Portable intelligent puncture detection device, system and method

Abstract

The invention relates to the technical field of medical equipment, in particular to a portable intelligent puncture detection device, system and method, and the device comprises a puncture module which is used for carrying out blood vessel puncture and obtaining a sample through infrared or ultrasonic guidance; the monitoring module is connected with the puncture module and is used for analyzing the sample and acquiring blood biological indexes of the blood; the monitoring module is also used for monitoring hemodynamic parameters through PICCO; the analysis module is used for carrying out analysis by adopting the constructed analysis model according to the blood biological indexes and the hemodynamic parameters to obtain analysis results, and the analysis results comprise illness condition severity program classification, treatment suggestion and prognosis prediction of the patient; the verification module is used for acquiring and displaying an analysis result; and the server is also used for acquiring an analysis result confirmation signal, confirming the analysis result and displaying a confirmation result. According to the scheme, multi-parameter and multi-index monitoring can be carried out, data analysis is carried out, the data utilization rate is increased, and medical workers are assisted to carry out treatment work.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical equipment, in particular to a portable intelligent puncture detection device, system and method. BACKGROUND

[0002] The rapid assessment of organ function and hemodynamic state before hospitalization is crucial for treatment. Currently, pre-hospital transport can only monitor blood pressure, heart rate, and pulse oxygen saturation index using a monitor, which has a small amount of data and is difficult to quickly and accurately assess the patient's condition. The hospital can monitor non-invasive blood pressure and PICCO. Although a large amount of index data is obtained, the data is not comprehensively utilized, the data utilization rate is low, and medical personnel need to analyze the patient's condition based on the data.

[0003] Therefore, there is an urgent need for a portable intelligent puncture detection device, system and method that can monitor multiple parameters and indicators, analyze data, and improve data utilization to assist medical personnel in treatment. SUMMARY

[0004] One of the purposes of the present application is to provide a portable intelligent puncture detection device that can monitor multiple parameters and indicators, analyze data, and improve data utilization to assist medical personnel in treatment.

[0005] The present application provides a basic scheme one: a portable intelligent puncture detection device, comprising: a puncture module, a monitoring module, an analysis module and a verification module; The puncture module is used to perform blood vessel puncture through infrared or ultrasonic guidance to obtain samples; The monitoring module is connected to the puncture module and is used to analyze the samples to obtain blood biological indicators of the blood; and is also used to monitor hemodynamic parameters through PICCO; The analysis module is used to analyze the blood biological indicators and the hemodynamic parameters using a constructed analysis model to obtain analysis results, including: patient condition severity classification, treatment recommendations and prognosis prediction; The verification module is used to obtain and display the analysis results; It is also used to obtain an analysis result confirmation signal to confirm the analysis results and display the confirmation results.

[0006] Further, the puncture module comprises: a positioning sub-module, a puncture sub-module and a PICCO module; The positioning sub-module is used to position the blood vessel using infrared or ultrasonic imaging to obtain blood vessel positioning data; The puncture sub-module is used to adjust the puncture needle according to the positioning data to perform blood vessel puncture and obtain samples.

[0007] Further, the positioning submodule acquires blood vessel selection information, and determines whether the puncture blood vessel type is a superficial blood vessel or a deep blood vessel according to the blood vessel selection information; If it is a superficial blood vessel, infrared rays are used to position the blood vessel, the infrared camera is used to capture the blood vessel thermal signal or reflection signal, and the positioning data of the blood vessel is determined; If it is a deep blood vessel, ultrasonic imaging is used to position the blood vessel, the ultrasonic probe is used to detect the blood vessel, the ultrasonic image is generated, the blood vessel is identified according to the ultrasonic image by using an image processing algorithm, and the positioning data of the blood vessel is acquired.

[0008] Further, the puncture submodule is provided with a mechanical arm or a needle guide device, the position of the mechanical arm or the needle guide device is adjusted according to the position in the positioning data to adjust the position and angle of the puncture needle, the blood vessel is punctured, and the depth of the mechanical arm or the needle guide device is adjusted according to the depth in the positioning data to adjust the depth of the puncture needle.

