or acute trauma, requiring immediate medical attention at a hospital's emergency department. These patients often present with rapid onset, rapid progression, and complex conditions, necessitating swift assessment, diagnosis, and intervention by medical staff to save lives or prevent deterioration. Rapid and accurate monitoring and assessment of the patient's condition are crucial for improving the success rate of resuscitation. With advancements in medical technology, emergency care places higher demands on the timeliness and accuracy of monitoring systems. However, existing technologies, particularly traditional monitoring systems, suffer from delays in identifying hidden injuries and untimely emergency warnings due to lagging physiological indicators and limitations of single-dimensional assessment.

[0003] Based on this, the present invention provides a comprehensive monitoring and emergency care system for emergency department patients to solve the aforementioned technical problems. Summary of the Invention

[0004] The purpose of this invention is to provide a comprehensive monitoring and emergency care system for patients in the emergency department. This invention features a trauma-specific multimodal monitoring architecture and a dynamic anti-interference mechanism, enabling precise and continuous monitoring of hemodynamics and vital signs in patients with acute trauma. It effectively identifies critical conditions such as occult shock and utilizes non-contact multimodal sensing and intelligent behavioral analysis technology to transform patients' body surface temperature, micro-positional movements, and voiceprints into clinical risk indicators, providing early warnings of potential risks. This provides multi-dimensional and high-precision monitoring and early warning for emergency trauma patients, significantly improving the timeliness and accuracy of emergency diagnosis.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] This invention provides a comprehensive monitoring and emergency care system for patients in the emergency department, comprising a patient information management unit, a real-time detection unit, a behavioral feature recognition unit, an intelligent early warning and decision support unit, and a remote collaboration and communication unit, wherein:

[0007] The patient information management unit is used to quickly input and prioritize key information of emergency patients through automatic identification technology and dynamic data tagging.

[0008] The real-time detection unit is used to perform accurate hemodynamic assessment and continuous vital sign monitoring of acute trauma patients through a trauma-specific multimodal monitoring architecture and dynamic anti-interference mechanism.

[0009] The behavioral feature recognition unit is used to dynamically identify and assess patients' clinical risk indicators in real time in an emergency environment through non-contact multimodal perception and intelligent behavioral analysis.

[0010] The intelligent early warning and decision support unit: based on the trauma progression prediction algorithm and the ISS score correlation model, it conducts risk warning and generates a graded intervention plan;

[0011] The remote collaboration and communication unit is used to support real-time participation of interdisciplinary teams in the treatment of critically ill patients by utilizing AR annotation and secure data sharing.

[0012] The patient information management unit includes an automatic identity recognition module, a data acquisition module, a priority marking module, and an information synchronization module, wherein:

[0013] The automatic identity recognition module is used to quickly identify and bind the identity of emergency patients through barcode scanning, RFID or facial recognition technology.

[0014] The data acquisition module is used to automatically acquire key medical data such as patient admission information, basic medical history, and allergy history.

[0015] The priority marking module is used to dynamically mark the patient's treatment priority based on the degree of trauma and vital signs.

[0016] The information synchronization module is used to synchronize patient information to relevant monitoring and treatment systems in real time.

[0017] The real-time detection unit includes a dual-mode monitoring module, a motion artifact suppression module, and a trauma-specific monitoring module, wherein:

[0018] The dual-mode monitoring module is used to simultaneously run impedance cardiometry and laser Doppler to jointly assess bleeding volume and cardiac function.

[0019] The motion artifact suppression module is used to dynamically filter signal noise caused by patient movement using IMU sensor data.

[0020] The trauma-specific monitoring module is used to calculate intra-abdominal pressure and perfusion index in real time and to identify occult shock.

[0021] The trauma-specific monitoring module calculates intra-abdominal pressure and perfusion index in real time to identify occult shock. The specific operation is as follows:

[0022] A1: Intra-abdominal pressure measurement:

[0023] 50 ml of normal saline was instilled into the patient's bladder through a catheter. The patient was kept in a supine position, and the intravesical pressure was measured at the pubic symphysis level. ;

[0024] Formula for calculating intra-abdominal pressure: ,in, Intra-abdominal pressure, The current atmospheric pressure is used, and the corrected measurement error is ≤2 mmHg;

[0025] A2: Perfusion Index Calculation:

[0026] ①Calculation of gastric mucosal pH:

[0027] 1) Gastric mucosal carbon dioxide partial pressure was collected via nasogastric tube;

[0028] 2) Simultaneously collect arterial blood carbon dioxide partial pressure;

[0029] 3) Calculation formula: ,in, The value is the pH of the gastric mucosa, and 0.03 is the carbon dioxide solubility coefficient.

