Nursing shift information association method and system based on dynamic knowledge graph

Abstract

The invention relates to a nursing shift information association method and system based on a dynamic knowledge graph, and belongs to the field of data processing. The method comprises the steps of integrating multi-dimensional data of a patient and outputting a situation matrix, the multi-dimensional data of the patient comprises a basic portrait of the patient, a dynamic index, a treatment process and a nursing focus, and the situation matrix comprises acute degree, vulnerability and intervention priority; acquiring and analyzing a nursing shift information text, and outputting a clinical event set with a corrected potential energy value based on the situation matrix and the nursing shift information text; carrying out direct effect projection and risk conduction modeling based on the clinical event set, and outputting an aging curve and a risk event set carrying a risk value; and constructing a dynamic knowledge graph based on the clinical event set, the timeliness curve and the risk event set. According to the invention, multi-source clinical data are integrated through the dynamic knowledge graph, and intelligent association of nursing shift information is realized.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a nursing handover information association method and system based on a dynamic knowledge graph. Background Art

[0002] Current clinical nursing handover systems generally rely on free text records and verbal handovers, which present significant flaws: information fragmentation leads to omissions of key risk points (e.g., a patient's refusal to turn over at night is recorded only as a behavioral event, unrelated to the patient's medical history and the subsequent risk of pressure ulcers); insufficient static data integration (vital signs, nursing record text, and risk assessment scales in electronic medical records are stored separately, lacking a dynamic association mechanism); and delayed warnings (e.g., wound infection relies on visual observation of exudate or redness, missing the golden window for intervention). In recent years, knowledge graphs and retrieval-enhanced generation technologies have been gradually applied in the field of clinical nursing handover. However, static knowledge graphs lack the ability to model time-series dynamics and cannot capture the trajectory of risk evolution. Therefore, a dynamic knowledge graph-based nursing handover information association method that can transform discrete nursing events into quantifiable causal chains is urgently needed. Summary of the Invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides a method and system for associating nursing handover information based on a dynamic knowledge graph.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A method for associating nursing handover information based on a dynamic knowledge graph, wherein the implementation of the method comprises the following steps: S1: Integrate multidimensional patient data and output a contextual matrix, wherein the multidimensional patient data includes a basic patient profile, dynamic indicators, treatment progress, and care focus; and the contextual matrix includes acuity, vulnerability, and intervention priority. S2: Obtaining and parsing a nursing handover information text, and outputting a clinical event set with a modified potential energy value based on the situation matrix and the nursing handover information text; S3: performing direct effect projection and risk conduction modeling based on the clinical event set, and outputting a time-effect curve and a risk event set carrying a risk value; S4: Construct a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

[0005] Preferably, the step S2 is specifically as follows: The nursing handover information text is parsed into the clinical event set, and the clinical event set is decomposed into a multidimensional structure, wherein the multidimensional structure includes nursing objects, behavioral actions, state transitions, timestamps, and clinical potential energy changes, and the clinical potential energy changes carry the modified potential energy value.

[0006] Preferably, the acquisition of the corrected potential energy value in step S2 is specifically as follows: Obtain the basic risk coefficient, behavior intensity coefficient, context information, time difference and department nursing rhythm, and calculate the clinical potential index, which is mathematically described as ,in, is the clinical potential index, is the basic risk factor, is the behavior intensity coefficient, is the amount of context information, is the time difference, Provides the rhythm of nursing care for the department; Based on the clinical potential energy index and the situation matrix, a modified potential energy value is obtained, which is mathematically described as follows: ,in, To correct the potential energy value, 、 and is the weight.

[0007] Preferably, the process of direct effect projection in step S3 is specifically as follows: Identify the operational event set related to medical operations in the clinical event set, and generate a time effect curve {(t1, effect1), (t2, effect2), (t3, effect3)...} based on the operational event set, where t1, t2... are time points, and effect1, effect2... are the operational effects at the corresponding time points.

[0008] Preferably, the risk transmission modeling process in step S3 is specifically as follows: Generate an S-shaped growth function based on the risk growth rate, mathematically described as ,in, is an S-shaped growth function, is the risk threshold time, k is the risk growth rate, and t is the current time point; Obtain the risk conversion coefficient, and obtain the risk value based on the risk conversion coefficient and the S-type growth function, which is mathematically described as ,in, is the risk value at time t, To correct the potential energy value, is the risk conversion coefficient, and the risk event set is obtained based on the risk value.