[0009] Further, the monitoring module comprises a blood detection submodule and a hemodynamics submodule. The blood detection submodule is configured to detect blood biological indicators in the sample by using a miniaturized blood analyzer, wherein the blood biological indicators include pH, PaO2, PaCO2, SaO2, Lac, Hb, K⁺, Na⁺, and Cl⁻. The hemodynamics submodule is configured to monitor hemodynamic parameters by using PICCO, wherein the hemodynamic parameters include blood pressure BP, heart rate HR, cardiac output CO, cardiac index CI, stroke volume SV, pulse pressure variation PPV, and extravascular lung water index EVLWI.

[0010] Further, the analysis model adopts a multi-modal time sequence fusion network, which comprises an input layer, a time sequence feature extraction layer, a static feature fusion layer, and a multi-task output layer. The input layer is configured to input time sequence data and static features, wherein the time sequence data is time sequence data obtained by splicing blood biological indicators and hemodynamic parameters, and the static features include age, gender, injury mechanism, underlying disease, and injury site. The time sequence feature extraction layer is configured to extract features from the time sequence data to obtain time sequence features. The static feature fusion layer is configured to fuse the static features and the time sequence features to generate comprehensive features. The multi-task output layer is configured to generate a disease severity program classification, a treatment suggestion, and a prognosis prediction according to the comprehensive features.

[0011] Further, the time sequence feature extraction layer comprises a multi-head self-attention time sequence encoder and a bidirectional LSTM time sequence modeling layer. The multi-head self-attention time sequence encoder inputs the spliced time sequence data. Position encoding is performed, sinusoidal position encoding is added, time sequence information is retained, enhanced time sequence features are obtained through a multi-head self-attention mechanism; A bidirectional LSTM time sequence modeling layer is used to model the output of the multi-head self-attention time sequence encoder, obtain the hidden features of the last time step as the time sequence features, and the bidirectional LSTM time sequence modeling layer includes a forward LSTM, a backward LSTM and a gating mechanism; The forward LSTM captures the development trend of the physiological parameters from the past to the future; The backward LSTM identifies the causal relationship of the parameter changes from the future to the past; The input gate in the gating mechanism controls the inflow of new information, the forgetting gate determines how much historical information to retain, and the output gate controls the output of the current state.

[0012] Further, the multi-task output layer includes three output heads: a disease severity program classification output head, a treatment recommendation output head and a prognosis prediction output head; The disease severity program classification output head learns the key threshold mode through a fully connected layer and a Softmax function, classifies the comprehensive features, and obtains the probability distribution of the disease severity program; The treatment recommendation output head learns the treatment logic in the clinical guidelines through a multi-label classifier, classifies the comprehensive features, and obtains the probability distribution of the treatment recommendation; The prognosis prediction output head performs survival probability prediction based on the organ failure score logic according to the comprehensive features through a regression layer and a Sigmoid activation function, and obtains the survival probability.

[0013] The second purpose of the present application is to provide a portable intelligent puncture detection system that can monitor multiple parameters and multiple indicators, analyze data, improve data utilization, and assist medical personnel in rescue work.

[0014] The present application provides a basic scheme two: a portable intelligent puncture detection method, comprising: Blood vessels are punctured through infrared or ultrasonic guidance to obtain samples; The samples are analyzed to obtain blood biological indicators of blood; and the PICCO is used to monitor hemodynamic parameters; According to the blood biological indicators and the hemodynamic parameters, an analysis model is constructed to analyze and obtain analysis results, including disease severity program classification of patients, treatment recommendations and prognosis prediction; The analysis results are obtained and displayed; An analysis result confirmation signal is obtained to confirm the analysis results and display the confirmation results.

[0015] The third object of the present application is to provide a portable intelligent puncture detection system that can perform multi-parameter and multi-index monitoring, data analysis, and improve data utilization to assist medical personnel in rescue work.

[0016] The present application provides a basic solution three: a portable intelligent puncture detection system for performing the above-mentioned portable intelligent puncture detection method.

[0017] Beneficial effects: this solution is guided by infrared or ultrasound, and can adapt to different blood vessel sampling requirements, perform blood vessel puncture, obtain samples, and then analyze them through the monitoring module to obtain blood biological indicators and hemodynamic parameters; according to the blood biological indicators and hemodynamic parameters, an analysis model is constructed to analyze and obtain analysis results, including patient condition severity classification, treatment recommendations, and prognosis prediction, to assist medical personnel in rescue work, and the verification module obtains the analysis results and displays them; it is also used to obtain an analysis result confirmation signal, confirm the analysis results, display the confirmation results, and ensure that the displayed results are reviewed by medical personnel; this solution further analyzes the collected data to effectively improve data utilization.

