CN118173283A - Emergency emergency condition analysis method, device, equipment and medium - Google Patents
Emergency emergency condition analysis method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of medical data analysis, in particular to a method, a device, equipment and a medium for analyzing the illness state of emergency treatment, wherein the method comprises the following steps: obtaining complaint information of a target patient; determining a preliminary diagnosis based on the complaint information; based on the preliminary diagnosis, medical image examination is opened, and an image report is obtained; determining the cause of the illness of the target patient based on the complaint information, the preliminary diagnosis and the image report; searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for a target patient; constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme; deducing an image of disease occurrence cause, drug resistance and external environmental factors of a target patient based on the PPI network; thereby deducing the condition change condition of the target patient, and rapidly assisting doctors in making diagnosis reports in the emergency treatment process, so that the condition is rapidly and effectively controlled.
Description
Technical Field
The invention relates to the technical field of medical data analysis, in particular to a method, a device, equipment and a medium for analyzing emergency conditions.
Background
The medical image data can provide a more accurate and more visual disease diagnosis basis for doctors, so that when a patient is subjected to medical diagnosis, the accurate diagnosis is often carried out by means of medical image reports. Currently available medical imaging reports include X-ray films, CT scans, MRI, and the like.
However, in emergency treatment, since a doctor cannot accurately deliver a diagnosis result in a short time, treatment is often delayed due to poor diagnosis efficiency.
Therefore, how to improve the diagnosis efficiency in emergency treatment is a technical problem to be solved.
Disclosure of Invention
In view of the foregoing, the present invention provides a method, apparatus, device and medium for emergency condition analysis that overcomes or at least partially solves the foregoing problems.
In a first aspect, the present invention provides a method for analyzing an emergency condition, comprising:
Obtaining complaint information of a target patient;
Determining a preliminary diagnosis based on the complaint information;
Based on the preliminary diagnosis, medical image examination is conducted, and an image report is obtained;
determining a cause of the illness of the target patient based on the complaint information, the preliminary diagnosis and the image report;
Searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for the target patient;
Constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme;
Deducing influence of disease occurrence cause, drug resistance and external environmental factors of the target patient based on the PPI network;
Deducing the condition change condition of the target patient based on the disease cause of the target patient, the drug resistance of the drug and the influence of external environment factors.
Further, the determining a cause of the disease of the target patient based on the complaint information, the preliminary diagnosis, and the image report includes:
Acquiring historical diagnosis and treatment data of a historical patient, wherein the historical diagnosis and treatment data comprises historical complaint information, historical preliminary diagnosis, a historical image report and a historical diagnosis result of the historical patient;
constructing a disease cause prediction model based on the historical diagnosis and treatment data of the historical patient;
and determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis, the image report and the disease cause prediction model.
Further, the determining a cause of the disease of the target patient based on the complaint information, the preliminary diagnosis, and the image report includes:
And carrying out fuzzy matching query in the history medical record database by taking the combination of the main judgment basis and the auxiliary judgment basis as query conditions to obtain diagnosis and treatment condition data similar to the target patient, wherein the main judgment basis comprises: the primary diagnosis and the image report, and the auxiliary judgment basis comprises the complaint information;
Sequencing the diagnosis and treatment condition data according to the priority to obtain a sequencing result;
and determining the disease cause of the target patient based on the sequencing result.
Further, the constructing the PPI network based on the disease cause, the treatment scheme and the recommended medication scheme includes:
Based on the disease cause, the treatment scheme and the recommended medication scheme, a PPI network is constructed by adopting a GO analysis method and a KEGG analysis method.
Further, the deriving the condition change of the target patient based on the disease origin of the target patient, the drug resistance of the drug and the influence of external environmental factors includes:
Analyzing the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors by using a Cox regression analysis method, and determining the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors on the survival rate of the target patient;
and screening by adopting a LASSO algorithm based on the influence of disease occurrence cause of the target patient, drug resistance of the drug and external environment factors on the survival rate of the target patient, and deducing the condition change condition of the target patient.
Further, after deriving the condition change of the target patient based on the influence of the disease origin, drug resistance and external environmental factors of the target patient, the method further comprises:
based on the condition change of the target patient and the image report, a complication is determined.
Further, after the determining of the complication based on the condition change of the target patient and the image report, the method further comprises:
control intervention is performed for a preset period of time prior to the onset of the complications.