[0030] ② Central venous oxygen saturation monitoring: Blood samples are collected through a central venous catheter, and central venous oxygen saturation is measured using spectral analysis, with an accuracy of up to [insert accuracy here]. ;

[0031] A3: Logic for identifying latent shock:

[0032] A latent shock warning is triggered when any of the following conditions are met:

[0033] 1) IAP ≥ 16 mmHg, and ;

[0034] 2) IAP < 16 mmHg, but pHi < 7.30, ScvO2 < 65%, and lactate value Lac ≥ 2.5 mmol / L;

[0035] A4: Warning levels are based on the formula. Calculate, when It was determined to be high risk at that time.

[0036] The behavior feature recognition unit includes a non-contact sensing module and an intelligent behavior analysis module, wherein:

[0037] The non-contact sensing module is used to capture abnormal body surface temperature, slight body movements, and pain-related voiceprint features of patients through infrared thermal imaging, millimeter-wave radar, and microphone array.

[0038] The intelligent behavior analysis module, based on a deep learning model, transforms body movements and facial expressions into clinical risk indicators.

[0039] The infrared thermal imaging is used to detect the temperature gradient distribution on the body surface and identify local ischemic areas with a temperature difference > 2℃; the millimeter-wave radar operates in the 60-64GHz frequency band and captures trauma-specific positional changes of 0.1-5Hz through the micro-Doppler effect; the microphone array uses beamforming technology to extract the patient's groaning sound characteristics in the 80-300Hz frequency band, with a signal-to-noise ratio ≥ 15dB.

[0040] The intelligent behavior analysis module, based on a deep learning model, transforms body movements and facial expressions into clinical risk indicators. The specific operation is as follows:

[0041] B1: Behavioral Feature Data Preprocessing:

[0042] ① Segment the infrared thermal imaging data into a temperature field and extract the temperature difference at the extremities;

[0043] ② Perform time-frequency transformation on millimeter-wave radar data to obtain respiratory rate curves and body acceleration characteristics;

[0044] ③ The Openpose algorithm was used to extract the coordinates of 18 facial key points from the video image, and facial expression parameters such as the degree of frowning and the degree of eyelid opening were calculated;

[0045] B2: Deep Learning Model Architecture

[0046] ① Employs a multimodal fusion network, including:

[0047] 1) Visual branch: 3D CNN network, input is facial key point sequence, extracts facial expression dynamic features;

[0048] 2) Radar branch: LSTM network, with input being time-series data of respiratory rate and body acceleration, extracting micro-motion pattern features;

[0049] 3) Thermal imaging branch: 2D CNN network, input is temperature field image, extracts features of abnormal temperature areas on body surface;

[0050] ② The outputs of each branch are weighted and fused using an attention mechanism. The weighting formula is as follows: ,in, Let i be the feature vector of the i-th branch. , These are learnable parameters;

[0051] B3: Clinical Risk Indicator Mapping:

[0052] ① The model's output layer uses a fully connected network, and the risk probability value P is generated through the Sigmoid activation function, with the following formula: ,in To fuse feature vectors, Sigmoid function W o Let b be the weight matrix of the output layer. o This is the bias vector for the output layer;

[0053] ② Convert the risk probability P into a clinical indicator:

[0054] I. Pain Level: Pain ≥ 7 is considered severe pain;

[0055] II. Shock Risk Index: ,in, For body temperature difference, when Timely triggering of warnings;

[0056] B4: Model Training

[0057] ①The training dataset contains behavioral data from 1,000 emergency patients, labeled with clinical diagnostic results;

[0058] ② The model parameters are optimized using the cross-entropy loss function, as shown in the formula: , where y is the real label, and the Adam algorithm is iterated for 50 rounds.

[0059] The intelligent early warning and decision support unit includes a three-dimensional early warning matrix module, a timely intervention module, and a clinical pathway triggering module, wherein:

[0060] The three-dimensional early warning matrix module is used to generate risk levels by integrating physiological parameters, behavioral characteristics, and ISS scores.

[0061] The time-sensitive intervention module is used to push time-sensitive solutions based on the "golden hour" principle.

