Abdominal trauma detection method and system

Abstract

The invention discloses an abdominal trauma detection method and system, and relates to the field of medical information processing and medical resource allocation. The method comprises the following steps: acquiring structured physiological data, accident types and unstructured clinical judgment information in real time; determining a physiological system threat type and a basic risk level; calculating a disease deterioration trend score; judging whether the disease worsening trend score exceeds a worsening trend alarm threshold value or not, and judging whether the unstructured clinical judgment information is clinical judgment confirmation information or not; if yes, correcting the risk level; and allocating medical resources to the patient. According to the abdominal trauma detection method and system provided by the invention, the structured physiological data, the accident type and the unstructured clinical judgment information are acquired and fused in real time, and the disease deterioration trend score and the clinical judgment confirmation mechanism are introduced, so that the problems of risk assessment lag and resource allocation rigidity in the prior art are solved, and the risk assessment accuracy is improved. The method can accurately and timely evaluate the condition of a patient, flexibly distributes medical resources, and improves the treatment efficiency and patient prognosis.

Description

Technical Field

[0001] This application relates to the fields of medical information processing and medical resource allocation, and more specifically, to a method and system for detecting abdominal trauma. Background Technology

[0002] In the field of pre-hospital emergency care, rapid and accurate risk assessment and pre-allocation of examination resources for trauma patients are crucial to ensuring timely and effective treatment. However, existing emergency information processing systems often reveal their limitations when faced with complex and ever-changing patient conditions. Specifically, the information received by the system typically includes two types: standardized, numerical vital sign data and unstructured descriptions provided by emergency personnel based on clinical experience. These descriptions may contain judgments about the trend of the condition or preliminary professional assessments. When performing risk stratification, existing systems often rely excessively on preset numerical thresholds, making it difficult to effectively integrate these two types of information.

[0003] Especially when a patient's condition is rapidly deteriorating, but their vital signs have not yet reached the preset extreme critical thresholds, the judgment of existing systems often lags behind the actual situation. For example, when a patient's blood pressure or heart rate shows a continuous downward or upward trend, but has not yet reached the traditional shock indicators, the system may still classify it as low to medium risk, thus underestimating the patient's true level of criticality. This lag can not only lead to misassessment of the patient's risk level, but also cause rigid allocation of in-hospital examination resources. Doctors or emergency personnel, based on their rich clinical experience, can keenly detect subtle trends in the deterioration of a patient's condition and make urgent resource requests accordingly, such as needing an immediate CT scan instead of a routine ultrasound examination. However, due to their rigid rules and insufficient processing of unstructured information, existing systems often cannot support such urgent resource requests based on trends and experience, causing obstacles for doctors at the system operation level and ultimately delaying the patient's access to critical diagnosis and treatment.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for detecting abdominal trauma, which effectively solves the problems of lagging risk assessment and rigid resource allocation in the prior art. It can more accurately and timely assess the patient's condition and flexibly allocate medical resources, thereby improving treatment efficiency and patient prognosis.

[0006] This application provides a method for detecting abdominal trauma, the technical solution of which is as follows:

[0007] Real-time acquisition of structured physiological data, accident type information, and unstructured clinical judgment information of patients who suffered abdominal trauma before hospitalization;

[0008] Based on the initial structured physiological data and accident type information in the structured physiological data, determine the type of threat to the patient's physiological system and the corresponding basic risk level;

[0009] Based on structured physiological data, calculate the disease deterioration trend score for the corresponding physiological system threat type of the patient;

[0010] It determines whether the disease progression trend score exceeds the preset progression trend alarm threshold, and whether the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type;

[0011] If so, the patient's baseline risk level will be revised to a higher risk level, A.

[0012] Based on the higher risk level A, relevant medical resources are reallocated to the patient.

[0013] The above solution effectively solves the problems of lagging risk assessment and rigid resource allocation in existing technologies, enabling more accurate and timely assessment of patients' conditions and flexible allocation of medical resources, thereby improving treatment efficiency and patient prognosis.

[0014] Optionally, this application also proposes that the structured physiological data be: patient vital signs data carrying collection timestamps;

[0015] The specific steps for calculating the disease progression trend score of the corresponding physiological system threat type in patients based on structured physiological data include:

[0016] Patient vital sign data are classified, and for each category of vital sign data, the slope of change of that category of vital sign data is calculated based on the collection timestamp.

[0017] When the slope of a certain vital sign data change is greater than or equal to a preset warning slope threshold, the deterioration trend score of the corresponding vital sign type is calculated based on the magnitude and duration of the slope change.

[0018] The deterioration trend scores of all calculated vital sign types are summed to obtain the deterioration trend score of the patient's condition for the corresponding physiological system threat type.

[0019] The above scheme further refines the calculation method of the disease deterioration trend score. By using the slope and duration of changes in vital sign data, it can more accurately capture the dynamic changes in the disease and improve the sensitivity of risk assessment.

[0020] Optionally, this application also proposes, further comprising:

[0021] A pre-set validation keyword database is configured, which maintains a list of validation keywords for various physiological system threat types recognized by the medical community.

[0022] Unstructured clinical judgment information refers to the subjective judgment of a patient's condition by pre-hospital emergency personnel based on their experience.

[0023] The specific steps for determining whether unstructured clinical judgment information is clinical judgment confirmation information for the corresponding physiological system threat type include:

[0024] Search the validation keyword database for a list of validation keywords that match the corresponding physiological system threat type;

[0025] Determine whether the subjective judgment information contains at least one validation keyword from the matched validation keyword list;

[0026] If so, then clinical judgment confirmation information for the corresponding physiological system threat type will be generated based on the verification keywords.

[0027] The above approach introduces a validation keyword database, enabling the systematic identification and utilization of unstructured clinical judgment information, thereby enhancing the objectivity and reliability of risk assessment.

[0028] Optionally, this application also proposes a step for determining the type of threat to a patient's physiological system and the corresponding basic risk level based on initial structured physiological data and incident type information in structured physiological data, specifically including:

[0029] Based on the initial structured physiological data and incident type information in the structured physiological data, the patient's physiological system threat type is determined, and the patient's baseline risk score is calculated.

[0030] The patient's baseline risk level is determined based on the baseline risk score.

[0031] The specific steps involved in revising a patient's baseline risk level to a higher risk level A include:

[0032] The patient's current total risk score is calculated based on the baseline risk score, the disease progression trend score, and the preset weighting coefficients.

[0033] The new total risk score is compared with the risk threshold corresponding to each risk level in the preset physiological system threat type.

[0034] When the new total risk score is greater than the risk threshold corresponding to a certain highest risk level A;

[0035] The patient's risk level will then be raised from the basic risk level to the highest risk level, A.

[0036] The above scheme provides specific methods for determining the basic risk level and correcting the risk level. By comprehensively considering the basic risk score, the score of disease deterioration trend, and the weighting coefficient, the risk assessment is more comprehensive and accurate.

[0037] Optionally, this application also proposes that the steps for reallocating relevant medical resources to patients based on a higher risk level A specifically include:

[0038] Based on the revised risk level A, determine the testing equipment required for the patient;

[0039] Determine the time period during which patients wait to use the required testing equipment;

[0040] Allocate the time slots for the required testing equipment to patients;

[0041] Send urgent notifications to relevant medical personnel regarding the treatment of patients during the designated time period when they have access to the necessary testing equipment.

[0042] The above scheme clarifies the process of reallocating medical resources, ensuring that patients can promptly obtain the necessary testing equipment and emergency medical treatment from medical personnel based on their revised risk levels, thereby improving resource utilization efficiency.

[0043] Optionally, this application also proposes that the structured physiological data be: the patient's systolic blood pressure value and heart rate value carrying the collection timestamp;

[0044] The specific steps for calculating the disease progression trend score of the corresponding physiological system threat type in patients based on structured physiological data include:

[0045] Based on the collected timestamps, calculate the time interval between the collection times of adjacent systolic blood pressure values ​​and adjacent heart rate values.

[0046] When a certain interval is longer than or equal to a preset time warning threshold, the rate of change of systolic blood pressure for the corresponding adjacent systolic blood pressure value and the rate of change of heart rate for the corresponding adjacent heart rate value are calculated.

[0047] The heart rate-blood pressure response imbalance index is calculated based on the rate of change of systolic blood pressure and the rate of change of heart rate.

[0048] When the heart rate-blood pressure response imbalance index is higher than the preset physiological compensation benchmark imbalance threshold, the correction value of the systolic blood pressure change rate is calculated based on the physiological compensation benchmark imbalance threshold and the heart rate-blood pressure response imbalance index.