In a second aspect, the present invention also provides a device for analyzing an emergency condition, including:
The acquisition module is used for acquiring the complaint information of the target patient;
The first determining module is used for determining preliminary diagnosis based on the complaint information;
the obtaining module is used for opening medical image examination based on the preliminary diagnosis to obtain an image report;
the second determining module is used for determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis and the image report;
the third determining module is used for searching a historical medical record database based on the disease cause and determining a treatment scheme and a recommended medication scheme for the target patient;
the construction module is used for constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme;
a first deriving module, configured to derive an influence of a disease cause, a drug resistance, and an external environmental factor of the target patient based on the PPI network;
The second deducing module is used for deducing the condition change condition of the target patient based on the influence of the disease initiation cause, the drug resistance of the target patient and external environment factors.
In a third aspect, the invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of the first aspect when executing the program.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method described in the first aspect.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
The invention provides a method for analyzing emergency conditions, which comprises the following steps: obtaining complaint information of a target patient; determining a preliminary diagnosis based on the complaint information; based on the preliminary diagnosis, medical image examination is opened, and an image report is obtained; determining the cause of the illness of the target patient based on the complaint information, the preliminary diagnosis and the image report; searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for a target patient; constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme; deducing an image of disease occurrence cause, drug resistance and external environmental factors of a target patient based on the PPI network; deducing the condition change condition of the target patient based on the disease cause of the target patient, the drug resistance of the drug and the images of external environment factors, and rapidly assisting a doctor in making a diagnosis report in the emergency treatment process so that the condition can be effectively controlled.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also throughout the drawings, like reference numerals are used to designate like parts. In the drawings:
FIG. 1 is a flow chart showing a method for analyzing an emergency condition according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of a PPI network for a suspected PE patient in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram showing the probability of survival at low risk, medium risk, and high risk in an embodiment of the present invention;
FIG. 4 shows a schematic of risk indices at low risk, medium risk, and high risk in an embodiment of the invention;
FIG. 5 is a schematic diagram showing the condition change of four target patients according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a device for analyzing emergency conditions in an embodiment of the invention;
Fig. 7 is a schematic structural diagram of a computer device for implementing a method for analyzing an emergency condition according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Embodiment one:
an embodiment of the present invention provides a method for analyzing an emergency condition, as shown in fig. 1, including:
S101, acquiring complaint information of a target patient;
S102, determining preliminary diagnosis based on the complaint information;
S103, based on the preliminary diagnosis, medical image examination is opened, and an image report is obtained;
S104, determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis and the image report;
S105, searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for a target patient;
S106, constructing a PPI network based on the pathogenesis, the treatment scheme and the recommended medication scheme;
S107, deducing influence of disease occurrence cause, drug resistance and external environmental factors of a target patient based on a PPI network;
s108, deducing the condition change condition of the target patient based on the disease cause of the target patient, the drug resistance of the drug and the influence of external environment factors.
First, the scheme of the present invention will be briefly described:
In emergency situations, doctors cannot accurately make diagnosis results according to limited medical data. Therefore, the patient's complaint is necessary information, and then, the doctor needs to make a preliminary diagnosis according to the complaint information, thereby opening a more detailed examination, in particular, opening a medical image examination, and thus obtaining an image report, and at this time, the patient's information is increased and needs to be comprehensively considered, so that the complaint information, the preliminary diagnosis and the image report are combined, thereby determining the cause of the patient's illness, and then, the cause of the illness is compared with the information in the history database, thereby determining the treatment plan and recommended medication plan for the target patient; then constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme; deducing the influence of disease initiation cause, drug resistance and external environmental factors of a target patient from the PPI network based on the PPI network; deducing the condition change condition of the target patient based on the disease occurrence cause of the target patient, the drug resistance of the drug and the influence of external environment factors. The disease cause is rapidly determined, and then the corresponding treatment scheme and the recommended medication scheme are rapidly found according to the disease cause, so that the condition change condition of the target patient is determined, a doctor can be rapidly assisted in making a diagnosis report, and the condition can be effectively controlled.
The steps are described in detail below.
S101-S103, wherein the three steps are mainly preliminary diagnosis made when a patient receives emergency treatment first aid, and the current symptoms of the target patient are inquired to obtain complaint information of the target patient, wherein the complaint information comprises duration of symptoms, symptoms of the symptoms and corresponding degrees.
The complaint information of the target patient can be acquired through voice, or can be selectively recorded on the equipment with the assistance of medical staff, and the method is not limited in this regard.
Next, a preliminary diagnosis, which may be a simple medical process, may be made based on the complaint information, resulting in a diagnosis. For example, according to the symptoms of thirst, polydipsia, dry mouth and tongue, listlessness, quadriplegia and the like of the patient, the patient is determined to belong to any one of the following suspected medical records: 1. suspected hyperglycemia, 2, suspected diabetes, 3, suspected hyperthyroidism.