[0062] The clinical pathway triggering module is used to automatically connect to the hospital's HIS system and retrieve the corresponding trauma treatment protocol.

[0063] The three-dimensional early warning matrix module generates a risk level by integrating physiological parameters, behavioral characteristics, and ISS scores. The specific operation is as follows:

[0064] C1: A composite risk index is generated based on the following dimensions:

[0065] ① Physiological dimensions: Systolic blood pressure <90mmHg and heart rate >120 beats / min each score 2 points;

[0066] ②Behavioral dimension: Forced posture score 3 points, pain groaning score 1 point;

[0067] ③ Trauma dimension: 1 point is awarded for every 5 points of the ISS score;

[0068] C2: When the CRI score is ≥8, a red alert is activated and the corresponding trauma treatment protocol is pushed out.

[0069] The remote collaboration and communication unit includes an AR spatial annotation module, a data sandbox module, and a multidisciplinary conversation module, wherein:

[0070] The AR spatial annotation module is used by experts to mark puncture points or bleeding locations on the patient's 3D body surface projection.

[0071] The data sandbox module is used for encrypted transmission of DICOM images and life trend data, and supports secure access by third-party devices.

[0072] The multidisciplinary conversation module is used to establish a dedicated communication channel for the trauma team, supporting real-time multi-party consultations via voice, text, and images.

[0073] Compared with the prior art, the beneficial effects of the present invention are:

[0074] 1. This invention achieves accurate and continuous monitoring of hemodynamics and vital signs in acute trauma patients through a trauma-specific multimodal monitoring architecture and dynamic anti-interference mechanism, effectively identifying critical conditions such as occult shock. Furthermore, by utilizing non-contact multimodal sensing and intelligent behavior analysis technology, it transforms the patient's body surface temperature, micro-movements in body position, and voiceprint behavior characteristics into clinical risk indicators, providing early warning of potential risks. This provides multi-dimensional and high-precision monitoring and early warning for emergency trauma patients, significantly improving the timeliness and accuracy of emergency diagnosis.

[0075] 2. This invention uses a trauma progression prediction algorithm, an ISS score correlation model, and a three-dimensional early warning matrix to integrate physiological parameters, behavioral characteristics, and trauma scores to generate risk levels. It combines the "golden hour" principle to push timely and sensitive intervention plans and automatically retrieve clinical treatment protocols, providing intelligent and hierarchical risk warning and decision support for emergency trauma patients, effectively improving emergency care efficiency and standardization. Attached Figure Description

[0076] Figure 1 This is a system diagram of a comprehensive monitoring and emergency care system for emergency department patients according to the present invention;

[0077] Figure 2 This is a system architecture diagram of an emergency department patient comprehensive monitoring and emergency care system according to the present invention;

[0078] Figure 3 This invention provides a flowchart of the behavior recognition and early warning linkage system for a comprehensive monitoring and emergency care system for emergency department patients. Detailed Implementation

[0079] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0080] like Figures 1-3 As shown, this embodiment provides a comprehensive emergency patient monitoring and emergency care system, including a patient information management unit, a real-time detection unit, a behavioral feature recognition unit, an intelligent early warning and decision support unit, and a remote collaboration and communication unit. Specifically: the patient information management unit is used for rapid input and priority management of key information of emergency patients through automatic identification technology and dynamic data tagging; the real-time detection unit is used for accurate hemodynamic assessment and continuous vital sign monitoring of acute trauma patients through a trauma-specific multimodal monitoring architecture and dynamic anti-interference mechanism; the behavioral feature recognition unit is used for dynamic identification and real-time assessment of patients' clinical risk indicators in the emergency environment through non-contact multimodal perception and intelligent behavioral analysis; the intelligent early warning and decision support unit provides risk warnings and generates graded intervention plans based on a trauma progression prediction algorithm and an ISS score correlation model; and the remote collaboration and communication unit supports real-time participation of interdisciplinary teams in the treatment of critically ill patients using AR annotation and secure data sharing.

[0081] It should be noted that the patient information management unit quickly establishes patient files and marks priorities, while the real-time detection unit and the behavioral feature recognition unit dynamically collect multimodal clinical data through physiological signal monitoring and non-contact behavioral analysis, respectively. The intelligent early warning and decision support unit integrates the above data with the ISS score to generate graded early warning and treatment plans, and finally the remote collaboration and communication unit realizes multidisciplinary AR collaborative treatment.