[0009] Preferably, the construction rules of the dynamic knowledge graph in step S4 are: S401: Set the clinical event as the event node, the risk event as the risk node, and the executed nursing operation as the intervention node. The event node weight of the event node is the modified potential energy value corresponding to the current time point, the risk node weight of the risk node is the risk value corresponding to the current time point, and the intervention node weight of the intervention node is the operation effect corresponding to the current time point. S402: Establishing node associations according to preset association types, wherein the association types include temporal association, causal association, risk transmission, and intervention response; S403: Starting from any node, traverse all associated paths to form the dynamic knowledge graph and obtain path weights; S404: Preset a path weight threshold, mark key paths based on the path weight and the path weight threshold, and generate a pop-up prompt.

[0010] Preferably, the path weight is obtained by obtaining the node distance, which is the distance in days between the node timestamp and the current time point, and obtaining the path weight according to the node distance and the node weight. The mathematical description of the path weight is: ,in, is the path weight, w is the node weight, d n is the node spacing, is the path half-life.

[0011] A nursing handover information association system based on a dynamic knowledge graph is used to implement the above-mentioned nursing handover information association method, including a situation matrix output module, a clinical event analysis module, a risk event analysis module and a graph construction module; The situation matrix output module is used to integrate multi-dimensional patient data and output a situation matrix, wherein the multi-dimensional patient data includes a basic patient profile, dynamic indicators, treatment progress, and nursing focus, and the situation matrix includes acuity, vulnerability, and intervention priority; The clinical event parsing module is used to obtain and parse the nursing handover information text, and output a clinical event set with a corrected potential energy value based on the situation matrix and the nursing handover information text; The risk event analysis module is used to perform direct effect projection and risk conduction modeling based on the clinical event set, and output a time effect curve and a risk event set carrying a risk value; The graph construction module is used to construct a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

[0012] The beneficial effects of the present invention are: (1) Integrate and associate various fragmented information in the nursing handover process through dynamic knowledge graphs to avoid missing key risk points in the process.

[0013] (2) Dynamically monitor and predict patient conditions through situational matrices, modified potential values, and risk values, so that medical staff can respond to patient needs in a timely manner.

[0014] (3) Mark key events through path weights to facilitate timely processing by medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of the steps of a nursing handover information association method based on a dynamic knowledge graph of the present invention. DETAILED DESCRIPTION

[0017] In order to better understand the present invention, various aspects of the present invention will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention and are not intended to limit the scope of the present invention in any way. Throughout the specification, the expression "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the terms "roughly", "approximately" and similar terms are used as terms to indicate approximate values, rather than as terms to indicate degree, and are intended to illustrate inherent deviations in measurements or calculated values ​​that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0018] It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open rather than closed expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention." And, the term "exemplary" is intended to refer to an example or illustration.

[0019] Unless otherwise defined, all terms used herein (including engineering and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this invention pertains. It should also be understood that, unless otherwise expressly stated herein, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense.

[0020] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] Example 1: See also Figure 1 , a nursing handover information association method based on dynamic knowledge graph, including: S1: Integrate multidimensional patient data and output a contextual matrix. The multidimensional patient data includes basic patient profiles, dynamic indicators, treatment progress, and nursing focus. The basic patient profile includes but is not limited to age, diagnosis, and allergy history (such as "68 years old, diabetes, penicillin allergy"). The dynamic indicators include but are not limited to vital sign trends in the past 24 hours (such as "blood sugar fluctuation range 4-7mmol / L"). The treatment progress includes but is not limited to the current treatment plan stage (such as "3rd day after surgery / wound healing period"). The nursing focus includes but is not limited to departmental key issues (such as "Endocrinology Department: blood sugar stability first"). The contextual matrix includes acuteness, vulnerability, and intervention priority. The acuteness is (current number of abnormal indicators / total number of indicators) × severity coefficient × 0.8, and the severity coefficient value range is an open interval (0-1.2). The vulnerability is min [1.5, (age coefficient + number of complications × 0.3) × treatment stage coefficient]. The intervention priority is nursing focus weight × min (1, recent event density / 6). The mathematical description of the contextual matrix is Example: A 68-year-old diabetic patient has a new wound infection and is accompanied by complications of kidney disease. He has elevated blood sugar, elevated body temperature, and increased white blood cell count. In the past 6 hours, the patient has experienced three abnormal indicator events. The patient's acuteness = (3 / 5) × 1.1 × 0.8 = 0.53 (three of the five indicators are abnormal, and they are relatively severe), vulnerability = (1.2 + 2 × 0.3) × 0.9 = 1.26 (age > 65, age coefficient 1.2, two complications of diabetes and kidney disease), intervention priority = 0.8 × min (1, 3 / 6) = 0.4 (blood sugar management weight 0.8, three abnormal indicator events in the past 6 hours). In summary, the patient's situational matrix CM = .