[0018] In summary, this solution can perform multi-parameter and multi-index monitoring, data analysis, and improve data utilization to assist medical personnel in rescue work BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a schematic diagram of an embodiment of the present application, a portable intelligent puncture detection device. DETAILED DESCRIPTION

[0020] The following will be further described in detail through specific embodiments: The markers in the drawings of the specification include: Embodiment one This embodiment provides, as shown in the accompanying Figure 1 : a portable intelligent puncture detection device, comprising: a puncture module, a monitoring module, an analysis module, and a verification module; The puncture module is used to perform blood vessel puncture and obtain samples through infrared or ultrasound guidance; Specifically, the puncture module includes a positioning sub-module, a puncture sub-module, and a constant temperature sub-module. The positioning sub-module is used to position the blood vessel using infrared or ultrasound imaging to obtain the positioning data of the blood vessel, including position and depth. Specifically, the positioning sub-module obtains blood vessel selection information and determines whether the puncture blood vessel type is a superficial blood vessel or a deep blood vessel according to the blood vessel selection information. If it is a superficial blood vessel, such as the radial artery, infrared rays are used to locate the blood vessel. In this embodiment, the infrared camera captures the blood vessel heat signal or reflection signal to determine the positioning data of the blood vessel. If it is a deep blood vessel, such as the femoral artery, ultrasonic imaging is used to locate the blood vessel. In this embodiment, the ultrasonic probe is used to detect the blood vessel by ultrasonic waves to generate an ultrasonic image. An image processing algorithm is used to identify the blood vessel based on the ultrasonic image to obtain the positioning data of the blood vessel. The image processing algorithm includes but is not limited to edge detection and segmentation algorithm.

[0021] The puncture sub-module is used to adjust the puncture needle according to the positioning data to perform blood vessel puncture and obtain the sample. Specifically, the puncture sub-module is provided with a mechanical arm or a needle guide device. The position of the puncture needle is adjusted according to the position in the positioning data, and the blood vessel is punctured. The depth of the puncture needle is adjusted according to the depth in the positioning data. The mechanical arm or the needle guide device is used to realize precise movement by using a stepping motor or a servo motor to avoid damage to the surrounding tissue. The position data obtained by the image processing algorithm includes the position coordinates of the puncture point. The puncture sub-module is also provided with a pressure sensor arranged above the puncture needle to obtain a pressure signal to determine whether the puncture is successful.

[0022] The constant temperature sub-module is used to heat the puncture point within a predetermined range to prevent blood vessel contraction. Specifically, a heating pad, such as a PTC thermistor, is integrated above the puncture needle. The constant temperature is maintained by a temperature sensor and a PID controller to prevent blood vessel contraction. In this embodiment, the constant temperature is 37℃.

[0023] The monitoring module is connected to the puncture module. Specifically, the puncture needle is connected to the catheter to access the monitoring module. The monitoring module is used to analyze the sample to obtain the blood biological indicators of the blood. It is also used to monitor the hemodynamic parameters by PICCO. Specifically, the monitoring module includes a blood detection sub-module and a hemodynamic sub-module. The blood detection sub-module is used to detect the blood biological indicators in the sample by a miniaturized blood analyzer. The blood biological indicators include but are not limited to pH, PaO2, PaCO2, SaO2, Lac, Hb, K⁺, Na⁺, and Cl⁻. The specific detection method includes using a glass electrode method to generate a potential difference based on the concentration of H⁺ ions to obtain pH. Clark electrodes (O2) and Severinghaus electrodes (CO2) are used to measure the concentrations of PaO2 and PaCO2 based on electrochemical reactions. The blood oxygen saturation SaO2 is measured by the spectral absorption method through the LED light source and the photoelectric detector. The lactic acid concentration Lac is obtained by the enzyme electrode method through the lactic acid oxidase catalytic reaction to generate current. The hemoglobin concentration Hb is obtained by the photoelectric colorimetric method through the measurement of the hemoglobin absorbance. According to the ion selective electrode, the electrolyte (K⁺, Na⁺, Cl⁻) concentration is obtained based on the membrane potential change.