[0049] The blood pressure deterioration trend score is calculated based on the correction value of the systolic blood pressure change rate and the interval duration;

[0050] Heart rate deterioration trend score based on heart rate variability rate and interval duration;

[0051] By summing the blood pressure deterioration trend score and the heart rate deterioration trend score, the disease deterioration trend score of the corresponding physiological system threat type of the patient is calculated.

[0052] Based on the above scheme, a more refined method for calculating the deterioration trend score of the condition was designed for systolic blood pressure and heart rate data. In particular, the heart rate-blood pressure response imbalance index was introduced, which can more accurately reflect the imbalance of physiological compensation mechanism and improve the early warning capability for critical conditions.

[0053] Optionally, this application also proposes a step for calculating the correction value of the systolic blood pressure change rate based on the physiological compensation benchmark imbalance threshold and the heart rate-blood pressure response imbalance index, specifically including:

[0054] Based on the preset multiplier calculation rules, the physiological compensation benchmark imbalance threshold, and the heart rate-blood pressure response imbalance index, a multiplier is generated to calculate the correction value of the systolic blood pressure change rate.

[0055] The correction value for the rate of change of systolic blood pressure is calculated by multiplying the absolute value of the rate of change of systolic blood pressure by the multiplier.

[0056] The above scheme further refines the calculation method of the correction value of the rate of change of systolic blood pressure. Through the multiplier calculation rule, the correction value can more reasonably reflect the degree of physiological compensation imbalance and improve the accuracy of the assessment.

[0057] Optionally, this application also proposes that, following the step of acquiring in real-time structured physiological data, accident type information, and unstructured clinical judgment information of patients who have suffered abdominal trauma before hospitalization, according to the above-mentioned abdominal trauma detection method, the following additional steps are included:

[0058] Real-time acquisition of the patient's first structured physiological data and first unstructured clinical judgment information;

[0059] Based on the initial first structured physiological data and incident type information in the first structured physiological data, determine the patient's first basic risk level and the patient's first physiological system threat type;

[0060] Based on the first structured physiological data, calculate the first disease deterioration trend score for the corresponding first physiological system threat type of the patient;

[0061] Determine whether the first disease deterioration trend score exceeds the preset first deterioration trend alarm threshold, and determine whether the first unstructured clinical judgment information is the first clinical judgment confirmation information of the corresponding first physiological system threat type;

[0062] If so, the patient's primary baseline risk level will be revised and upgraded to a higher risk level B.

[0063] Based on the preset priority ranking table of physiological system threat types, determine the priority of the physiological system threat type and the first physiological system threat type;

[0064] When the priority of the primary physiological system threat type is higher than that of the secondary physiological system threat type, the step of reallocating relevant medical resources to the patient based on the higher risk level A also includes:

[0065] Release relevant medical resources that are reallocated to patients based on risk level A;

[0066] Based on the primary physiological system threat type, relevant medical resources are allocated to patients according to risk level B.

[0067] The above scheme adds a mechanism for parallel processing and priority judgment of threats to multiple physiological systems, ensuring that when a patient experiences multiple critical conditions, the system can dynamically adjust resource allocation according to the priority of the threats, avoid resource conflicts, and optimize treatment strategies.

[0068] Optionally, this application also proposes that, after determining the priority of the physiological system threat type and the first physiological system threat type according to the above-mentioned abdominal trauma detection method, the following steps are also included:

[0069] If the priority of the physiological system threat type is higher than that of the first physiological system threat type, a treatment notification with the risk level B of the first physiological system threat type will be sent to the relevant medical personnel.

[0070] The above scheme supplements the mechanism of sending treatment notifications after priority assessment, ensuring that relevant medical personnel can promptly understand and respond to changes in the priority of threats to different physiological systems and coordinate treatment.

[0071] Optionally, this application also proposes an abdominal trauma detection system, the technical solution of which is as follows:

[0072] include:

[0073] The acquisition module is used to acquire in real time structured physiological data, accident type information, and unstructured clinical judgment information of patients who have suffered abdominal trauma before hospitalization;

[0074] The determination module is used to determine the type of threat to the patient's physiological system and the corresponding basic risk level based on the initial structured physiological data and accident type information in the structured physiological data;

[0075] The calculation module is used to calculate the disease deterioration trend score of the corresponding physiological system threat type of the patient based on structured physiological data;

[0076] A judgment module, configured to judge whether a disease deterioration trend score exceeds a preset deterioration trend alarm threshold, and to judge whether unstructured clinical judgment information is clinical judgment confirmation information of a corresponding physiological system threat type;

[0077] A correction module, configured to, when the disease deterioration trend score exceeds the preset deterioration trend alarm threshold and the unstructured clinical judgment information is clinical judgment confirmation information of a corresponding physiological system threat type, upgrade and correct the patient's basic risk level to a higher risk level A;

[0078] An allocation module, configured to re-allocate relevant medical resources for the patient according to the higher risk level A.

[0079] Through the above solution, a system for implementing the above abdominal trauma detection method is provided. Through modular design, the method can be efficiently and stably deployed and run, facilitating practical application.

[0080] As can be seen from the above, an abdominal trauma detection method and system provided by this application can dynamically and accurately evaluate the patient's risk level by obtaining and fusing structured physiological data, accident type information, and unstructured clinical judgment information in real time, and introducing a disease deterioration trend score and a clinical judgment confirmation mechanism, and accordingly flexibly allocate medical resources, effectively solving the problems of lagging risk assessment and rigid resource allocation in the prior art, and being able to more accurately and timely evaluate the patient's condition and flexibly allocate medical resources, thereby improving the treatment efficiency and the prognosis of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a flowchart of an abdominal trauma detection method provided by this application.

[0082] Figure 2 It is a structural diagram of an abdominal trauma detection system provided by this application.

[0083] Figure 2 In the figure: 1 is an acquisition module; 2 is a determination module; 3 is a calculation module; 4 is a judgment module; 5 is a correction module; 6 is an allocation module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] Next, the technical solutions in this application will be clearly and completely described in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments.

[0085] Refer Figure 1 , this application proposes an abdominal trauma detection method, including:

[0086] S110: Real-time acquisition of structured physiological data, accident type information, and unstructured clinical judgment information of patients who suffered abdominal trauma before hospitalization;

[0087] S120. Based on the initial structured physiological data and accident type information in the structured physiological data, determine the type of threat to the patient's physiological system and the corresponding basic risk level;

[0088] S130. Based on structured physiological data, calculate the disease deterioration trend score for the corresponding physiological system threat type of the patient;

[0089] S140, determine whether the disease deterioration trend score exceeds the preset deterioration trend alarm threshold, and determine whether the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type;

[0090] S150. When the disease deterioration trend score exceeds the preset deterioration trend alarm threshold, and the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type, the patient's basic risk level will be upgraded and corrected to a higher risk level A.

[0091] S160. Based on the higher risk level A, relevant medical resources are reallocated to the patient.

[0092] Structured physiological data refers to standardized and quantifiable patient physiological indicators, which can be obtained in various forms, such as values ​​like blood pressure, heart rate, respiratory rate, and blood oxygen saturation automatically collected by vital sign monitoring equipment, or laboratory test results entered into electronic medical record systems. Its primary purpose is to provide quantitative evidence of the patient's current physiological state. Unstructured clinical judgment information refers to subjective judgments made by emergency personnel or medical staff based on their professional knowledge and experience regarding the patient's condition. This information can be recorded and transmitted through methods such as voice recording, free text input, or preset phrase selection. Its main purpose is to supplement the structured data in capturing changes in the patient's condition and risks, reflecting the professional judgment of frontline clinical personnel. The disease deterioration trend score is an indicator used to assess the degree and speed of the patient's disease deterioration by analyzing and calculating the patterns of changes in the patient's structured physiological data over time. It can be generated using methods such as time series analysis, slope calculation, or physiological index models. Its main purpose is to identify changes in the patient's physiological state and provide early warning of disease progression. Clinical judgment confirmation information refers to a confirmation signal that can be recognized by the system after processing and verifying unstructured clinical judgment information. It indicates that the unstructured judgment contains a clear indication of the type of threat to the physiological system. This information can be generated using methods such as keyword matching, natural language processing, or manual review. Its main purpose is to transform subjective judgments into system-recognizable evidence to assist in risk assessment. The higher-level risk grade A refers to a risk assessment level that is dynamically upgraded based on the patient's baseline risk level, the trend of disease deterioration, and clinical judgment confirmation information. It can be determined using preset risk grading standards or dynamic scoring models. Its main purpose is to ensure that patients receive medical resources of priority commensurate with the urgency of their condition.

[0093] The core innovation of this application lies in the fact that by integrating the disease deterioration trend score obtained from the analysis of structured physiological data with the clinical judgment confirmation information transformed from unstructured clinical judgment information, the problem of risk assessment lagging behind the actual rate of disease deterioration is solved, thereby achieving the effect of dynamically and accurately assessing patient risk and flexibly allocating medical resources.