[0022] S2: Obtaining and parsing a nursing handover information text, and outputting a clinical event set with a modified potential energy value based on the situation matrix and the nursing handover information text; S3: performing direct effect projection and risk conduction modeling based on the clinical event set, and outputting a time-effect curve and a risk event set carrying a risk value; S4: Construct a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

[0023] In this embodiment, outputting a clinical event set with a modified potential energy value based on the situation matrix and the nursing handover information text can be specifically implemented by the following steps: The semantic parser is used to parse the nursing handover information text into the clinical event set, and the clinical event set is decomposed into a multi-dimensional structure, wherein the multi-dimensional structure includes nursing objects, behavioral actions, state transitions, timestamps, and clinical potential energy changes, wherein the clinical potential energy changes carry the modified potential energy value; Example: Part of the nursing handover information text is "The patient in bed 3 of the general ward refused to turn over (10:00), and the skin of the sacrum and coccyx became red (11:00)"; The nursing handover information text is parsed into a clinical event set = {Clinical event A: The patient in bed 3 of the general ward refused to turn over; Clinical event B: The skin of the sacrum and coccyx became red}, and each clinical event is decomposed into a four-dimensional structure: Clinical event A: {Nursing object: "Patient in bed 3 of the general ward", behavioral action: "Refuse", state transition: "Activity decreased", timestamp: "10:00", clinical potential energy change: =0.97"}; Clinical event B: {nursing object: "sacrum", behavioral action: "presentation", state transition: "skin changes from normal to red", timestamp: "11:00", clinical potential change: =0.88”}.

[0024] In this embodiment, the modified potential energy value is obtained as follows: S201: Obtain the basic risk coefficient, behavior intensity coefficient, context information, time difference and department nursing rhythm, and calculate the clinical potential index, which is mathematically described as ,in, is the clinical potential index, is the basic risk factor (from the medical knowledge base), is the behavioral intensity coefficient (the intensity coefficient of the patient's behavioral action), is the amount of contextual information (i.e., the number of associated symptoms), is the time difference (in hours, i.e. the time difference from the last relevant event), The nursing rhythm of the department (the unit is taken in hours, that is, the time interval for routine nursing / inspection in each department); S202: Obtain a modified potential energy value based on the clinical potential energy index and the situation matrix, which is mathematically described as ,in, To correct the potential energy value, 、 and is the weight.

[0025] In this embodiment, the purpose of direct effect projection is to quantify the immediate therapeutic effect of the nursing operation (such as the process of drug onset). The specific process is as follows: Identify the set of operation events related to medical operations in the clinical event set, and generate a time-effect curve {(t1, effect1), (t2, effect2), (t3, effect3)...} based on the operation event set, where t1, t2... are time points, and effect1, effect2... are the operation effects at the corresponding time points. Its mathematical description is ,in, is the operation intensity coefficient (the operation intensity coefficient is obtained from the drug dosage, for example: 20mg furosemide 1.0, 10mg furosemide is 0.5), is the effect function at time t (e.g., rapid-acting insulin =e^(-0.15t))、 The maximum effect benchmark value (e.g., hypoglycemic drugs =10 (blood sugar drops by 10mmol / L / h). If there is no medical intervention, the output is blank.

[0026] In this embodiment, the purpose of the risk transmission modeling is to predict the dynamic evolution trajectory of potential nursing risks (such as the trend of infection worsening). The specific process is as follows: Generate an S-shaped growth function based on the risk growth rate, mathematically described as ,in, is an S-shaped growth function, is the risk threshold time (the time point when the risk begins to accelerate, for example: wound infection = 24h, which is the starting point of the logarithmic phase of bacterial proliferation), k is the risk growth rate (obtained by disease severity classification, e.g., diabetic foot ulcer: mild k = 0.3, severe k = 0.6); The risk conversion coefficient is obtained by analyzing the hospital adverse event database (for example, if 100 cases of refusal to turn over are analyzed and the proportion of pressure sores occurring within 72 hours is 70%, then the risk conversion coefficient is 0.7). The risk value is obtained based on the risk conversion coefficient and the S-type growth function, which is mathematically described as follows: ,in, is the risk value at time t, is the risk conversion coefficient, It is an S-shaped growth function. Based on the risk value, the risk event set is obtained. The mathematical description is risk event set = {risk event a, ,Timestamp; risk event b, ,timestamp;…}( is the risk value set of risk event a in the entire time process, is the risk value set of risk event b in the entire time process); the risk events here are events that may be caused by nursing operations / patient behaviors, not events that have already occurred or will definitely occur. For example, a patient's refusal to turn over may lead to a pressure sore at some point in the future.