[0024] The hemodynamic submodule is used to monitor the hemodynamic parameters through the PICCO (PICCO monitor), wherein the hemodynamic parameters include but are not limited to: blood pressure BP, heart rate HR, cardiac output CO, cardiac index CI, stroke volume SV, pulse pressure variation PPV, and extravascular lung water index EVLWI. Specifically, the arterial pressure waveform is collected in real time through the sensor such as the MEMS sensor connected to the catheter to obtain the BP and HR. The normal temperature saline is injected, the blood temperature change is detected through the temperature sensor, the cardiac output CO is calculated, and the waveform analysis parameters are calibrated. The cardiac index CI, stroke volume SV, pulse pressure variation PPV, and extravascular lung water index EVLWI are calculated through the waveform analysis algorithm, specifically, the features such as the waveform area and peak time are extracted from the arterial pressure waveform, and the Windkessel model is used to calculate the SV and CI; the PPV is calculated through the pulse pressure change in the respiratory cycle; and the EVLWI is derived based on the thermal dilution curve and the heart parameters.

[0025] In this embodiment, the puncture needle is connected to the first catheter, the three-way valve group is connected to the first catheter, and the other two valve ports of the three-way valve group are connected to the miniaturized blood analyzer and the PICCO monitor through the second catheter and the third catheter; in other embodiments, the miniaturized blood analyzer can also be connected without the catheter, one valve port is left empty as a sampling port, the sample is collected at the sampling port, and then the miniaturized blood analyzer is used for analysis.

[0026] The analysis module is used to analyze the blood biological indicators and the hemodynamic parameters by using the constructed analysis model to obtain the analysis results, including: the patient's disease severity classification, treatment suggestion, and prognosis prediction. Specifically, the analysis model uses a multi-modal time series fusion network, inputs the time series data and static data, and outputs the disease severity classification, treatment suggestion, and prognosis prediction. Specifically, it includes: an input layer, a time series feature extraction layer, a static feature fusion layer, and a multi-task output layer. The input layer is used to input the time series data and static features. The time series data is time series data obtained by splicing blood biological indicators and hemodynamic parameters. Hemodynamic parameters at T time points are acquired, and Z-score standardization is performed, missing values are processed, time series hemodynamic data is formed, shape: T x Nt. According to a preset sampling frequency, blood biological indicators are collected, and the time series hemodynamic data is aligned to form time series blood biological indicator data, shape: T x Nb. The static features include age, gender, injury mechanism, underlying disease, and injury site. The classification variables in the classification are one-hot encoded, and the continuous variables are standardized to form static features, shape: Ns. The time series feature extraction layer is used for feature extraction of the time series data, and the time series features are obtained. The time series feature extraction layer includes a multi-head self-attention time series encoder and a bidirectional LSTM time series modeling layer. The multi-head self-attention time series encoder inputs the spliced time series data [hemodynamics, blood biological indicators], shape: T x (Nt+Nb). Position encoding is performed, sine position encoding is added, time sequence information is retained, enhanced time series features are obtained through a multi-head self-attention mechanism, shape: T x Datt, and layer normalization and residual connection are used to ensure training stability. Each attention head of the multi-head self-attention mechanism focuses on different physiological relationship patterns, for example: head 1 focuses on the cooperative change of oxygenation-related parameters (such as PO2, SO2, pH); head 2 focuses on the interaction of circulation parameters (such as BP, HR, CI, SV); and head 3 focuses on the association between metabolic indicators (such as Lac, pH) and circulation state.

[0027] The bidirectional LSTM time series modeling layer is used for time series modeling of the output of the multi-head self-attention time series encoder, and the hidden features at the last time step are obtained as the time series features, shape: 2 x Dlstm. The bidirectional LSTM time series modeling layer includes a forward LSTM, a backward LSTM, and a gating mechanism. The forward LSTM captures the development trend of physiological parameters from past to future, the backward LSTM identifies the causal relationship of parameter changes from future to past, and the gating mechanism controls the inflow of new information through the input gate, determines how much historical information to retain through the forget gate, and controls the output of the current state through the output gate.

[0028] The static feature fusion layer is used for fusion of the static features and the time series features to generate comprehensive features, shape: Dfused. Specifically, the input static features and time sequence features are mapped to a high-dimensional space through a fully connected layer, and the features are spliced, [static features, time sequence features], that is, [static features, LSTM forward state, LSTM backward state]; the static features and the time sequence features are interacted through a cross-attention mechanism, in which the query (Q) comes from the static features, the key (K) and the value (V) come from the time sequence features, the time sequence features most relevant to the current static features are calculated, and the integrated features after fusion are generated.