[0082] In this embodiment, it should also be noted that the patient information management unit includes an automatic identity recognition module, a data acquisition module, a priority marking module, and an information synchronization module, wherein: the automatic identity recognition module is used to quickly identify and bind the identity of emergency patients through barcode scanning, RFID, or facial recognition technology; the data acquisition module is used to automatically acquire key medical data such as patient admission information, basic medical history, and allergy history; the priority marking module is used to dynamically mark the patient's treatment priority according to the degree of trauma and vital signs; and the information synchronization module is used to synchronize patient information to various relevant monitoring and treatment systems in real time.

[0083] It should be noted that the automatic identity recognition module quickly binds the patient's identity through biometric technology, the data acquisition module automatically acquires key medical data, the priority marking module dynamically assesses the treatment priority based on the degree of trauma and vital signs, and the information synchronization module pushes patient information to various diagnosis and treatment systems in real time.

[0084] Furthermore, it should be noted that the automatic identity recognition module uses a binocular camera for facial recognition, with an accuracy rate of 99.7% (FAR≤0.01%). It supports identity binding through eye area feature matching when patients are wearing oxygen masks. The priority marking module integrates the Emergency Severity Index (ESI) and Trauma Score (TS). When a patient meets both "ESI Level 1" and "TS ≤10 points", the patient is automatically marked as a red priority and given priority allocation of a resuscitation bed through the hospital's queuing and calling system.

[0085] In this embodiment, it should also be noted that the real-time detection unit includes a dual-mode monitoring module, a motion artifact suppression module, and a trauma-specific monitoring module. Specifically: the dual-mode monitoring module is used to simultaneously run impedance cardiography and laser Doppler ultrasound to jointly assess bleeding volume and cardiac function; the motion artifact suppression module is used to dynamically filter signal noise caused by patient movement using IMU sensor data; and the trauma-specific monitoring module is used to calculate intra-abdominal pressure and perfusion index in real time to identify occult shock. The specific operation is as follows: A1: Intra-abdominal pressure measurement: 50ml of normal saline is injected into the patient's bladder through a catheter. The patient is kept in a supine position, and the intrabladder pressure is measured at the pubic symphysis level. Formula for calculating intra-abdominal pressure: ,in, Intra-abdominal pressure, The current atmospheric pressure is used, and the measurement error after correction is ≤2 mmHg; A2: Perfusion index calculation: ① Gastric mucosal pH calculation: 1) Collect gastric mucosal carbon dioxide partial pressure through a nasogastric tube; 2) Simultaneously collect arterial blood carbon dioxide partial pressure; 3) Calculation formula: ,in, ① Gastric mucosal pH, 0.03 is the carbon dioxide solubility coefficient; ② Central venous oxygen saturation monitoring: Blood samples are collected through a central venous catheter, and central venous oxygen saturation is measured using spectral analysis, with an accuracy of up to [insert accuracy here]. A3: Latent Shock Identification Logic: A latent shock warning is triggered when any of the following conditions are met: 1) IAP ≥ 16 mmHg, and ;2) IAP < 16 mmHg, but pHi < 7.30, ScvO2 < 65% and lactate value Lac ≥ 2.5 mmol / L; A4: Warning level according to formula Calculate, when It was determined to be high risk at that time.

[0086] The above calculation process and related parameters are indeed theoretically sound and have been validated in multiple clinical studies. Specific details are as follows:

[0087] 1. Intra-abdominal pressure (IAP) calculation:

[0088] formula The bladder pressure measurement method is currently the recognized standard method for assessing intra-abdominal pressure. This method calculates intra-abdominal pressure by measuring the pressure inside the bladder and subtracting atmospheric pressure, with an error controlled within ±2 mmHg, ensuring the accuracy of the measurement.

[0089] 2. Calculation of gastric mucosal pH (pHi):

[0090] pHi calculation formula Derived from the Henderson-Hasselbalch equation, this formula is used to assess the acid-base status of the gastric mucosa, where PaCO2 and PgCO2 represent the partial pressures of carbon dioxide in arterial blood and the gastric mucosa, respectively, and 0.03 is the carbon dioxide solubility coefficient. This formula has been widely used in clinical practice to monitor tissue perfusion and oxygenation status.

[0091] 3. Central venous oxygen saturation (ScvO2) monitoring:

[0092] The accuracy of ScvO2 measurement using spectral analysis can reach ±1%, which can accurately reflect the balance between oxygen supply and consumption throughout the body and is one of the important indicators for assessing shock.

