CN117334290A - Critical disease evidence-based nursing system - Google Patents
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Abstract
The application discloses critical illness evidence-based care system, the system includes: the data acquisition module is used for acquiring nursing problems of critical patients in the emergency treatment and clinical treatment process and establishing a sample information database; the preprocessing module is used for marking and standardizing treatment and establishing a classification standard information base for critical illness evidence-based nursing; the cognition model module is used for calculating a classification standard information base of critical illness evidence-based nursing and a conditional probability value of actual information and data of the critical illness patient in the first aid and clinical treatment process based on a conditional random field model; and the evidence-based nursing module is used for carrying out cognition and evidence-based nursing on the critical patients by using the corresponding schemes in the classification standard information base of the critical patient evidence-based nursing. The critical illness evidence-based nursing system can improve nursing quality and effectively reduce the incidence rate of complications.
Description
Technical Field
The application relates to the field of monitoring management, in particular to a critical illness evidence-based nursing system.
Background
For critical patients, nursing staff is especially required to know the treatment and nursing methods of various critical diseases, and is familiar with the technical operation of various critical care and master the correct use methods of various modern monitoring and treatment equipment. For example, cerebral infarction is ischemic stroke, a common disease of the elderly, and a very common disease in daily life. The pathogenesis is caused by various factors, and the pathological manifestations are mainly avascular ischemic necrosis. After the patient has developed, the internal cerebral arteries may be occluded, stenosed or ruptured, possibly leading to acute cerebral circulatory disorders. The clinical treatment mode is determined according to the illness state of a patient, and the operation treatment is a treatment mode commonly applied clinically, and due to more complications of the traditional operation, the operation still has certain complications, and the occurrence of related complications is reduced by means of effective nursing.
The related complications in clinical care cannot be well avoided in the related technology, and the negative emotion of critical patients cannot be relieved.
In view of the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
The primary objective of the present application is to provide a critical care system for evidence-based treatment of critical illness, so as to solve the problems in the related art.
In order to achieve the above object, according to one aspect of the present application, there is provided a critical illness evidence-based care system
A critical illness evidence-based care system according to the present application includes:
the data acquisition module is used for acquiring nursing problems of critical patients in the emergency treatment and clinical treatment process and establishing a sample information database;
the data acquisition module is further used for establishing a set of critical sign-based nursing evidence and sign-based nursing schemes corresponding to the nursing problems;
the pretreatment module is used for marking and standardizing the nursing problems of the critical patients in the first aid and clinical treatment process and the collection of critical evidence-based nursing evidence and evidence-based nursing schemes corresponding to the nursing problems, and establishing a classification standard information base of critical evidence-based nursing;
the cognition model module is used for calculating a classification standard information base of critical illness evidence-based nursing and a conditional probability value of actual information and data of the critical illness patient in the first aid and clinical treatment process based on a conditional random field model;
and the evidence-based nursing module is used for determining the conditional probability value conforming to the conditional probability threshold based on the set conditional probability threshold and carrying out the cognition and evidence-based nursing on the critical patient by using the corresponding scheme in the classification standard information base of the critical patient evidence-based nursing.
Further, the cognition model module is further used for determining key semantic classes in sentences through named entity recognition on the actual information of the critically ill patient in the emergency treatment and clinical treatment process based on a conditional random field model;
and calculating the similarity between the key semantic class and the keywords in the dictionary obtained in advance by carrying out accurate or fuzzy matching on the result of the key semantic class, selecting the keywords with the similarity meeting the condition to replace the key semantic class, and carrying out class marking.
Further, the conditional random field model is based on a Markov random field.
Further, the cognitive model module is further configured to
The classification standard information of critical illness evidence-based nursing is used as an input variable X, and various problems actually occurring in critical illness patients in the emergency treatment process are output as an output variable Y through a conditional probability model P (Y|X) based on a Markov random field;
wherein, in the conditional probability model P (y|x), Y is an output variable representing a marker sequence; x is an input variable representing the observation sequence to be annotated.