[0029] A multi-task output layer is configured to generate a disease severity progression classification, a treatment recommendation, and a prognosis prediction according to the integrated features. Specifically, the multi-task output layer includes three output heads: a disease severity progression classification output head, a treatment recommendation output head, and a prognosis prediction output head. The disease severity progression classification output head learns a key threshold mode through a fully connected layer and a Softmax function, classifies the integrated features, and obtains a probability distribution of the disease severity progression. In this embodiment, the probability distribution of four categories includes: stable, compensatory shock, decompensatory shock, and end-stage MODS. The treatment recommendation output head learns a treatment logic in a clinical guideline through a multi-label classifier, classifies the integrated features, and obtains a probability distribution of the treatment recommendation. In this embodiment, the probability distribution of seven treatment recommendations includes: restrictive fluid management, active fluid resuscitation, vasoactive drugs, blood transfusion, ventilation support, correction of acidosis, and correction of electrolytes. The prognosis prediction output head performs survival probability prediction based on organ failure score logic according to the integrated features through a regression layer and a Sigmoid activation function, and obtains a survival probability.

[0030] The constructed analysis model is trained using historical data, and an Adam optimizer is used for learning rate scheduling to obtain an analysis model with a loss function meeting a preset threshold.

[0031] A verification module is configured to obtain and display the analysis result. The verification module is also configured to obtain an analysis result confirmation signal, confirm the analysis result, and display the confirmation result. The verification module is also configured to obtain analysis result modification information and modify the analysis result. The medical staff can confirm and modify the analysis result through the verification module, and the analysis result can assist the medical staff in their work. In this embodiment, the verification module uses a display terminal device, and the verification module can also be a mobile terminal or a computer terminal of the medical staff.

[0032] Embodiment Two The embodiment provides a portable intelligent puncture detection method, which comprises the following steps: Blood vessels are punctured through infrared or ultrasonic guidance, and samples are obtained; The samples are analyzed to obtain blood biological indexes of blood; and the portable intelligent puncture detection method is also used for monitoring hemodynamic parameters through PICCO; According to the blood biological indexes and the hemodynamic parameters, an analysis model is constructed, analysis is performed, and an analysis result is obtained, which comprises a patient's disease severity classification, a treatment suggestion and a prognosis prediction; The analysis result is obtained and displayed; An analysis result confirmation signal is obtained, the analysis result is confirmed, and a confirmation result is displayed.

[0033] The embodiment also provides a portable intelligent puncture detection system, which adopts the portable intelligent puncture detection method.

[0034] The above is only an embodiment of the present application, and common knowledge such as specific structures and characteristics in the scheme is not described in detail, the ordinary skilled in the art knows all the ordinary technical knowledge in the technical field of the present application before the filing date or the priority date, can know all the prior art in the field and has the ability to apply conventional experimental means before the date, the ordinary skilled in the art can improve and implement the present scheme under the guidance of the present application, some typical known structures or known methods should not be an obstacle for the ordinary skilled in the art to implement the present application. It should be pointed out that, for the skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the present application. The protection scope of the present application should be subject to the content of its claims, and the specific implementation mode and the like in the specification can be used to explain the content of the claims.

Claims

1. A portable intelligent puncture detection device, characterized in that: include: The module includes a puncture module, a monitoring module, an analysis module, and a verification module. The puncture module is used to perform vascular puncture and obtain samples under infrared or ultrasound guidance. The monitoring module, connected to the puncture module, is used to analyze samples and obtain blood biomarkers; it is also used to monitor hemodynamic parameters via PICCO. The analysis module is used to analyze blood biomarkers and hemodynamic parameters using a constructed analysis model to obtain analysis results, including: classification of the severity of the patient's condition, treatment recommendations, and prognosis prediction; The verification module is used to acquire and display the analysis results; It is also used to obtain confirmation signals for analysis results, confirm the analysis results, and display the confirmation results.