[0027] Example: Event A: {Nursing object: "Patient in bed 3 of general ward", behavioral action: "Refuse debridement", state transition: "Increased wound exudate", clinical potential change: =0.89", its direct effect projection is: no nursing operation, output is empty; its risk conduction model is: the risk conversion coefficient is 0.75, at this time, the risk value needs to be predicted after 24 hours and 36 hours respectively, and the risk growth rate k=0.5, then , .

[0028] In this embodiment, the construction rules of the dynamic knowledge graph in step S4 are: S401: Set the clinical event as the event node, the risk event as the risk node, and the executed nursing operation as the intervention node. The event node weight of the event node is the modified potential energy value corresponding to the current time point, the risk node weight of the risk node is the risk value corresponding to the current time point, and the intervention node weight of the intervention node is the operation effect corresponding to the current time point (i.e., the corresponding position of the time-effect curve); S402: Establish node associations based on preset association types, including temporal association (event A occurs before event B), causal association (event A directly causes event B), risk transmission (clinical event A triggers risk event B), and intervention response (nursing operation A targets risk event B). The establishment of node associations is specifically as follows: (1) Temporal association is specifically applicable to scenarios where events occur in chronological order but the causal relationship is unclear, such as: Node A: 10:00 The patient refuses to turn over, Node B: 14:00 The patient's skin becomes red, but it is not clear that the redness is caused by refusing to turn over, then nodes AB are temporally associated with the time nodes. (2) Causal association is specifically applicable to events with clear clinical causal relationships. The established rules are to match the clinical causal knowledge base (such as "the probability of hypoglycemia caused by not eating on time is 62%); meet the time sequence (cause first, effect later); and conform to the physiological and pathological mechanism. For example: Node A: At 8:00, the patient refused to take hypoglycemic drugs, Node B: At 12:00, the patient's blood sugar increased significantly. The clinical causal knowledge base query shows that the probability of a causal relationship is 62%, so nodes AB are causally associated. (3) Risk conduction is specifically applicable to scenarios where current events trigger future risks. The established rule is that node A is an actual event; node B is a risk event predicted by S3. For example: Node A: At 9:00, the patient refused wound care, Node B: The 24h infection risk is predicted to increase, so nodes AB are associated with risk conduction. (4) Intervention response is specifically applicable to scenarios where nursing measures target specific risks. The established rule is: Node A is a nursing intervention measure; Node B is a risk event in S3. For example: Node A: Debridement is performed at 11:00, Node B: The risk of wound infection within 24 hours is 0.45, so nodes AB are associated with intervention response; S403: Starting from any node, traverse all associated paths to form the dynamic knowledge graph and obtain path weights; The path weight is obtained by obtaining the node distance, which is the distance in days between the node timestamp and the current time point, and obtaining the path weight according to the node distance and the node weight. The node weight is the event node weight, risk node weight and intervention node weight. The mathematical description of the path weight is ,in, is the path weight, w is the node weight, d n is the node spacing, For the path half-life (acute problem =2 days, chronic problem = 7 days). You can now drag the timeline to view changes in path weight and node weight.

[0029] S404: Preset a path weight threshold, mark key paths based on the path weight and the path weight threshold, and generate a pop-up window prompt (for example: (If the value is greater than 1.5, it will be highlighted and sent to the nurse’s mobile phone immediately).

[0030] Example 2: A nursing handover information association system based on a dynamic knowledge graph, including a situation matrix output module, a clinical event analysis module, a risk event analysis module, and a graph construction module; The situation matrix output module is used to integrate multi-dimensional patient data and output a situation matrix, wherein the multi-dimensional patient data includes a basic patient profile, dynamic indicators, treatment progress, and nursing focus, and the situation matrix includes acuity, vulnerability, and intervention priority; The clinical event parsing module is used to obtain and parse the nursing handover information text, and output a clinical event set with a corrected potential energy value based on the situation matrix and the nursing handover information text; The risk event analysis module is used to perform direct effect projection and risk conduction modeling based on the clinical event set, and output a time effect curve and a risk event set carrying a risk value; The graph construction module is used to construct a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