Further, the evidence-based care module is also used for
Setting a conditional probability threshold, and classifying and similarity matching calculation is carried out on an actual condition parameter subset of the preset critical patient in the emergency treatment and clinical treatment processes and the critical evidence-based care cognitive model;
when the conditional probability value does not meet the conditional probability threshold, reselecting the conditional probability value;
when the conditional probability value meets a conditional probability threshold value, comparing with the conditional probability threshold value;
and when the conditional probability value meets the conditional probability threshold, generating a corresponding evidence-based nursing cognitive report and an evidence-based nursing scheme according to the classification standard information base.
Further, the preprocessing module is also used for
Setting n parameter subsets in a classification standard information base of the problems acquired by critical patients in the first aid and clinical treatment processes and critical evidence-based nursing;
and establishing a classification standard information base for critical illness evidence-based nursing based on a clustering center model, a value calculation model and matrix updating operation through the n parameter subsets.
Further, the preprocessing module further includes:
a cluster center calculating unit for calculating c cluster centers;
a value calculation unit for calculating a value of the value;
and the U matrix updating unit is used for calculating a new U matrix and inputting the new U matrix into the clustering center calculating unit.
Further, the data acquisition module is also used for acquiring nursing problems of critical patients in emergency treatment, clinical treatment process and postoperative complications.
Further, the evidence-based care module is also used for
And generating a corresponding evidence-based care cognitive report and a evidence-based care scheme according to the classification standard information base of critical evidence-based care.
The data acquisition module is further used for
And establishing a set of critical sign-based nursing evidence and sign-based nursing schemes corresponding to the nursing problems through clinical medical literature data and combining clinical nursing experience.
In the critical illness evidence-based nursing system in the embodiment of the application, a data acquisition module is used for acquiring nursing problems of critical illness patients in the emergency treatment and clinical treatment processes and establishing a sample information database;
the data acquisition module is further used for establishing a set of critical sign-based nursing evidence and sign-based nursing schemes corresponding to the nursing problems; the pretreatment module is used for marking and standardizing the nursing problems of the critical patients in the first aid and clinical treatment process and the collection of critical evidence-based nursing evidence and evidence-based nursing schemes corresponding to the nursing problems, and establishing a classification standard information base of critical evidence-based nursing; the cognition model module is used for calculating a classification standard information base of critical illness evidence-based nursing and a conditional probability value of actual information and data of the critical illness patient in the first aid and clinical treatment process based on a conditional random field model; and the evidence-based nursing module is used for determining the conditional probability value conforming to the conditional probability threshold based on the set conditional probability threshold and carrying out the cognition and evidence-based nursing on the critical patient by using the corresponding scheme in the classification standard information base of the critical patient evidence-based nursing. The method realizes the improvement of critical nursing quality and provides an effective nursing mode technical effect, thereby solving the technical problems that related complications in clinical nursing cannot be well avoided and negative emotion of critical patients cannot be relieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to provide a further understanding of the application with regard to the other features, objects and advantages of the application. The drawings of the illustrative embodiments of the present application and their descriptions are for the purpose of illustrating the present application and are not to be construed as unduly limiting the present application. In the drawings:
FIG. 1 is a schematic diagram of a critical care system architecture according to an embodiment of the present application;
fig. 2 is a schematic structural view of a pretreatment module in a critical care system according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the present application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal" and the like indicate an azimuth or a positional relationship based on that shown in the drawings. These terms are used primarily to better describe the present application and its embodiments and are not intended to limit the indicated device, element or component to a particular orientation or to be constructed and operated in a particular orientation.