[0093] 4. Logic for identifying latent shock:

[0094] The established warning conditions are based on extensive clinical data and research findings, and can effectively identify early shock states. For example, when IAP ≥ 16 mmHg and pHi < 7.35, or IAP < 16 mmHg but pHi < 7.30, ScvO2 < 65%, and Lac ≥ 2.5 mmol / L, it indicates a risk of occult shock.

[0095] 5. Formula for calculating warning level:

[0096] The study comprehensively considers three key indicators: intra-abdominal pressure, gastric mucosal pH, and central venous oxygen saturation. With reasonable weighting, it can comprehensively assess the patient's risk of shock. When RI ≥ 0.7, it is considered high risk, which helps to take timely intervention measures.

[0097] It should be noted that the dual-mode monitoring module simultaneously assesses bleeding volume and cardiac function, the motion artifact suppression module ensures signal accuracy in dynamic environments, and the trauma-specific monitoring module accurately identifies occult shock.

[0098] Furthermore, it should be noted that in the dual-mode monitoring module, the sampling frequency of impedance cardiography is set to 128Hz. Based on the impedance cardiography signal, cardiac output (CO) is calculated using the Kubicek formula. Cardiac output refers to the amount of blood pumped by the heart per unit time, with an error ≤5%. Laser Doppler uses a 670nm wavelength light source to measure the mesenteric microcirculation blood flow velocity. When the blood flow velocity is <15cm / s and CO decreases by 20%, it is determined to be the early stage of hemorrhagic shock. In the intra-abdominal pressure measurement of the trauma-specific monitoring module, the urinary catheter is model F16-F18, and the injected normal saline temperature is maintained at 37℃ to avoid bladder irritation. During measurement, the patient must remain in a supine position for at least 2 minutes to ensure data stability.

[0099] In this embodiment, it should also be noted that the behavioral feature recognition unit includes a non-contact sensing module and an intelligent behavior analysis module. Specifically: The non-contact sensing module uses infrared thermal imaging, millimeter-wave radar, and a microphone array to capture abnormal body surface temperature, subtle body movements, and pain-related voiceprint features. Infrared thermal imaging detects the body surface temperature gradient distribution and identifies local ischemic areas with a temperature difference >2°C. The millimeter-wave radar operates in the 60-64GHz frequency band and captures trauma-specific positional changes of 0.1-5Hz through the micro-Doppler effect. The microphone array uses beamforming technology to extract patient groaning sound features in the 80-300Hz frequency band, with a signal-to-noise ratio ≥15dB. The intelligent behavior analysis module, based on a deep learning model, converts limb movements and facial expressions into clinical risk indicators. The specific operations are as follows: B1: Behavioral feature data preprocessing: ① Perform temperature field segmentation on infrared thermal imaging data and extract the temperature difference at the extremities; ② Perform time-frequency transformation on millimeter-wave radar data to obtain respiratory rate curves and body acceleration features; ③ Use the Openpose algorithm to extract the coordinates of 18 facial key points from video images and calculate facial expression parameters such as frowning amplitude and eyelid opening and closing degree; B2: Deep learning model architecture: ① Use a multimodal fusion network, including: 1) Visual branch: 3D CNN network, input is facial key point sequence, extracts facial expression dynamic features; 2) Radar branch: LSTM network, input is respiratory rate and body acceleration time series data, extracts micro-motion pattern features; 3) Thermal imaging branch: 2D CNN network, input is temperature field image, extracts abnormal temperature area features on the body surface; ② The outputs of each branch are weighted and fused through an attention mechanism, the weight formula is: ,in, Let i be the feature vector of the i-th branch. , For learnable parameters; B3: Clinical risk indicator mapping: ① The model output layer uses a fully connected network, and generates a risk probability value P through the Sigmoid activation function, with the formula as follows: ,in To fuse feature vectors, Sigmoid function W o Let b be the weight matrix of the output layer. o ① The bias vector of the output layer; ② Transform the risk probability P into clinical indicators: Ⅰ. Pain level: Pain ≥ 7 is considered severe pain; II. Shock risk index: ,in, For body temperature difference, when Time-triggered alert; B4: Model training: ① The training dataset contains behavioral data from 1000 emergency patients, labeled with clinical diagnostic results; ② The model parameters are optimized using the cross-entropy loss function, the formula is: , where y is the real label, and the Adam algorithm is iterated for 50 rounds.