[0094] This application's solution achieves risk assessment and medical resource allocation for patients with abdominal trauma through a series of collaborative steps. First, the system acquires the patient's structured physiological data, incident type information, and unstructured clinical judgment information in real time. The structured physiological data provides quantitative indicators, the incident type information provides context, and the unstructured clinical judgment information supplements the experience-based judgment of medical staff. Based on the initial data and incident type information in the structured physiological data, the system determines the patient's physiological system threat type and sets a corresponding baseline risk level, providing a baseline for subsequent dynamic adjustments. On this basis, the system continuously calculates the patient's condition deterioration trend score for the corresponding physiological system threat type based on the structured physiological data. This calculation aims to capture changes in the patient's physiological indicators over time, identifying early signals of a deteriorating trend even if the value has not yet reached an extreme threshold. Subsequently, the system performs a dual judgment: first, it determines whether the condition deterioration trend score exceeds a preset deterioration trend alarm threshold to quantitatively identify the rate of condition deterioration; second, it determines whether the unstructured clinical judgment information is clinical confirmation information for the corresponding physiological system threat type, integrating the professional experience of medical staff. When both conditions are met—that is, the quantitative trend value is triggered and the clinical experience judgment is confirmed—the system upgrades the patient's baseline risk level to a higher risk level, A. This mechanism ensures the timeliness and accuracy of risk assessment, avoiding the lag that may result from relying solely on static numerical thresholds. Ultimately, based on the upgraded risk level A, the system reallocates relevant medical resources to the patient, ensuring that the patient receives examination and treatment priorities commensurate with their urgency, thereby optimizing the efficiency of medical resource utilization.

[0095] Through the aforementioned technical solution, this application effectively addresses the problems in existing technologies, such as risk stratification lagging behind the actual rate of disease deterioration and decision-making conflicts arising from system rules rejecting doctors' urgent resource requests based on clinical experience. Specifically, by acquiring and integrating structured physiological data and unstructured clinical judgment information in real time, this application can dynamically capture subtle changes and deterioration trends in a patient's condition. Combined with the professional judgment of medical personnel, risk assessment no longer relies solely on static numerical thresholds, thus avoiding assessment lag. When the trend of disease deterioration is quantified and confirmed by clinical judgment, the patient's risk level can be promptly and accurately adjusted, ensuring the dynamism and accuracy of risk assessment. This dynamically adjusted risk level directly guides the reallocation of medical resources, allowing for flexible adjustments to examination equipment and treatment priorities based on the patient's actual urgency. This avoids resource allocation conflicts caused by rigid system rules, improving the efficiency of pre-hospital emergency care and in-hospital treatment, and enhancing the timeliness of patient care.

[0096] This application further proposes a method for detecting abdominal trauma, wherein the structured physiological data is: patient vital signs data carrying collection timestamps;

[0097] The specific steps for calculating the disease progression trend score of the corresponding physiological system threat type in patients based on structured physiological data include:

[0098] Patient vital sign data are classified, and for each category of vital sign data, the slope of change of that category of vital sign data is calculated based on the collection timestamp.

[0099] When the slope of a certain vital sign data change is greater than or equal to a preset warning slope threshold, the deterioration trend score of the corresponding vital sign type is calculated based on the magnitude and duration of the slope change.

[0100] The deterioration trend scores of all calculated vital sign types are summed to obtain the deterioration trend score of the patient's condition for the corresponding physiological system threat type.

[0101] Classifying patient vital sign data involves grouping different types of vital sign data according to their physiological attributes or clinical significance. For example, blood pressure, heart rate, respiratory rate, and body temperature can be categorized separately. The purpose is to analyze different physiological indicators independently, avoid mutual interference between different indicators, and improve the accuracy of the assessment. The slope of change refers to the rate at which a vital sign value changes over a specific time period. It can be calculated using mathematical methods such as linear regression, difference method, or moving average method. Its purpose is to quantify the dynamic trend of vital signs. The preset warning slope threshold is a critical value for the rate of change of each type of vital sign data, used to determine whether the condition has begun to deteriorate rapidly. This threshold can be determined based on clinical experience, medical guidelines, or big data analysis results. Its purpose is to identify abnormal changes in the rate of change of vital signs. The deterioration trend score is a numerical value that quantifies the contribution of a particular vital sign type to the deterioration of the condition based on the slope of change and its duration. This score can be calculated using weighted summation, piecewise functions, or lookup tables. Its purpose is to transform dynamic changes into an additive risk indicator.

[0102] This application's solution provides a more refined and dynamic mechanism for calculating disease deterioration trend scores by utilizing patient vital sign data with accompanying collection timestamps. First, the patient's vital sign data is systematically categorized, ensuring that different physiological indicators can be analyzed independently and accurately, avoiding data contamination. Then, for each category of vital sign data, the system calculates the slope of change for that category of vital sign data based on its accompanying collection timestamp. This process captures the dynamic trend of vital sign values ​​changing over time, rather than relying solely on a single static value, thus more accurately reflecting the real-time evolution of the patient's condition.

[0103] When the slope of a change in a vital sign reaches or exceeds a preset warning slope threshold, it indicates that the rate of change in that vital sign has exceeded the normal or acceptable range, suggesting that the patient's condition may be rapidly deteriorating. At this point, the system will further calculate a deterioration trend score for the corresponding vital sign type based on the magnitude of the slope and its duration. The magnitude of the slope directly reflects the degree of deterioration, while the duration provides an assessment of the stability and severity of the deterioration. Taking both factors into account allows for a comprehensive assessment of the severity of the deterioration.

[0104] Finally, the calculated deterioration trend scores for all vital sign types are summed to obtain the total deterioration trend score for the corresponding physiological system threat type of the patient. This summation mechanism allows the system to comprehensively consider the dynamic changes of multiple physiological indicators of the patient, forming a comprehensive assessment of the risk of disease deterioration. Combining this dynamically calculated deterioration trend score with the baseline risk level and determining whether it exceeds the preset deterioration trend alarm threshold can effectively compensate for the shortcomings of relying solely on initial structured physiological data and incident type information to determine the baseline risk level. In this way, even if the patient's vital sign values ​​have not yet reached the traditional critical threshold, as long as their changing trends show signs of deterioration, the system can promptly identify and raise the patient's risk level, thereby avoiding delays in the allocation of critical medical resources due to delayed judgment and ensuring that the patient receives timely medical treatment that matches their actual condition.

[0105] Through the aforementioned technical solution, this application enables dynamic and quantitative assessment of patient condition deterioration trends using patient vital sign data with collection timestamps. By classifying vital sign data and calculating their change slopes, subtle trends in disease progression can be accurately captured, allowing for timely identification of potential deterioration risks even before vital sign values ​​reach traditional critical thresholds. When the change slope exceeds a preset warning threshold, a deterioration trend score is calculated by combining the magnitude and duration of the change slope, resulting in an assessment that more comprehensively reflects the severity and urgency of the condition. Finally, by accumulating the deterioration trend scores for all vital sign types, a comprehensive assessment of the disease's deterioration trend can be obtained, providing a more reliable and timely basis for subsequent risk level judgments. This avoids the problem of untimely allocation of medical resources caused by the lag in static numerical judgments, helping patients receive more precise treatment.

[0106] This application further proposes a method for detecting abdominal trauma, which, in addition to the above-mentioned approach, also includes:

[0107] A pre-set validation keyword database is configured, which maintains a list of validation keywords for various physiological system threat types recognized by the medical community.

[0108] Unstructured clinical judgment information refers to the subjective judgment of patients' conditions by pre-hospital emergency personnel based on their experience.

[0109] The specific steps for determining whether unstructured clinical judgment information is clinical judgment confirmation information for the corresponding physiological system threat type include:

[0110] Search the validation keyword database for a list of validation keywords that match the corresponding physiological system threat type; determine whether the subjective judgment information contains at least one validation keyword from the matched validation keyword list;

[0111] If so, then clinical judgment confirmation information for the corresponding physiological system threat type will be generated based on the verification keywords.

[0112] The validation keyword database refers to a pre-built and stored knowledge base, which can be a relational database, a NoSQL database, or a simple collection of text files. Its purpose is to provide a reference for the parsing and standardization of unstructured clinical judgment information. The validation keyword list for various physiological system threat types, recognized by the medical community, refers to a set of specific words, phrases, or symptom descriptions determined by medical experts or authoritative institutions for different physiological system threats (e.g., hemorrhagic shock, septic shock, respiratory failure, etc.), which can indicate or confirm the existence of the threat. These can be key terms extracted through expert consensus, clinical guidelines, or big data analysis, aiming to transform complex clinical experience into information that machines can recognize and process. The subjective judgment information of pre-hospital emergency personnel based on experience refers to the description or inference of the patient's condition expressed in natural language, formed by emergency personnel based on their professional knowledge and clinical experience after observing, questioning, and initially assessing the patient's condition on-site. This can be free text obtained through voice recording, text input, or handwritten notes, aiming to capture timely and important clinical clues that are difficult to reflect in structured data. Clinical judgment confirmation information refers to a standardized and quantifiable information representation generated by matching and verifying unstructured subjective judgment information through keywords. It can be a Boolean value (indicating whether it is confirmed), a confidence score, or a structured data entry with a specific label. Its purpose is to transform ambiguous subjective judgments into signals that the system can directly use for risk assessment and decision support.