Also, some of the terms described above may be used to indicate other meanings in addition to orientation or positional relationships, for example, the term "upper" may also be used to indicate some sort of attachment or connection in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "configured," "provided," "connected," "coupled," and "sleeved" are to be construed broadly. For example, it may be a fixed connection, a removable connection, or a unitary construction; may be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements, or components. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
As shown in fig. 1, a schematic structural diagram of a critical care system according to an embodiment of the present application specifically includes: the data acquisition module 110 is used for acquiring nursing problems of critical patients in the emergency treatment and clinical treatment processes and establishing a sample information database; the data acquisition module 110 is further configured to establish a set of critical evidence-based care evidence and evidence-based care plan corresponding to the care problem; the preprocessing module 120 is configured to label and normalize a nursing problem of the critical patient in a first-aid and clinical treatment process and a set of critical evidence-based nursing evidence and a evidence-based nursing scheme corresponding to the nursing problem, and establish a classification standard information base of critical evidence-based nursing; the cognition model module 130 is used for calculating a classification standard information base of critical illness evidence-based nursing and a conditional probability value of actual information and data of the critical illness patient in the emergency treatment and clinical treatment process based on a conditional random field model; the evidence-based care module 140 is configured to determine, based on a set conditional probability threshold, the conditional probability value that meets the conditional probability threshold, and perform cognitive and evidence-based care on the critical patient using a corresponding scheme in the classification standard information base of critical care. In specific implementation, the data acquisition module 110 acquires nursing problems of critical patients in the process of emergency treatment and clinical treatment, and establishes a sample information database, namely, the acquisition module 110 is mainly used for acquiring various nursing problems of critical patients in the process of emergency treatment and clinical treatment, and the sample information and the database of critical clinical nursing problems are formed through summarization; meanwhile, based on clinical medical literature data and clinical nursing experience, a corresponding critical illness evidence-based nursing evidence and evidence-based nursing scheme set can be formed. The preprocessing module 120 marks and standardizes the nursing problem of the critical patient in the process of emergency treatment and clinical treatment and the collection of critical evidence-based nursing evidence and evidence-based nursing scheme corresponding to the nursing problem, and establishes a classification standard information base of critical evidence-based nursing, and the preprocessing module 120 mainly marks and standardizes various problems found by the critical patient in the process of emergency treatment and clinical treatment and the corresponding critical evidence-based nursing evidence and scheme, and establishes a classification standard information base of various problems found by the critical patient in the process of emergency treatment and clinical treatment and critical evidence-based nursing. The cognitive model module 130 calculates the conditional probability value between the classification standard information base of critical illness evidence-based nursing and the actual condition information and data of the critical illness patient in the emergency treatment and clinical treatment process by establishing a conditional random field model and acquiring the actual condition information and data of the critical illness patient in the emergency treatment and clinical treatment process. The evidence-based care module 140 classifies and similarity matches and calculates the actual condition parameter subset of the preset critical patient in the emergency treatment and clinical treatment process and the critical evidence-based care cognitive model by setting a conditional probability threshold, so as to obtain classification results and similarity probability and realize the solution of critical evidence-based care cognition and evidence-based care. When the conditional probability value does not meet the conditional probability threshold, the conditional probability value is reselected.
In addition, the critical sign-based care system sets a conditional probability threshold, compares the conditional probability value with the conditional probability threshold, and generates a corresponding sign-based care cognitive report and a sign-based care plan according to the classification standard information base when the conditional probability value meets the conditional probability threshold.
It will be appreciated that evidence-based care is a type of evidence-based care that combines scientific research with rigorous, discreet and clinical experience and considers the desires of the patient. Theoretical evidence is the basis of evidence-based care. The core idea of evidence-based medical theory is to apply the best evidence available in a discreet, definitive and intelligent way to make decisions for the individual patient's medical activity.
Nursing of common critical diseases, including cardiopulmonary resuscitation and visceral failure nursing; shock and wound care; acute myocardial infarction, hemoptysis, severe bronchial asthma, upper gastrointestinal hemorrhage, nail Kang Wei, diabetic ketoacidosis, anesthesia, postoperative care of transplantation and various wounds, etc. According to the critical illness evidence-based nursing system, an evidence-based nursing cognitive model is established according to experiences of nursing staff, specific conditions of patients and clinical medical literature data in the first aid and clinical treatment process, and the effectiveness of nursing operation is ensured, the nursing quality is improved, and the occurrence rate of complications is effectively reduced through scientific and feasible nursing measures and schemes.