[0031] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for associating nursing handover information based on dynamic knowledge graph, characterized in that: The implementation of the nursing handover information association method includes the following steps: Step S1: Integrate multidimensional patient data and output a context matrix, wherein the multidimensional patient data includes a basic patient profile, dynamic indicators, treatment progress, and nursing focus, and the context matrix includes acuity, vulnerability, and intervention priority; Step S2: Acquire and parse the nursing handover information text, and output a clinical event set with a modified potential energy value based on the situation matrix and the nursing handover information text; Step S3: performing direct effect projection and risk conduction modeling based on the clinical event set, and outputting a time-effect curve and a risk event set carrying a risk value; Step S4: constructing a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

2. The nursing handover information association method according to claim 1, characterized in that: The step S2 is specifically as follows: The nursing handover information text is parsed into the clinical event set, and the clinical event set is decomposed into a multidimensional structure, wherein the multidimensional structure includes nursing objects, behavioral actions, state transitions, timestamps, and clinical potential energy changes, and the clinical potential energy changes carry the modified potential energy value.

3. The nursing handover information association method according to claim 2, characterized in that: The acquisition of the modified potential energy value in step S2 is specifically as follows: Obtain the basic risk coefficient, behavior intensity coefficient, context information, time difference and department nursing rhythm, and calculate the clinical potential index, which is mathematically described as ,in, is the clinical potential index, is the basic risk factor, is the behavior intensity coefficient, is the amount of context information, is the time difference, Provides the rhythm of nursing care for the department; Based on the clinical potential energy index and the situation matrix, a modified potential energy value is obtained, which is mathematically described as follows: ,in, To correct the potential energy value, 、 and is the weight.

4. The nursing handover information association method according to claim 1, characterized in that: The specific process of the direct effect projection in step S3 is as follows: Identify the operational event set related to medical operations in the clinical event set, and generate a time effect curve {(t1, effect1), (t2, effect2), (t3, effect3)...} based on the operational event set, where t1, t2... are time points, and effect1, effect2... are the operational effects at the corresponding time points.

5. The nursing handover information association method according to claim 1, characterized in that: The specific process of risk transmission modeling in step S3 is as follows: Generate an S-shaped growth function based on the risk growth rate, mathematically described as ,in, is an S-shaped growth function, is the risk threshold time, k is the risk growth rate, and t is the current time point; Obtain the risk conversion coefficient, and obtain the risk value based on the risk conversion coefficient and the S-type growth function, which is mathematically described as ,in, is the risk value at time t, To correct the potential energy value, is the risk conversion coefficient, and the risk event set is obtained based on the risk value.

6. The nursing handover information association method according to claim 1, characterized in that: The construction rules of the dynamic knowledge graph in step S4 are: S401: Set the clinical event as the event node, the risk event as the risk node, and the executed nursing operation as the intervention node. The event node weight of the event node is the modified potential energy value corresponding to the current time point, the risk node weight of the risk node is the risk value corresponding to the current time point, and the intervention node weight of the intervention node is the operation effect corresponding to the current time point. S402: Establishing node associations according to preset association types, wherein the association types include temporal association, causal association, risk transmission, and intervention response; S403: Starting from any node, traverse all associated paths to form the dynamic knowledge graph and obtain path weights; S404: Preset a path weight threshold, mark key paths based on the path weight and the path weight threshold, and generate a pop-up prompt.

7. The nursing handover information association method according to claim 6, characterized in that: The path weight is obtained by obtaining the node distance, which is the distance in days between the node timestamp and the current time point, and obtaining the path weight according to the node distance and the node weight. The mathematical description of the path weight is: ,in, is the path weight, w is the node weight, d n is the node spacing, is the path half-life.

8. A nursing handover information association system based on dynamic knowledge graph, characterized in that: The system is used to execute the nursing handover information association method according to any one of claims 1 to 7, comprising a situation matrix output module, a clinical event analysis module, a risk event analysis module and a graph construction module; The situation matrix output module is used to integrate multi-dimensional patient data and output a situation matrix, wherein the multi-dimensional patient data includes a basic patient profile, dynamic indicators, treatment progress, and nursing focus, and the situation matrix includes acuity, vulnerability, and intervention priority; The clinical event parsing module is used to obtain and parse the nursing handover information text, and output a clinical event set with a corrected potential energy value based on the situation matrix and the nursing handover information text; The risk event analysis module is used to perform direct effect projection and risk conduction modeling based on the clinical event set, and output a time effect curve and a risk event set carrying a risk value; The graph construction module is used to construct a dynamic knowledge graph based on the clinical event set, the time effect curve and the risk event set.

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