2. The portable intelligent puncture detection device according to claim 1, characterized in that: The puncture module includes: a positioning submodule, a puncture submodule, and a PICCO module; The positioning submodule is used to locate blood vessels using infrared or ultrasound imaging and acquire the positioning data of the blood vessels. The puncture submodule is used to adjust the position of the puncture needle based on the positioning data, perform vascular puncture, and obtain samples.

3. The portable intelligent puncture detection device according to claim 2, characterized in that: The positioning submodule acquires blood vessel selection information and determines whether the puncture blood vessel type is a superficial blood vessel or a deep blood vessel based on the blood vessel selection information. If the blood vessel is superficial, an infrared camera is used to capture the thermal or reflected signals of the blood vessel to determine its location data. If the blood vessel is deep, an ultrasound probe is used to detect the blood vessel and generate an ultrasound image. Image processing algorithms are then used to identify the blood vessel based on the ultrasound image and obtain the blood vessel location data.

4. The portable intelligent puncture detection device according to claim 3, characterized in that: The puncture submodule is equipped with a robotic arm or needle guide device. Based on the position in the positioning data, the robotic arm or needle guide device is controlled to adjust the position and angle of the puncture needle to puncture the blood vessel. Based on the depth in the positioning data, the robotic arm or needle guide device is controlled to adjust the depth of the puncture needle.

5. The portable intelligent puncture detection device according to claim 1, characterized in that: The monitoring module includes: a blood detection submodule and a hemodynamics submodule; The blood testing submodule is used to detect blood biomarkers in samples using a miniaturized blood analyzer; these blood biomarkers include: pH, PaO2, PaCO2, SaO2, Lac, Hb, K⁺, Na⁺, and Cl⁻. The hemodynamic submodule is used to monitor hemodynamic parameters via PICCO. These hemodynamic parameters include: blood pressure (BP), heart rate (HR), cardiac output (CO), cardiac index (CI), stroke volume (SV), pulse pressure variability (PPV), and extravascular lung water index (EVLWI).

6. The portable intelligent puncture detection device according to claim 1, characterized in that: The analysis model employs a multimodal temporal fusion network, including: The input layer is used to input time-series data and static features; the time-series data is a concatenation of blood biometrics and hemodynamic parameters. The temporal feature extraction layer is used to extract features from time-series data and obtain temporal features; The static feature fusion layer is used to fuse static features and temporal features to generate comprehensive features; The multi-task output layer is used to generate disease severity classification, treatment recommendations, and prognostic predictions based on comprehensive features.

7. The portable intelligent puncture detection device according to claim 6, characterized in that: The temporal feature extraction layer includes: a multi-head self-attention temporal encoder and a bidirectional LSTM temporal modeling layer; The multi-head self-attention temporal encoder performs position encoding on the concatenated temporal data through a multi-head self-attention mechanism and adds sinusoidal position encoding to preserve temporal order information and obtain enhanced temporal features. A bidirectional LSTM temporal modeling layer is used to perform temporal modeling on the output of a multi-head self-attention temporal encoder, and to obtain the hidden features of the last time step as temporal features.

8. The portable intelligent puncture detection device according to claim 7, characterized in that: The multi-task output layer includes three output heads: a disease severity classification output head, a treatment suggestion output head, and a prognosis prediction output head; The severity level classification output head learns key threshold patterns through fully connected layers and the Softmax function, classifies comprehensive features, and obtains the probability distribution of the severity level of the illness. The treatment suggestion output head learns the treatment logic in clinical guidelines through a multi-label classifier, classifies the comprehensive features, and obtains the probability distribution of the treatment suggestions. The prognostic prediction output head, through a regression layer and a sigmoid activation function, based on organ failure scoring logic and comprehensive features, predicts the survival probability and obtains the survival probability.

9. A portable intelligent puncture detection method, characterized in that: include: A sample is obtained by puncturing a blood vessel under infrared or ultrasound guidance. It is used to analyze samples and obtain blood biomarkers; it is also used to monitor hemodynamic parameters via PICCO. Based on blood biomarkers and hemodynamic parameters, the constructed analytical model is used to conduct analysis and obtain the results, including: classification of the severity of the patient's condition, treatment recommendations, and prognostic predictions. Obtain and display the analysis results; Obtain a confirmation signal for the analysis results, confirm the analysis results, and display the confirmation result.

10. A portable intelligent puncture detection system, characterized in that: Used to perform the portable intelligent puncture detection method as described in claim 9.

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