As a preference in this embodiment, the cognitive model module is further configured to determine a key semantic class in a sentence by performing named entity recognition on actual information of the critically ill patient occurring in a first-aid and clinical treatment process based on a conditional random field model; and calculating the similarity between the key semantic class and the keywords in the dictionary obtained in advance by carrying out accurate or fuzzy matching on the result of the key semantic class, selecting the keywords with the similarity meeting the condition to replace the key semantic class, and carrying out class marking.
The conditional random field model (conditional random fields, abbreviated as CRF, or CRFs), which is a discriminant probability model, is a type of random field and is commonly used for labeling or analyzing sequence data, such as natural language text or biological sequences. The conditional random field is a conditional probability distribution model P (y|x) representing a markov random field for a given set of input random variables X and another set of output random variables Y, that is to say CRF is characterized by the assumption that the output random variables constitute the markov random field.
As a preference in this embodiment, the conditional random field model is based on a markov random field.
In specific implementation, a critical illness evidence-based care cognitive recognition and judgment model can be established by using a conditional random field model, namely, key semantic classes in sentences are found by recognizing named entities of the characteristic data and the information; and carrying out accurate matching on the key semantic class, carrying out fuzzy matching when the accurate matching fails, calculating the similarity between the key semantic class and the key words in the dictionary, selecting the key words with larger similarity to replace the key semantic class, and carrying out class labeling. Preferably, the conditional random field is a conditional probability distribution model P (y|x) representing a markov random field for a given set of input random variables X and another set of output random variables Y, that is to say a conditional random field featuring a hypothetical output random variable constituting the markov random field.
As a preferred embodiment of the present embodiment, the cognitive model module is further configured to output, by using classification standard information of critical care and evidence-based care as an input variable X and using a conditional probability model P (y|x) based on a markov random field, various problems actually occurring in a critical patient during emergency treatment and clinical treatment as an output variable Y; wherein, in the conditional probability model P (y|x), Y is an output variable representing a marker sequence; x is an input variable representing the observation sequence to be annotated.
The conditional random field is a serialization labeling algorithm that receives an input sequence such as x= (X) 1 ,X 2 ,...,X n ) And outputs the target sequence y= (Y) 1 ,Y 2 ,...,Y n ). The input sequence X is called observatings and Y is called states. Let X and Y be random variables, P (y|x) be the conditional probability distribution of Y given X, if the random variable Y constitutes a markov random field represented by the undirected graph g= (w, v), namely: p (Y) v |X,Y w ,w≠v)＝P(Y v |X,Y w If v) is true for any node v, the conditional probability distribution P (y|x) is called a conditional random field.
In particular, in the embodiment of the present application, it is assumed that X (classification standard information of critical care) is known, and all that is required is to match Y (various problems found in emergency treatment and clinical treatment of a critical patient), for example, X may represent a variable sequence formed by each word appearing in a sentence, and Y is a part-of-speech label corresponding to each word. Y in the conditional probability model P (Y|X) is an output variable and represents a marker sequence; x is the input variable representing the observation sequence that needs to be annotated.
As a preferred embodiment of the present embodiment, the evidence-based care module is further configured to set a conditional probability threshold, and classify and calculate a similarity match between the actual condition parameter subset of the preset critical patient in the emergency and clinical treatment process and the critical evidence-based care cognitive model; when the conditional probability value does not meet the conditional probability threshold, reselecting the conditional probability value; when the conditional probability value meets a conditional probability threshold value, comparing with the conditional probability threshold value; and when the conditional probability value meets the conditional probability threshold, generating a corresponding evidence-based nursing cognitive report and an evidence-based nursing scheme according to the classification standard information base.
The evidence-based nursing module of the system classifies and similarity matches and calculates the actual condition parameter subset of the preset critical patient in the first aid and clinical treatment process and the critical evidence-based nursing cognitive model, so that classification results and similarity probability are obtained, and the solution of the critical evidence-based nursing and the solution of the evidence-based nursing is realized. When the conditional probability value does not meet the conditional probability threshold, the conditional probability value is reselected.