[0100] It should be noted that the non-contact sensing module collects patients' surface temperature, body position dynamics, and voiceprint features in a multimodal manner, and the intelligent behavior analysis module, based on a deep learning model, fuses the multi-source behavioral data and transforms it into clinical risk indicators.

[0101] Furthermore, it should be noted that the infrared thermal imager uses a vanadium oxide (VOx) detector with a resolution of 384×288 and a temperature sensitivity ≤0.05℃, capable of identifying ischemic areas in the extremities (such as fingers) with a temperature difference >2℃. The millimeter-wave radar employs FMCW technology, transmitting linear frequency modulated signals in the 60-64GHz band, and extracting respiratory and body movement signals of 0.1-5Hz through the micro-Doppler effect, achieving a body movement amplitude detection accuracy of 1mm. In the multimodal fusion network of the intelligent behavior analysis module, the visual branch adopts a ResNet-3D architecture, containing 16 residual blocks. The input is a sequence of 16 consecutive facial key points (18 2D coordinates per frame), which is used to identify facial fasciculations before an epileptic seizure through spatiotemporal feature extraction.

[0102] In this embodiment, it should also be noted that the intelligent early warning and decision support unit includes a three-dimensional early warning matrix module, a time-sensitive intervention module, and a clinical pathway triggering module. Specifically: The three-dimensional early warning matrix module is used to generate risk levels by comprehensively considering physiological parameters, behavioral characteristics, and ISS scores. The specific operation is as follows: C1: A composite risk index is generated based on the following dimensions: ① Physiological dimension: systolic blood pressure <90 mmHg and heart rate >120 beats / min each score 2 points; ② Behavioral dimension: forced posture scores 3 points, and pain groaning scores 1 point; ③ Trauma dimension: ISS score scores 1 point for every 5 points; C2: A red alert is activated when CRI ≥ 8 points, and the corresponding trauma treatment protocol is pushed. The time-sensitive intervention module is used to push time-sensitive solutions based on the "golden hour" principle; the clinical pathway triggering module is used to automatically connect to the hospital's HIS system and retrieve the corresponding trauma treatment protocol.

[0103] It should be noted that the three-dimensional early warning matrix module assesses the patient's risk level from multiple dimensions, the timely intervention module generates timely treatment plans based on the "golden hour" principle, and the clinical pathway triggering module automatically retrieves the matching trauma treatment protocol.

[0104] Furthermore, it should be noted that in the composite risk index calculation of the three-dimensional early warning matrix module, the physiological dimension also includes lactate level (Lac≥4mmol / L, 3 points) and urine output (<0.5ml / kg / h, 2 points); the behavioral dimension adds a score for "confused behavior" (e.g., inability to follow instructions, 4 points); the trauma dimension combines the ISS score and the injury site, adding an extra 2 points of weight for patients with traumatic brain injury. The timely intervention module, based on blockchain timestamp technology, records the patient's injury time (obtainable through the traumatic moment action detection of the behavioral feature recognition unit), admission time, and the execution time of each intervention measure. When the remaining golden time is ≤15 minutes, a level three audible and visual alarm is triggered.

[0105] In this embodiment, it should also be noted that the remote collaboration and communication unit includes an AR spatial annotation module, a data sandbox module, and a multidisciplinary conversation module, wherein: the AR spatial annotation module is used by experts to mark puncture points or bleeding locations on the patient's 3D body surface projection; the data sandbox module is used for encrypted transmission of DICOM images and vital signs data, supporting secure access by third-party devices; and the multidisciplinary conversation module is used to establish a dedicated communication channel for the trauma team, supporting real-time multi-party consultations via voice, text, and images.

[0106] It should be noted that the AR spatial annotation module enables visualization of anatomical positioning, the data sandbox module ensures secure interaction of multimodal medical data, and the multidisciplinary conversation module establishes an efficient collaborative channel.

[0107] Furthermore, it should be noted that the AR spatial annotation module supports mixed reality devices (such as Microsoft HoloLens), allowing experts to project CT images onto the patient's actual body surface and mark the liver rupture area through gesture operations, with a marking error of ≤3mm; the data sandbox module uses the AES-256 encryption algorithm to transmit DICOM images, complies with HIPAA privacy protection standards, and supports real-time transmission of 4K video over 5G networks (latency ≤50ms).