[0113] This application addresses the problem of insufficient utilization of subjective judgment information from pre-hospital emergency personnel by introducing a mechanism for processing unstructured clinical judgment information. Specifically, after acquiring the patient's structured physiological data, accident type information, and unstructured clinical judgment information, this solution pre-establishes a validation keyword database. This database maintains a list of validation keywords for various physiological system threat types recognized by the medical community. The establishment of this database provides a knowledge foundation for subsequent processing of unstructured information, enabling previously ambiguous subjective judgments to be associated with medical concepts. When it is necessary to determine whether unstructured clinical judgment information is clinical confirmation information for the corresponding physiological system threat type, the system first searches the validation keyword database for a list of validation keywords that match the current patient's physiological system threat type. For example, if the patient is initially judged to have a physiological system threat of "hemorrhagic shock," the system will retrieve a list of keywords related to hemorrhagic shock, such as "pale complexion," "cold sweats," "rapid and weak pulse," and "abdominal distension." Subsequently, the system will determine whether the subjective judgment information of the pre-hospital emergency personnel contains at least one validation keyword from the matched list of validation keywords. This matching process filters and identifies unstructured information, comparing emergency responders' experiential descriptions with pre-defined medical standards. If the subjective judgment information contains matching validation keywords, clinical judgment confirmation information for the corresponding physiological system threat type is generated based on these validation keywords. This step is crucial; it transforms unstructured subjective judgments, which are difficult to quantify and utilize directly, into structured confirmation signals that the system can recognize and process. For example, if an emergency responder reports "patient is pale, suspected internal bleeding," and "pale" is one of the keywords in the hemorrhagic shock keyword list, the system will generate a "clinical judgment confirmation of hemorrhagic shock" signal. This clinical judgment confirmation information, along with the severity of the condition score, is then used to determine whether the patient's baseline risk level needs to be upgraded to a higher risk level, A. This avoids the risk assessment lag caused by relying solely on rigid numerical thresholds, allowing the system to identify and respond to the patient's actual condition earlier. In this way, the solution effectively integrates structured data and unstructured clinical experience, improving the accuracy and timeliness of risk assessment, thereby buying valuable treatment time for patients and optimizing the allocation efficiency of medical resources.

[0114] Through the aforementioned technical solution, this application can utilize the subjective judgment information of pre-hospital emergency personnel, transforming it from an unstructured form into quantifiable clinical judgment confirmation information. This allows information that was previously difficult to directly apply to risk assessment to be systematically identified and processed, thus compensating for the shortcomings of solely relying on structured physiological data for risk assessment. This improves the accuracy and timeliness of patient condition assessment, avoids risk underestimation and delayed allocation of medical resources due to insufficient information utilization, and ensures that patients receive more timely and precise medical intervention.

[0115] This application further proposes steps for determining the type of threat to a patient's physiological system and the corresponding basic risk level, including:

[0116] Based on the initial structured physiological data and incident type information in the structured physiological data, the patient's physiological system threat type is determined, and the patient's baseline risk score is calculated.

[0117] The patient's baseline risk level is determined based on the baseline risk score.

[0118] The specific steps involved in revising a patient's baseline risk level to a higher risk level A include:

[0119] The patient's current total risk score is calculated based on the baseline risk score, the disease progression trend score, and the preset weighting coefficients.

[0120] The new total risk score is compared with the risk threshold corresponding to each risk level in the preset physiological system threat type.

[0121] When the new total risk score is greater than the risk threshold corresponding to a certain highest risk level A;

[0122] The patient's risk level will then be raised from the basic risk level to the highest risk level, A.

[0123] Among them, the patient's physiological system threat type refers to the type identified by rule matching or machine learning models based on preset medical classification standards, combined with the patient's initial physiological data and accident type information, such as hemorrhagic shock and neurogenic shock, with the aim of making a preliminary medical classification of the patient's condition; the baseline risk score refers to the quantitative value calculated by preset scoring models (such as the revised Glasgow Coma Scale, trauma severity score, etc.) based on initial structured physiological data (such as blood pressure, heart rate, respiratory rate, etc.) and accident type information (such as high-energy trauma, penetrating injury, etc.), with the aim of providing a quantitative basis for subsequent risk level determination; the baseline risk level refers to the risk level to which the patient is classified based on the baseline risk score and against preset risk level classification standards (such as mild, moderate, severe, etc.), with the aim of providing a basis for subsequent resource allocation; the disease deterioration trend score refers to the score reflecting the risk level of the patient's condition. The quantitative indicators of the patient's continuously deteriorating physiological state can be calculated based on the dynamic trends of vital sign data, aiming to capture the dynamic evolution of the patient's condition. Preset weighting coefficients are parameters used to adjust the relative importance of the baseline risk score and the disease deterioration trend score when calculating the new total risk score. These can be pre-set based on clinical experience or data analysis, aiming to flexibly reflect the impact of different factors on the risk level. The new total risk score is a quantitative assessment of the patient's current overall risk status after comprehensively considering the patient's baseline risk and disease deterioration trend. It can be calculated by weighting the baseline risk score, the disease deterioration trend score, and the corresponding weighting coefficients, aiming to provide a more comprehensive risk assessment. The risk threshold is a critical value used to distinguish different risk levels. It can be pre-set based on medical guidelines or statistical analysis, aiming to provide an objective standard for judging risk levels.

[0124] This application's solution optimizes the treatment process for patients with abdominal trauma by refining the patient risk assessment mechanism. First, the system identifies the types of physiological system threats the patient may face based on the patient's initial structured physiological data and accident type information, and calculates a quantified baseline risk score. This score comprehensively reflects the patient's physiological state and external trauma mechanism at the initial stage of the accident. Subsequently, the system maps this baseline risk score to a preset risk level standard to determine the patient's baseline risk level. In cases where the patient's condition may worsen, this solution further introduces a dynamic correction mechanism. The system not only considers the patient's initial baseline risk but also incorporates a disease deterioration trend score that reflects the dynamic changes in the condition. By weighting the baseline risk score, the disease deterioration trend score, and preset weighting coefficients, a new total risk score that better reflects the patient's current overall condition can be obtained. This calculation method allows risk assessment to dynamically capture the evolution of the condition, rather than relying solely on static initial data. Next, the system compares this new total risk score with the corresponding risk thresholds for each preset risk level for different types of physiological system threats. This comparison mechanism ensures that risk level assessments are based on quantitative evidence and objective standards. When the calculated total risk score exceeds several preset risk levels, with risk level A being the highest, the system immediately raises the patient's risk level from its original baseline risk level to risk level A. In this way, this approach overcomes the limitations of traditional methods that rely solely on static thresholds, enabling earlier and more accurate identification of patients with rapidly deteriorating conditions. This dynamic, quantitative risk assessment and upgrade mechanism allows the patient's risk level to reflect their true condition in a timely manner, providing a more scientific and timely basis for subsequent medical resource allocation and ensuring that critically ill patients receive priority and appropriate treatment.

[0125] Through the aforementioned technical solution, this application can more accurately determine a patient's baseline risk level and more rationally and dynamically improve that level. By combining initial structured physiological data and accident type information, the patient's initial state can be quantitatively assessed, resulting in an objective baseline risk score, which in turn determines the baseline risk level. More importantly, during the risk level improvement process, the solution comprehensively considers the patient's baseline risk score and the disease deterioration trend score, and introduces adjustable weighting coefficients. This ensures that the final calculated total risk score fully reflects the patient's current condition, including its potential deterioration trend. When this total risk score exceeds several preset risk level thresholds, the system can quickly raise the patient's risk level to the highest among these risk levels, avoiding the risk assessment lag caused by relying solely on static thresholds. This ensures that even when the condition is rapidly evolving but has not yet reached traditional critical threshold values, the patient can be promptly identified as high-risk, thereby receiving priority medical resource allocation and emergency treatment, significantly improving the response speed and treatment efficiency of pre-hospital emergency care.

[0126] This application further proposes steps for reallocating relevant medical resources to patients based on a higher risk level A, including: determining the testing equipment required by the patient based on the revised risk level A;

[0127] Determine the time slots during which patients need to use the required testing equipment; allocate the right to use the required testing equipment time slots to patients;

[0128] Send urgent notifications to relevant medical personnel regarding the treatment of patients during the designated time period when they have access to the necessary testing equipment.