As a preferred embodiment, the preprocessing module is further configured to set n parameter subsets in a classification standard information base of problem and critical disease evidence-based care acquired by a critical patient in a first aid and clinical treatment process; and establishing a classification standard information base for critical illness evidence-based nursing based on a clustering center model, a value calculation model and matrix updating operation through the n parameter subsets.
As a preference in this embodiment, the preprocessing module further includes: a cluster center calculation unit 1201 for calculating c cluster centers; a value calculation unit 1202 for calculating a value of a function; the U matrix updating unit 1203 calculates a new U matrix, and inputs the new U matrix to the cluster center calculating unit.
As shown in fig. 2, the preprocessing module is used for labeling and standardizing various problems found by critical patients in the process of emergency treatment and clinical treatment and corresponding critical evidence-based nursing evidence and schemes, and establishing a classification standard information base of various problems found by critical patients in the process of emergency treatment and clinical treatment and critical evidence-based nursing.
a. Setting n parameter subsets, namely feature vectors xj (j=1, 2, …, n), in a classification standard information base of various problems found by critical patients in the process of first aid and clinical treatment and critical evidence-based nursing, initializing a membership matrix U by using random numbers with values between 0 and 1 to enable elements uij to meet the formulaIs a constraint in (a);
b. cluster center calculation unit: by means of a beltCalculating c cluster centers ci, i=1, …, c; m is a hyper-parameter representing ambiguity;
c. a value calculation unit: according toCalculating a cost function value, wherein J represents a cost function, and dij= |ci-xj|is the Euclidean distance between an ith cluster center ci and a jth data point xj; if the value of the cost function is less than the preset threshold, the algorithm stops;
b.U matrix updating unit: by usingAnd calculating a new U matrix, and inputting the new U matrix into a clustering center calculating unit. As a preferred embodiment, the data acquisition module is further configured to acquire care problems occurring in emergency treatment, clinical treatment procedures and postoperative complications for critically ill patients.
The data acquisition module acquires nursing problems in critical emergency and clinical treatment processes and also acquires nursing problems in postoperative complications.
As a preferred mode in this embodiment, the evidence-based care module is further configured to generate a corresponding evidence-based care cognitive report and an evidence-based care plan according to the classification standard information base of critical evidence-based care.
And comparing the conditional probability value with a conditional probability threshold in the evidence-based care module, and generating a corresponding evidence-based care cognitive report and an evidence-based care scheme according to the classification standard information base when the conditional probability value meets the conditional probability threshold.
As a preferred embodiment, the data acquisition module is further configured to establish, by combining clinical medical literature data with clinical care experience, a set of critical care evidence and evidence-based care plan corresponding to the care problem.
Based on clinical medical literature data and clinical nursing experience, a corresponding critical illness evidence-based nursing evidence and evidence-based nursing scheme set is formed. Such as cerebral infarction care, (1) psychological education. Communicating with infarcted patients, knowing the psychological state of critical patients in time, guiding the cerebral infarcted patients to relieve psychological pressure, eliminating thought concern, and improving compliance of the cerebral infarcted patients; (2) dietary education. Guiding cerebral infarction patients to eat food which is easy to digest, high in heat and high in protein, increasing the resistance of the cerebral infarction patients, and positively supplementing calcium and protein and various trace elements; (3) disease education. Issuing a health education manual, carefully explaining the related knowledge of cerebral infarction, a treatment method and a self-care method, answering the questions of cerebral infarction patients, and eliminating the concerns of cerebral infarction patients; (4) life health knowledge propaganda and education. Enhancing night ward examination, closely monitoring vital signs of patients, and making diet guidance and complication care for the patients; (5) early rehabilitation training: when the cerebral infarction patient is in the convalescence, the cerebral infarction patient is guided to perform early rehabilitation training, such as walking, going up and down stairs and other daily activities, so as to promote early rehabilitation training and the like.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A critical care system, the system comprising:
the data acquisition module is used for acquiring nursing problems of critical patients in the emergency treatment and clinical treatment process and establishing a sample information database;
the data acquisition module is further used for establishing a set of critical sign-based nursing evidence and sign-based nursing schemes corresponding to the nursing problems;
the pretreatment module is used for marking and standardizing the nursing problems of the critical patients in the first aid and clinical treatment process and the collection of critical evidence-based nursing evidence and evidence-based nursing schemes corresponding to the nursing problems, and establishing a classification standard information base of critical evidence-based nursing;
the cognition model module is used for calculating a classification standard information base of critical illness evidence-based nursing and a conditional probability value of actual information and data of the critical illness patient in the first aid and clinical treatment process based on a conditional random field model;
and the evidence-based nursing module is used for determining the conditional probability value conforming to the conditional probability threshold based on the set conditional probability threshold and carrying out the cognition and evidence-based nursing on the critical patient by using the corresponding scheme in the classification standard information base of the critical patient evidence-based nursing.