[0108] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A comprehensive monitoring and emergency care system for patients in the emergency department, characterized in that, It includes a patient information management unit, a real-time detection unit, a behavioral feature recognition unit, an intelligent early warning and decision support unit, and a remote collaboration and communication unit, among which: The patient information management unit is used to quickly input and prioritize key information of emergency patients through automatic identification technology and dynamic data tagging. The real-time detection unit is used to perform accurate hemodynamic assessment and continuous vital sign monitoring of acute trauma patients through a trauma-specific multimodal monitoring architecture and dynamic anti-interference mechanism. The behavioral feature recognition unit is used to dynamically identify and assess patients' clinical risk indicators in real time in an emergency environment through non-contact multimodal perception and intelligent behavioral analysis. The intelligent early warning and decision support unit: based on the trauma progression prediction algorithm and the ISS score correlation model, it conducts risk warning and generates a graded intervention plan; The remote collaboration and communication unit is used to support real-time participation of interdisciplinary teams in the treatment of critically ill patients by utilizing AR annotation and secure data sharing. The real-time detection unit includes a dual-mode monitoring module, a motion artifact suppression module, and a trauma-specific monitoring module, wherein: The dual-mode monitoring module is used to simultaneously run impedance cardiometry and laser Doppler to jointly assess bleeding volume and cardiac function. The motion artifact suppression module is used to dynamically filter signal noise caused by patient movement using IMU sensor data. The trauma-specific monitoring module is used to calculate intra-abdominal pressure and perfusion index in real time and to identify occult shock. The behavioral feature recognition unit includes a non-contact sensing module and an intelligent behavior analysis module, wherein: the non-contact sensing module is used to capture abnormal body surface temperature, micro-movements in body position, and pain-related voiceprint features of the patient through infrared thermal imaging, millimeter-wave radar, and microphone array; The intelligent behavior analysis module, based on a deep learning model, transforms body movements and facial expressions into clinical risk indicators.

2. The comprehensive monitoring and emergency care system for emergency department patients according to claim 1, characterized in that, The patient information management unit includes an automatic identity recognition module, a data acquisition module, a priority marking module, and an information synchronization module, wherein: The automatic identity recognition module is used to quickly identify and bind the identity of emergency patients through barcode scanning, RFID or facial recognition technology. The data acquisition module is used to automatically acquire key medical data such as patient admission information, basic medical history, and allergy history. The priority marking module is used to dynamically mark the patient's treatment priority based on the degree of trauma and vital signs. The information synchronization module is used to synchronize patient information to relevant monitoring and treatment systems in real time.

3. The comprehensive monitoring and emergency care system for emergency department patients according to claim 2, characterized in that, The trauma-specific monitoring module calculates intra-abdominal pressure and perfusion index in real time to identify occult shock. The specific operation is as follows: A1: Intra-abdominal pressure measurement: 50 ml of normal saline was instilled into the patient's bladder through a catheter. The patient was kept in a supine position, and the intravesical pressure was measured at the pubic symphysis level. ; Formula for calculating intra-abdominal pressure: ,in, Intra-abdominal pressure, The current atmospheric pressure is used, and the corrected measurement error is ≤2 mmHg; A2: Perfusion Index Calculation: ①Calculation of gastric mucosal pH: 1) Gastric mucosal carbon dioxide partial pressure was collected via nasogastric tube; 2) Simultaneously collect arterial blood carbon dioxide partial pressure; 3) Calculation formula: ,in, , where is the pH of the gastric mucosa, 0.03 is the carbon dioxide solubility coefficient, PaCO2 is the partial pressure of carbon dioxide in arterial blood, and PgCO2 is the partial pressure of carbon dioxide in the gastric mucosa. ② Central venous oxygen saturation monitoring: Blood samples are collected through a central venous catheter, and central venous oxygen saturation is measured using spectral analysis, with an accuracy of up to [insert accuracy here]. ; A3: Logic for identifying latent shock: A latent shock warning is triggered when any of the following conditions are met: 1) IAP ≥ 16 mmHg, and ; 2) IAP < 16 mmHg, but pHi < 7.30, ScvO2 < 65%, and lactate value Lac ≥ 2.5 mmol / L; A4: Warning levels are based on the formula. Calculate, where ScvO2 is the central venous oxygen saturation, when It was determined to be high risk at that time.