[0129] The required testing equipment refers to the medical examination or treatment equipment that a patient must use to make a clear diagnosis or for emergency intervention under the current risk level. This can be achieved using CT scanners, MRI equipment, ultrasound diagnostic instruments, or operating rooms, etc., with the aim of providing patients with accurate diagnostic information or timely treatment. Time slots refer to the time intervals during which medical equipment is available to patients, specifically idle time slots, appointment time slots, or emergency queueing time slots, with the aim of planning and managing limited medical resources in terms of time. Time slot usage rights refer to the patient's right to use specific medical equipment within a specific time period, specifically through system-generated appointment vouchers, electronic passes, or priority tokens, with the aim of ensuring that patients can use equipment as planned and avoiding resource conflicts. Urgent patient treatment notifications are urgent reminders issued to medical personnel responsible for patient treatment, containing key patient information and resource allocation details, specifically via SMS, mobile application push notifications, electronic medical record system pop-ups, or voice calls, with the aim of reminding medical personnel to respond promptly and expedite the treatment process.

[0130] This application's solution achieves dynamic and precise allocation of medical resources through a series of logically rigorous steps. First, based on a higher-level risk level (A) determined by the system's previous assessment and correction, the system intelligently identifies the most urgently needed testing equipment for the patient. This assessment is based on a comprehensive analysis of the patient's real-time condition evolution and clinical judgment information, ensuring that the allocated equipment closely matches the patient's actual needs and avoiding unnecessary resource waste or shortages. Given the limited availability of medical testing equipment, the system further dynamically determines the time slot for the patient to use the testing equipment. This is not simply allocating the next available time slot, but rather comprehensively considering the real-time occupancy of the equipment, appointments of other high-priority patients, and the patient's urgency level, thereby planning the most reasonable and feasible usage time window. Based on this, the system explicitly allocates the right to use the testing equipment time slot to the patient, essentially reserving a dedicated equipment access channel for the patient, ensuring that the patient can use critical equipment immediately or in the shortest possible time upon arrival at the hospital, thus significantly reducing waiting time. Simultaneously, to ensure seamless medical workflow, the system sends an expedited treatment notification to relevant medical personnel for the patient with the allocated testing equipment time slot. This notification not only outlines the patient's emergency status but also specifies the time and location for equipment use, allowing medical staff to prepare in advance, such as allocating personnel and preparing supplies, so that the treatment process can be initiated immediately upon the patient's arrival. It is precisely this precise resource matching, time planning, and real-time information synchronization based on dynamic risk assessment that enables patients to receive the necessary testing equipment and treatment promptly, effectively avoiding delays in treatment caused by unreasonable resource allocation or information lag.

[0131] Through the aforementioned technical solution, this application can dynamically and accurately determine the required testing equipment based on the patient's revised risk level and rationally plan the equipment usage time, thereby promptly allocating equipment access to patients. Simultaneously, by sending expedited notifications to relevant medical personnel, it ensures that they respond promptly and are prepared. This enables patients to obtain critical testing equipment in a timely manner, avoiding unnecessary waiting and treatment delays, improving the efficiency of medical resource utilization and the timeliness of patient treatment.

[0132] This application further proposes steps for calculating the disease deterioration trend score of a patient for the corresponding physiological system threat type, including:

[0133] Structured physiological data consist of the patient's systolic blood pressure and heart rate values, each accompanied by a data collection timestamp.

[0134] Based on the collected timestamps, calculate the time interval between the collection times of adjacent systolic blood pressure values ​​and adjacent heart rate values.

[0135] When a certain interval is longer than or equal to a preset time warning threshold, the rate of change of systolic blood pressure for the corresponding adjacent systolic blood pressure value and the rate of change of heart rate for the corresponding adjacent heart rate value are calculated.

[0136] The heart rate-blood pressure response imbalance index is calculated based on the rate of change of systolic blood pressure and the rate of change of heart rate.

[0137] When the heart rate-blood pressure response imbalance index is higher than the preset physiological compensation benchmark imbalance threshold, the systolic blood pressure change rate correction value is calculated based on the physiological compensation benchmark imbalance threshold and the heart rate-blood pressure response imbalance index.

[0138] The blood pressure deterioration trend score is calculated based on the correction value of the systolic blood pressure change rate and the interval duration;

[0139] Heart rate deterioration trend score based on heart rate variability rate and interval duration;

[0140] By summing the blood pressure deterioration trend score and the heart rate deterioration trend score, the disease deterioration trend score of the corresponding physiological system threat type of the patient is calculated.

[0141] Among them, the heart rate-blood pressure response imbalance index refers to an indicator that reflects the physiological state by quantifying the degree of imbalance in the physiological compensatory relationship between the patient's heart rate and systolic blood pressure. It can be calculated by the ratio or difference between the rate of change of systolic blood pressure and the rate of change of heart rate, or by a composite function based on the trends of the two. Its purpose is to capture the changes in the compensatory capacity of the patient's cardiovascular system under stress.

[0142] The systolic blood pressure change rate correction value refers to the value used to adjust the systolic blood pressure change rate when the heart rate-blood pressure response imbalance index exceeds a preset threshold. It can be achieved by multiplying the absolute value of the systolic blood pressure change rate with a multiplier generated based on the physiological compensation benchmark imbalance threshold and the heart rate-blood pressure response imbalance index. Its purpose is to more accurately reflect the impact of heart rate-blood pressure imbalance on the trend of blood pressure deterioration.

[0143] This application's solution constructs a more refined mechanism for calculating the severity trend score of a patient's condition by focusing on the patient's systolic blood pressure and heart rate values ​​with acquisition timestamps. First, the system acquires these time-informed physiological data and calculates the time interval between adjacent systolic blood pressure and heart rate values. This time interval allows the system to identify the frequency of data updates, providing a time reference for subsequent rate-of-change calculations. When a certain interval duration is detected to reach or exceed a preset time warning threshold, the system further calculates the corresponding rate of change in systolic blood pressure and heart rate. This time interval-based triggering mechanism avoids misjudgments caused by frequent data fluctuations and ensures that rate assessment is only performed when there is a sufficient time span to reflect the true trend.

[0144] Building upon this foundation, the system further calculates a heart rate-blood pressure response imbalance index. This index quantifies the degree of imbalance in the physiological compensatory relationship between a patient's heart rate and blood pressure by comprehensively considering the rates of change in systolic blood pressure and heart rate. The introduction of this imbalance index allows the system to move beyond independent changes in single vital signs and gain deeper insights into the overall compensatory capacity of the patient's cardiovascular system under stress. When the heart rate-blood pressure response imbalance index exceeds a preset physiological compensation baseline imbalance threshold, the system calculates a correction value for the rate of change in systolic blood pressure based on this threshold and the imbalance index, adjusting the rate of change in systolic blood pressure accordingly. This correction step demonstrates the profound impact of heart rate-blood pressure imbalance on blood pressure assessment, making the assessment of blood pressure deterioration trends more accurate and sensitive.

[0145] Subsequently, the system combines the corrected systolic blood pressure rate of change with the interval duration to calculate a blood pressure deterioration trend score; simultaneously, it combines the heart rate rate of change with the interval duration to calculate a heart rate deterioration trend score. By combining the rate of change with the duration, the protocol can comprehensively assess the deterioration trend of both vital signs. Finally, the blood pressure and heart rate deterioration trend scores are summed to obtain the disease deterioration trend score for the corresponding physiological system threat type of the patient.

[0146] It is precisely this consideration of time intervals, quantification of the physiological compensatory relationship between heart rate and blood pressure, and dynamic correction of the rate of change in systolic blood pressure that enables this approach to more accurately capture the dynamic changes and potential risks of a patient's condition. This more precise score of the disease's deterioration trend can serve as the basis for adjusting the baseline risk level of the patient's physiological system threat type, thereby making the patient's risk level assessment closer to the actual condition. This provides more reliable decision support for the reallocation of relevant medical resources for the patient, effectively avoiding the problems of risk underestimation and resource allocation lag caused by insufficient data interpretation.

[0147] Through the above technical solutions, this application can more accurately assess the deterioration trend of a patient's condition. By introducing collection timestamps and calculating the time interval between adjacent data, the problem of excessively long time intervals in vital sign data collection can be effectively addressed, ensuring the accuracy of the rate of change calculation. By calculating the heart rate-blood pressure response imbalance index and correcting the systolic blood pressure change rate accordingly, the physiological compensatory relationship between heart rate and blood pressure can be fully considered, making the calculation of the deterioration trend score more refined and sensitive. This more accurately reflects the dynamic changes and potential risks of the patient's condition, avoiding assessment bias caused by insufficient data processing.