2. The critical care system according to claim 1, wherein the cognitive model module is further configured to determine key semantic classes in sentences by named entity recognition of actual information of the critical patient occurring during emergency and clinical treatment based on a conditional random field model;
and calculating the similarity between the key semantic class and the keywords in the dictionary obtained in advance by carrying out accurate or fuzzy matching on the result of the key semantic class, selecting the keywords with the similarity meeting the condition to replace the key semantic class, and carrying out class marking.
3. The critical illness evidence-based care system of claim 2, wherein the conditional random field model is based on markov random fields.
4. The critical care system according to claim 2, wherein the cognitive model module is further configured to
The classification standard information of critical illness evidence-based nursing is used as an input variable X, and various problems actually occurring in critical illness patients in the emergency treatment process are output as an output variable Y through a conditional probability model P (Y|X) based on a Markov random field;
wherein, in the conditional probability model P (y|x), Y is an output variable representing a marker sequence; x is an input variable representing the observation sequence to be annotated.
5. The critical care system of claim 1, wherein the evidence-based care module is further configured to
Setting a conditional probability threshold, and classifying and similarity matching calculation is carried out on an actual condition parameter subset of the preset critical patient in the emergency treatment and clinical treatment processes and the critical evidence-based care cognitive model;
when the conditional probability value does not meet the conditional probability threshold, reselecting the conditional probability value;
when the conditional probability value meets a conditional probability threshold value, comparing with the conditional probability threshold value;
and when the conditional probability value meets the conditional probability threshold, generating a corresponding evidence-based nursing cognitive report and an evidence-based nursing scheme according to the classification standard information base.
6. The critical care system of claim 1, wherein the pre-processing module is further configured to
Setting n parameter subsets in a classification standard information base of the problems acquired by critical patients in the first aid and clinical treatment processes and critical evidence-based nursing;
and establishing a classification standard information base for critical illness evidence-based nursing based on a clustering center model, a value calculation model and matrix updating operation through the n parameter subsets.
7. The critical care system of claim 6, wherein the pre-processing module further comprises:
a cluster center calculating unit for calculating c cluster centers;
a value calculation unit for calculating a value of the value;
and the U matrix updating unit is used for calculating a new U matrix and inputting the new U matrix into the clustering center calculating unit.
8. The critical care system of claim 1, wherein the data acquisition module is further configured to acquire care problems for critical patients during emergency, clinical treatment, and post-operative complications.
9. The critical care system of claim 1, wherein the evidence-based care module is further configured to
And generating a corresponding evidence-based care cognitive report and a evidence-based care scheme according to the classification standard information base of critical evidence-based care.
10. The critical care system of claim 1, wherein the data acquisition module is further configured to
And establishing a set of critical sign-based nursing evidence and sign-based nursing schemes corresponding to the nursing problems through clinical medical literature data and combining clinical nursing experience.
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| CN117936100B (en) * | 2024-03-21 | 2024-05-31 | 深圳达实旗云健康科技有限公司 | Critical illness early warning method based on medical big data and related device |
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