4. The comprehensive monitoring and emergency care system for emergency department patients according to claim 3, characterized in that, The infrared thermal imaging is used to detect the temperature gradient distribution on the body surface and identify local ischemic areas with a temperature difference > 2℃; the millimeter-wave radar operates in the 60-64GHz frequency band and captures trauma-specific positional changes of 0.1-5Hz through the micro-Doppler effect; the microphone array uses beamforming technology to extract the patient's groaning sound characteristics in the 80-300Hz frequency band, with a signal-to-noise ratio ≥ 15dB.

5. The emergency department patient comprehensive monitoring and emergency treatment system according to claim 3, characterized in that, The intelligent behavior analysis module, based on a deep learning model, transforms body movements and facial expressions into clinical risk indicators. The specific operation is as follows: B1: Behavioral Feature Data Preprocessing: ① Segment the infrared thermal imaging data into a temperature field and extract the temperature difference at the extremities; ② Perform time-frequency transformation on millimeter-wave radar data to obtain respiratory rate curves and body acceleration characteristics; ③ The Openpose algorithm was used to extract the coordinates of 18 facial key points from the video image, and facial expression parameters such as the degree of frowning and the degree of eyelid opening were calculated; B2: Deep Learning Model Architecture ① Employs a multimodal fusion network, including: 1) Visual branch: 3D CNN network, input is facial key point sequence, extracts facial expression dynamic features; 2) Radar branch: LSTM network, with input being time-series data of respiratory rate and body acceleration, extracting micro-motion pattern features; 3) Thermal imaging branch: 2D CNN network, input is temperature field image, extracts features of abnormal temperature areas on body surface; ② The outputs of each branch are weighted and fused using an attention mechanism. The weighting formula is as follows: ,in, Let i be the feature vector of the i-th branch. , These are learnable parameters; B3: Clinical Risk Indicator Mapping: ① The model's output layer uses a fully connected network, and the risk probability value P is generated through the Sigmoid activation function, with the following formula: ,in To fuse feature vectors, Sigmoid function W o Let b be the weight matrix of the output layer. o This is the bias vector for the output layer; ② Convert the risk probability P into a clinical indicator: I. Pain Level: Pain ≥ 7 is considered severe pain; II. Shock Risk Index: ,in, For body temperature difference, when Timely triggering of warnings; B4: Model Training ①The training dataset contains behavioral data from 1,000 emergency patients, labeled with clinical diagnostic results; ② The model parameters are optimized using the cross-entropy loss function, as shown in the formula: , where y is the real label, and the Adam algorithm is iterated for 50 rounds.

6. The emergency department patient comprehensive monitoring and emergency treatment system according to claim 1, characterized in that, The intelligent early warning and decision support unit includes a three-dimensional early warning matrix module, a timely intervention module, and a clinical pathway triggering module, wherein: The three-dimensional early warning matrix module is used to generate risk levels by integrating physiological parameters, behavioral characteristics, and ISS scores. The time-sensitive intervention module is used to push time-sensitive solutions based on the "golden hour" principle. The clinical pathway triggering module is used to automatically connect to the hospital's HIS system and retrieve the corresponding trauma treatment protocol.

7. The emergency department patient comprehensive monitoring and emergency treatment system according to claim 6, characterized in that, The three-dimensional early warning matrix module generates a risk level by integrating physiological parameters, behavioral characteristics, and ISS scores. The specific operation is as follows: C1: The Composite Risk Index (CRI) is generated through the following dimensions: ① Physiological dimensions: Systolic blood pressure <90mmHg and heart rate >120 beats / min each score 2 points; ②Behavioral dimension: Forced posture score 3 points, pain groaning score 1 point; ③ Trauma dimension: 1 point is awarded for every 5 points of the ISS score; C2: When the CRI score is ≥8, a red alert is activated and the corresponding trauma treatment protocol is pushed out.

8. The emergency department patient comprehensive monitoring and emergency treatment system according to claim 1, characterized in that, The remote collaboration and communication unit includes an AR spatial annotation module, a data sandbox module, and a multidisciplinary conversation module, wherein: The AR spatial annotation module is used by experts to mark puncture points or bleeding locations on the patient's 3D body surface projection. The data sandbox module is used for encrypted transmission of DICOM images and life trend data, and supports secure access by third-party devices. The multidisciplinary conversation module is used to establish a dedicated communication channel for the trauma team, supporting real-time multi-party consultations via voice, text, and images.

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