[0148] This application further proposes a step for calculating the correction value of the systolic blood pressure change rate based on the physiological compensation benchmark imbalance threshold and the heart rate-blood pressure response imbalance index, including:

[0149] Based on the preset multiplier calculation rules, the physiological compensation benchmark imbalance threshold, and the heart rate-blood pressure response imbalance index, a multiplier is generated to calculate the correction value of the systolic blood pressure change rate.

[0150] The correction value for the rate of change of systolic blood pressure is calculated by multiplying the absolute value of the rate of change of systolic blood pressure by the multiplier.

[0151] The preset multiplier calculation rules refer to a series of logical judgments, mathematical formulas, or lookup tables used to determine the multiplier value. These rules can be implemented using conditional judgment functions, polynomial fitting models, or predefined mapping relationships. Their purpose is to dynamically adjust the multiplier size based on the relative relationship between the physiological compensation baseline imbalance threshold and the heart rate-blood pressure response imbalance index. The multiplier, a dimensionless numerical factor, is used to amplify or reduce the absolute value of the systolic blood pressure change rate. It can be represented as a floating-point number or an integer, and its purpose is to quantify the impact of the degree of physiological compensation imbalance on the correction magnitude of the systolic blood pressure change rate.

[0152] This application's solution incorporates a multiplier to integrate the effects of the physiological compensation baseline imbalance threshold and the heart rate-blood pressure response imbalance index into the correction process for the systolic blood pressure change rate. Specifically, firstly, a multiplier is generated based on preset multiplier calculation rules, the physiological compensation baseline imbalance threshold, and the heart rate-blood pressure response imbalance index to calculate the corrected value for the systolic blood pressure change rate. This process ensures that the multiplier generation is not arbitrary but based on a set of preset rules, comprehensively considering both the physiological compensation baseline imbalance threshold and the heart rate-blood pressure response imbalance index. The physiological compensation baseline imbalance threshold represents the normal range of physiological compensation capacity in the human body, while the heart rate-blood pressure response imbalance index reflects the degree of coordination between the patient's heart rate and blood pressure. By incorporating these two factors into the multiplier calculation, it can be ensured that the corrected systolic blood pressure change rate reflects the patient's true physiological state. For example, when the heart rate-blood pressure response imbalance index is much higher than the physiological compensation baseline imbalance threshold, it indicates that the patient's physiological compensation mechanism has failed and the condition is rapidly deteriorating. In this case, the generated multiplier will increase accordingly, thereby increasing the magnitude of the corrected systolic blood pressure change rate. Subsequently, the absolute value of the rate of change of systolic blood pressure is multiplied by a multiplier to calculate the corrected rate of change of systolic blood pressure. The absolute value of the rate of change of systolic blood pressure is used because both a decrease and an increase in blood pressure can indicate a worsening of the condition and require equal correction. Multiplying the absolute value by the multiplier applies the effect of the multiplier calculated in the previous step to the rate of change of systolic blood pressure, thus obtaining the final corrected value. This corrected value reflects the degree of deterioration of the patient's condition, providing a reliable basis for subsequent risk assessment and resource allocation.

[0153] This proposed solution, combined with prior methods, further refines the calculation of systolic blood pressure rate change correction values ​​by determining the heart rate-blood pressure response imbalance index and identifying values ​​exceeding the physiological compensation benchmark imbalance threshold. By introducing a dynamically generated multiplier, the correction value can sensitively and accurately reflect the degree of loss of the patient's physiological compensatory capacity, thus overcoming the limitations of simple or fixed correction methods. This quantitative assessment of the disease progression trend provides reliable input for the subsequent accumulation of blood pressure and heart rate progression trend scores, enabling the disease progression trend score for the corresponding physiological system threat type to accurately reflect the patient's true condition and providing solid data support for risk level adjustments.

[0154] Through the above technical solution, this application can dynamically generate a multiplier for calculating the systolic blood pressure rate of change correction value based on preset multiplier calculation rules, physiological compensation benchmark imbalance threshold, and heart rate-blood pressure response imbalance index. The absolute value of the systolic blood pressure rate of change is then multiplied by this multiplier to calculate the systolic blood pressure rate of change correction value. This makes the calculation of the systolic blood pressure rate of change correction value no longer a simple linear relationship or a fixed value, but rather reflects the degree of physiological compensation imbalance in the patient, thereby assessing the patient's condition deterioration trend and providing a reliable basis for subsequent risk assessment and medical resource allocation.

[0155] This application further proposes steps for dynamically adjusting patient risk levels and reallocating medical resources, including:

[0156] Real-time acquisition of the patient's first structured physiological data and first unstructured clinical judgment information;

[0157] Based on the initial first structured physiological data and accident type information in the first structured physiological data, the patient's first basic risk level and the patient's first physiological system threat type are determined;

[0158] Based on this first structured physiological data, calculate the first disease deterioration trend score for the patient's corresponding first physiological system threat type;

[0159] Determine whether the first disease deterioration trend score exceeds the preset first deterioration trend alarm threshold, and determine whether the first unstructured clinical judgment information is the first clinical judgment confirmation information of the corresponding first physiological system threat type;

[0160] If so, the patient's primary baseline risk level will be revised and upgraded to a higher risk level B.

[0161] Based on the preset priority ranking table of physiological system threat types, determine the priority of this physiological system threat type compared to the first physiological system threat type;

[0162] When the priority of the first physiological system threat type is higher than that of the physiological system threat type, the step of reallocating relevant medical resources to the patient based on the higher risk level A also includes:

[0163] Release relevant medical resources that are reallocated to the patient based on risk level A;

[0164] Based on the first physiological system threat type, relevant medical resources are allocated to the patient according to the risk level B.

[0165] The first severity score is a numerical measure of the degree of deterioration in a patient's condition. It can be calculated by analyzing the rate of change, fluctuation range, or abnormal trends of specific physiological indicators over time, such as the rate of sustained decrease in blood pressure or the magnitude of sustained increase in heart rate. The physiological system threat type priority ranking table is a pre-defined list used to determine the order of urgency among different physiological system threat types. It can be established through medical expert consensus, clinical guidelines, or big data analysis. For example, respiratory system threats can be prioritized over circulatory system threats, and circulatory system threats can be prioritized over nervous system threats, to guide the priority allocation of medical resources.

[0166] This application's solution effectively overcomes the limitations of relying solely on initial assessments for medical resource allocation by introducing a real-time monitoring and response mechanism for dynamic changes in the patient's condition. Specifically, after completing the initial risk assessment and allocating medical resources, the system continuously acquires the patient's first structured physiological data and first unstructured clinical judgment information in real time. This allows the system to capture new changes in the patient's condition. Based on this real-time data, the system can redetermine the patient's first baseline risk level and the possible first physiological system threat type, and further calculate the corresponding first deterioration trend score, thereby quantifying the degree of deterioration. When the deterioration trend score exceeds a preset first deterioration trend alarm threshold, and the first unstructured clinical judgment information is confirmed as the first clinical judgment confirmation information for the corresponding first physiological system threat type, it indicates that the patient's condition has indeed significantly deteriorated or a new emergency has occurred. At this time, the system will upgrade and correct the patient's first baseline risk level to a higher risk level B.

[0167] Furthermore, this solution introduces a priority ranking table for physiological system threat types to determine the priority of currently identified physiological system threat types compared to previously determined ones. This mechanism enables the system to intelligently identify more urgent threats. When the priority of a newly identified primary physiological system threat type is higher than that of the existing ones, the system will immediately release the medical resources previously allocated to the patient based on risk level A, and reallocate relevant medical resources to the patient based on the upgraded risk level B for the new, more urgent primary physiological system threat type.

[0168] This dynamic adjustment and prioritization mechanism ensures that the allocation of medical resources is no longer static or one-off, but can be flexibly adjusted according to the real-time evolution of the patient's condition and the urgency of the threat. This effectively complements the resource allocation based on initial assessment in the basic plan, ensuring that even if the patient's condition changes rapidly or a new high-priority threat emerges at the emergency scene, the system can respond promptly, avoiding unreasonable resource allocation due to information lag or rigid judgment, thereby improving emergency response efficiency and the timeliness of patient treatment.

[0169] Through the aforementioned technical solution, this application can capture the dynamic changes in a patient's condition in real time and promptly adjust the patient's risk level based on the trend of deterioration and clinical judgment information. More importantly, by introducing a priority ranking table of physiological system threat types, this application can determine the urgency of different threat types and immediately release existing resources and reallocate more suitable medical resources when a higher priority threat is identified. This effectively solves the problem of unreasonable allocation of medical resources caused by dynamic changes in the condition and differences in priority in existing technologies, ensuring that medical resources can be prioritized for the most urgent patients and the most critical treatment links, thereby improving the rationality and efficiency of medical resource allocation at the emergency scene and avoiding delays in patient treatment due to information lag or rigid judgment.

[0170] This application further proposes that, after the step of determining the priority of the physiological system threat type and the first physiological system threat type, the following method is also included:

[0171] When the priority of the physiological system threat type is higher than that of the first physiological system threat type, the procedure is to send a treatment notification to the relevant medical personnel carrying the first physiological system threat type as risk level B.

[0172] The process of prioritizing physiological system threats, specifically the first physiological system threat, involves ranking the various potential physiological system threats to the patient based on pre-defined medical urgency or severity assessment criteria. This can be done by querying and comparing a pre-defined priority ranking table of physiological system threats. The aim is to determine which threat requires priority treatment or attention. Sending a notification to relevant medical personnel indicating a risk level B for the first physiological system threat involves using information systems or communication devices to transmit information about the patient's first physiological system threat (risk level B) to the medical staff responsible for the patient's treatment in the form of a warning or reminder. This can be achieved through SMS, instant messaging, pop-up notifications in the hospital information system (HIS) or electronic medical record system (EMR), or voice broadcasts. The purpose is to ensure that medical personnel receive timely information about the patient's potential risks in order to comprehensively assess the condition and take appropriate treatment measures.

[0173] This application's solution proactively triggers an information transmission mechanism when the system identifies multiple physiological system threats to a patient and determines, based on a pre-defined priority ranking table, that one type of physiological system threat is more important than another primary physiological system threat. Because the physiological system threat type is determined to have a higher priority, the system prioritizes resource allocation and treatment processes related to that high-priority threat. However, to avoid overlooking information about other potential risks to the patient, this solution further ensures that even if the primary physiological system threat type is determined to have a lower priority, its relevant information, especially the crucial information that its risk level is a higher risk level B, will still be promptly sent to the medical personnel responsible for the patient's treatment. This design allows medical personnel to have a comprehensive understanding of the patient's condition, focusing not only on the current highest priority threat but also understanding and preparing to address other risks that, while having a slightly lower priority, still require attention. In this way, this solution compensates for the information blind spots that may occur in traditional systems when multiple threats coexist, ensuring the comprehensiveness and timeliness of medical decisions, thereby avoiding treatment delays caused by information asymmetry.

[0174] Through the aforementioned technical solution, even if the priority of a physiological system threat type is higher than that of the first physiological system threat type, the system can proactively send a treatment notification to relevant medical personnel carrying the risk level B of the first physiological system threat type. This ensures that medical personnel can promptly and comprehensively understand the various potential risks to the patient, especially those threats with relatively lower priority but still requiring attention. Therefore, medical personnel can comprehensively consider all aspects of the patient's condition, avoiding insufficient attention to the patient's condition due to information omissions, thereby effectively preventing delays in treatment and improving the timeliness and comprehensiveness of patient care.

[0175] See Figure 2 This application further proposes an abdominal trauma detection system, comprising: an acquisition module 1, a determination module 2, a calculation module 3, a judgment module 4, a correction module 5, and an allocation module 6. The acquisition module 1 is used to acquire in real time structured physiological data, accident type information, and unstructured clinical judgment information of patients who have suffered abdominal trauma before hospitalization.

[0176] Module 2 is used to determine the type of threat to the patient's physiological system and the corresponding basic risk level based on the initial structured physiological data and accident type information in the structured physiological data;

[0177] Calculation module 3 is used to calculate the disease deterioration trend score of the corresponding physiological system threat type of the patient based on structured physiological data;

[0178] Module 4 is used to determine whether the disease deterioration trend score exceeds the preset deterioration trend alarm threshold, and to determine whether the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type.

[0179] The correction module 5 is used to raise and correct the patient's basic risk level to a higher risk level A when the disease deterioration trend score exceeds the preset deterioration trend alarm threshold and the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type.

[0180] Allocation module 6 is used to reallocate relevant medical resources to patients based on a higher risk level, A.

[0181] The system comprises the following modules: Acquisition Module 1, which is responsible for data collection and input, and can be a data interface unit, sensor interface unit, or information receiving and processing unit. Its purpose is to provide comprehensive raw data for subsequent risk assessment. Determination Module 2, which is responsible for preliminary risk assessment and classification, and can be a rule engine, decision tree model, or preset logic processor. Its purpose is to quickly establish a preliminary risk profile of the patient based on initial information. Calculation Module 3, which is responsible for dynamic trend analysis, and can be a data analysis engine, algorithm executor, or trend prediction model. Its purpose is to quantify the trend of changes in the patient's condition over time. Judgment Module 4, which is responsible for condition triggering and logical judgment, and can be a condition judge, threshold comparator, or pattern recognizer. Its purpose is to identify specific situations that require an increase in risk level. Correction Module 5, which is responsible for dynamic adjustment of risk level, and can be a risk level updater, state machine controller, or priority adjuster. Its purpose is to ensure that the patient's risk level reflects changes in their condition in real time. Allocation Module 6, which is responsible for medical resource scheduling and optimization, and can be a resource scheduler, queue management system, or notification distributor. Its purpose is to rationally allocate medical resources based on the patient's latest risk level.

[0182] This application's solution, through a modular system design, achieves dynamic assessment and improved accuracy of risk level judgment for patients with abdominal trauma, as well as intelligent allocation of medical resources. Specifically, the acquisition module 1 first collects the patient's structured physiological data, accident type information, and unstructured clinical judgment information in real time, laying the data foundation for the entire assessment process. Based on this initial data, the determination module 2 can quickly identify the type of physiological system threat to the patient and set a basic risk level, thereby providing a preliminary assessment of the condition. On this basis, the calculation module 3 continuously analyzes the structured physiological data and quantifies the deterioration trend score of the patient's condition, enabling the system to capture subtle and continuous changes in the condition, compensating for the shortcomings of static assessment. Subsequently, the judgment module 4 performs dual verification of the deterioration trend score and unstructured clinical judgment information, ensuring that the risk level is only modified when the condition has indeed deteriorated and is clinically confirmed. Once the conditions are met, the modification module 5 raises the patient's basic risk level to a higher level, ensuring that critically ill patients receive priority attention. Ultimately, allocation module 6, based on the revised high-risk level, reallocates and distributes critical medical resources to the patient, such as prioritizing examination equipment or notifying medical personnel, thereby ensuring that the patient receives treatment commensurate with their condition at critical moments. This systematic collaborative work enables this application to effectively integrate multi-source information and dynamically respond to changes in the patient's condition, avoiding treatment delays caused by data processing lags or rigid resource allocation in existing methods, thus improving the efficiency of pre-hospital emergency care and the success rate of patient treatment.

[0183] In one specific implementation, this application is implemented as follows: The acquisition module 1 can be a data acquisition and transmission unit integrated into a pre-hospital emergency vehicle or a handheld device of emergency personnel. It receives, in real time, structured physiological data from physiological monitoring devices worn by the patient (e.g., wearable blood pressure monitors, heart rate monitors) via a wireless communication interface, and receives accident type information and unstructured clinical judgment information entered by emergency personnel via a tablet or voice input device, such as the emergency personnel's subjective description after abdominal palpation. This data is then packaged and transmitted to a remote medical data processing center via cellular network or satellite communication. The determination module 2 can be a software service deployed on a server in the medical data processing center. It contains a preset rule engine that, based on the received initial structured physiological data (e.g., initial blood pressure and heart rate measurements) and accident type information (e.g., traffic accidents, falls from heights), quickly determines the types of physiological system threats the patient may face (e.g., circulatory system threats, digestive system threats) and the corresponding basic risk level by consulting a predefined risk assessment table or decision tree model. The calculation module 3 can be an independent data analysis service that continuously receives and processes the structured physiological data stream transmitted by the acquisition module 1. For example, by performing time-series analysis on continuous vital sign data, it calculates the slope of changes in systolic blood pressure and heart rate, thereby deriving a disease deterioration trend score for the corresponding physiological system threat type of the patient. The judgment module 4 can be a logical judgment unit that receives the disease deterioration trend score output by the calculation module 3 and compares it with a preset deterioration trend alarm threshold. Simultaneously, it performs semantic analysis or keyword matching on the unstructured clinical judgment information received by the acquisition module 1 to determine whether it is clinical judgment confirmation information for the corresponding physiological system threat type. The correction module 5 can be a risk level management service. When both conditions of the judgment module 4 (deterioration trend score exceeding the threshold and clinical judgment information being confirmed) are simultaneously met, this service automatically upgrades and corrects the patient's basic risk level in the system to a higher risk level A. The allocation module 6 can be a resource scheduling and notification system that automatically triggers the reallocation process of medical resources based on the higher risk level A updated by the correction module 5. For example, it can send an instruction to the imaging diagnostic system of the hospital's emergency department to schedule a CT scan in advance, and send an urgent notification with the patient's high risk level and priority examination authority to the mobile terminals of relevant medical staff to ensure that the patient can quickly obtain the diagnosis and treatment he / she needs.

[0184] Through the above technical solution, this application provides an abdominal trauma detection system. This system can acquire and effectively integrate the patient's structured physiological data, accident type information, and unstructured clinical judgment information in real time and comprehensively. By dynamically calculating the severity trend score and combining it with clinical judgment information for double verification, the system improves the accuracy and dynamically corrects the assessment of the patient's risk level, avoiding the lag in risk assessment caused by relying solely on static numerical thresholds in existing methods. Furthermore, based on the corrected high-risk level, the system can intelligently and quickly reallocate key medical resources for the patient, such as prioritizing examination equipment and sending urgent notifications, thereby solving the problem of doctors' urgent resource requests based on clinical experience being rejected by the system's rigid rules. This improves the efficiency and accuracy of pre-hospital emergency information processing, ensures the rational and timely allocation of medical resources, and thus buys more time for the treatment of abdominal trauma patients, increasing the success rate of treatment.

[0185] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An abdominal trauma detection method, characterized by, The method comprises the following steps: obtaining structured physiological data, accident type information and unstructured clinical judgment information of a patient suffering from abdominal trauma in real time; determining a physiological system threat type and a corresponding basic risk level of the patient according to initial structured physiological data in the structured physiological data and the accident type information; calculating a condition deterioration trend score of the patient for the corresponding physiological system threat type according to the structured physiological data; judging whether the condition deterioration trend score exceeds a preset deterioration trend alarm threshold and whether the unstructured clinical judgment information is clinical judgment confirmation information for the corresponding physiological system threat type; if yes, modifying the basic risk level of the patient to a higher risk level A; redistributing relevant medical resources for the patient according to the higher risk level A.

2. The method of claim 1, wherein, The structured physiological data is patient vital sign data carrying a collection timestamp; The step of calculating the condition deterioration trend score of the patient for the corresponding physiological system threat type according to the structured physiological data specifically comprises the following steps: classifying the patient vital sign data, and calculating a change slope of each type of vital sign data according to the collection timestamp carried by the vital sign data; when the change slope of a certain type of vital sign data is greater than or equal to a preset warning slope threshold, calculating a deterioration trend score of the corresponding vital sign type according to the size and duration of the change slope; accumulating the deterioration trend scores of all types of vital signs to obtain the condition deterioration trend score of the patient for the corresponding physiological system threat type.

3. The method of claim 1, wherein, The method further comprises the following steps: pre-setting a verification keyword database, wherein a list of verification keywords for each physiological system threat type recognized by the medical field is maintained in the verification keyword database; The unstructured clinical judgment information is subjective judgment information of a patient's condition made by pre-hospital emergency personnel according to experience; The step of judging whether the unstructured clinical judgment information is clinical judgment confirmation information for the corresponding physiological system threat type specifically comprises the following steps: finding a list of verification keywords matching the corresponding physiological system threat type in the verification keyword database; judging whether the subjective judgment information contains at least one verification keyword in the matched list of verification keywords; if yes, generating clinical judgment confirmation information for the corresponding physiological system threat type according to the verification keyword.

4. The method of claim 1, wherein, The step of determining a physiological system threat type and a corresponding basic risk level of the patient according to initial structured physiological data in the structured physiological data and the accident type information specifically comprises the following steps: determining a physiological system threat type of the patient and calculating a basic risk score value of the patient according to the initial structured physiological data in the structured physiological data and the accident type information; determining a basic risk level of the patient according to the basic risk score value; The step of modifying the basic risk level of the patient to a higher risk level A specifically comprises the following steps: calculating a new risk total score value of the patient according to the basic risk score value, the condition deterioration trend score and a preset weight coefficient; comparing the new total risk score with a risk threshold corresponding to a risk level of a corresponding physiological system threat type; when the new total risk score is greater than a risk threshold corresponding to a highest risk level A; then the patient's risk level is upgraded from a basic risk level to the highest risk level A.

5. The method of claim 1, wherein, the step of re-allocating relevant medical resources to the patient according to the higher risk level A specifically includes: determining a required detection device for the patient according to the revised risk level A; determining a time period for the required detection device for the patient to use; allocating the time period for the required detection device to the patient; sending a patient rescue urgent notification carrying the time period for the use of the required detection device to relevant medical staff.

6. The method of claim 1, wherein, the structured physiological data is: a systolic blood pressure value and a heart rate value of the patient carrying a collection timestamp; the step of calculating a disease deterioration trend score of a corresponding physiological system threat type of the patient according to the structured physiological data specifically includes: calculating an interval length of collection time of adjacent systolic blood pressure values and adjacent heart rate values according to the collection timestamp carried; when an interval length is greater than or equal to a preset time warning threshold, calculating a systolic blood pressure change rate of a corresponding adjacent systolic blood pressure value and a heart rate change rate of a corresponding adjacent heart rate value; calculating a heart rate-blood pressure response imbalance index according to the systolic blood pressure change rate and the heart rate change rate; when the heart rate-blood pressure response imbalance index is higher than a preset physiological compensation reference imbalance threshold, calculating a systolic blood pressure change rate correction value according to the physiological compensation reference imbalance threshold and the heart rate-blood pressure response imbalance index; calculating a blood pressure deterioration trend score according to the systolic blood pressure change rate correction value and the interval length; calculating a heart rate deterioration trend score according to the heart rate change rate and the interval length; accumulating the blood pressure deterioration trend score and the heart rate deterioration trend score to obtain the disease deterioration trend score of the corresponding physiological system threat type of the patient.

7. The method of claim 6, wherein, the step of calculating a systolic blood pressure change rate correction value according to the physiological compensation reference imbalance threshold and the heart rate-blood pressure response imbalance index specifically includes: generating a multiplier for calculating the systolic blood pressure change rate correction value according to a preset multiplier calculation rule, the physiological compensation reference imbalance threshold, and the heart rate-blood pressure response imbalance index; multiplying the absolute value of the systolic blood pressure change rate by the multiplier to obtain the systolic blood pressure change rate correction value.

8. The method of detecting an abdominal trauma according to any one of claims 1 to 7, characterized in that, the step of acquiring the structured physiological data, the accident type information, and the unstructured clinical judgment information of the patient suffering from abdominal trauma in real time further includes: acquiring first structured physiological data and first unstructured clinical judgment information of the patient in real time; determining a first basic risk level of the patient and a first physiological system threat type of the patient according to initial first structured physiological data in the first structured physiological data and the accident type information; calculating a first disease deterioration trend score of a corresponding first physiological system threat type of the patient according to the first structured physiological data; determining whether the first deterioration trend score exceeds a preset first deterioration trend alarm threshold, and determining whether the first unstructured clinical judgment information is first clinical judgment confirmation information of a corresponding first physiological system threat type; if so, performing an upgrade correction on the first basic risk level of the patient, and correcting the first basic risk level of the patient to a higher risk level B; determining the priority of the physiological system threat type and the first physiological system threat type according to a preset physiological system threat type priority ranking table; when the priority of the first physiological system threat type is higher than that of the physiological system threat type, the step of reallocating relevant medical resources to the patient according to the higher risk level A further comprises: releasing the relevant medical resources reallocated to the patient according to the risk level A; allocating relevant medical resources to the patient according to the risk level B for the first physiological system threat type.

9. The method of claim 8, wherein, the step of determining the priority of the physiological system threat type and the first physiological system threat type further comprises: when the priority of the physiological system threat type is higher than that of the first physiological system threat type, sending a rescue notification carrying the first physiological system threat type with the risk level B to relevant medical staff.

10. An abdominal trauma detection system characterized by, comprises: an acquisition module, configured to acquire structured physiological data, accident type information, and unstructured clinical judgment information of a patient suffering from abdominal trauma in a pre-hospital stage in real time; a determination module, configured to determine a physiological system threat type and a corresponding basic risk level of the patient according to initial structured physiological data in the structured physiological data and the accident type information; a calculation module, configured to calculate a deterioration trend score of the corresponding physiological system threat type of the patient according to the structured physiological data; a judgment module, configured to determine whether the deterioration trend score exceeds a preset deterioration trend alarm threshold, and determine whether the unstructured clinical judgment information is clinical judgment confirmation information of the corresponding physiological system threat type; a correction module, configured to correct the basic risk level of the patient to a higher risk level A when the deterioration trend score exceeds the preset deterioration trend alarm threshold, and the unstructured clinical judgment information is the clinical judgment confirmation information of the corresponding physiological system threat type; a distribution module, configured to reallocate relevant medical resources to the patient according to the higher risk level A.

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