To further determine the condition of the patient, an image report is then obtained based on the preliminary diagnosis, by opening a medical image examination, which may be specifically an X-ray film, a CT scan or an MRI (magnetic resonance). For example, shooting chest radiography, further judging that symptoms such as pneumonia exist.
After the image report is obtained, S104 is executed to determine the cause of the disease of the target patient based on the complaint information, the preliminary diagnosis, and the image report.
Here, the cause of the disease may be determined in two ways, one is to predict the cause of the disease by constructing a disease cause prediction model, and the other is to determine the cause of the disease by fuzzy query of a database.
These two methods are described in detail below.
Firstly, acquiring historical diagnosis and treatment data of a historical patient, wherein the historical diagnosis and treatment data comprises historical complaint information, historical preliminary diagnosis, a historical image report and a historical diagnosis result of the historical patient;
Constructing a disease cause prediction model based on historical diagnosis and treatment data of a historical patient;
and determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis, the image report and the disease cause prediction model.
Specifically, by collecting historical diagnosis and treatment data of a historical patient, the historical diagnosis and treatment data comprises historical complaint information, historical preliminary diagnosis, historical image report and diagnosis results of the historical patient. The diagnosis results in the historical diagnosis and treatment data can be data obtained by doctors through multiparty research.
And inputting the historical diagnosis and treatment data into a prediction model for training, thereby obtaining a disease cause prediction model. The prediction model may employ CNN or KNN, which is not limited herein. Wherein, the diagnosis result is the diagnosis result of the disease cause. Through training, a disease cause prediction model is obtained, and the disease cause prediction model can predict the disease cause of a patient. Then, the complaint information, the preliminary diagnosis and the image report of the target patient are input into the disease cause prediction model to obtain the disease cause of the target patient.
The disease is caused by being used as a reference basis for diagnosis of doctors, and is convenient for the doctors to refer to.
Secondly, performing fuzzy matching query in a history medical record database by taking the combination of main judgment bases and auxiliary judgment bases as query conditions to obtain diagnosis and treatment condition data similar to a target patient, wherein the main judgment bases comprise: preliminary diagnosis and image report, auxiliary judgment basis comprises main complaint information; sequencing diagnosis and treatment condition data according to the priority to obtain a sequencing result; based on the sequencing result, the cause of the disease of the target patient is determined.
In the method, a fuzzy matching query method is mainly adopted, data in a historical medical record database is assumed to be put into an array, a primary diagnosis and an image report are used as main judgment basis, main complaint information is used as auxiliary judgment basis to carry out fuzzy matching query on similar medical records, and the primary diagnosis, the image report and the main complaint information are respectively compared with the array data in the historical medical record database in sequence in a specific query process. The method is characterized in that the preliminary diagnosis to be compared, or the image report to be compared, or the complaint information to be compared is used as a target value, and whether the target value is in an array is judged, so that n+1 possibilities exist for the algorithm, and the number of elements in the array to be inspected under the condition of n+1 is added up and divided by n+1, so that the number screened by fuzzy matching inquiry can be obtained.
For a complete matching, taking data a as an example, the data a refers to any one information data of preliminary diagnosis, image report and complaint information of a target patient, and an array formed by data in a history medical record database is [1,2,3], and then a complete matching process is a1, a2, a3. Then data a is actually very diverse and then is understood to be a fuzzy matching query over multiple cycles.
The specific matching process may be, but not limited to, comparing the ratio of the same number of words, comparing the same degree of meaning, extracting keywords, comparing the matching degree of keywords, and the like.
Next, according to the above-described cycle matching and the determination of the matching degree, diagnosis and treatment condition data similar to the target patient is output, which may be a plurality of sets of data. The data are ranked according to priority, specifically, the data with high matching degree are ranked in front, and the data with low matching degree are ranked in back. Thereby determining the disease cause of the target patient according to the sequencing result.
Of course, the cause of the disease is also used as a reference basis for diagnosis of doctors, so that the doctor can conveniently refer to the disease.
After determining the cause of the illness of the target patient, S105 is executed to search the history database based on the cause of the illness, determine the treatment regimen for the target patient and the recommended medication regimen.
Firstly, a doctor issues a final diagnosis result according to the disease cause, and then searches a historical medical record database based on the diagnosis result. The history medical record database is used for storing a large number of diagnosis and treatment schemes of the history patients, and the treatment scheme and recommended medication scheme for the target patients are determined by searching the history medical record database. Of course, data that is completely consistent with the condition of the target patient may not be found from the historical medical record database, and the treatment regimen and recommended regimen for the target patient determined herein may be the closest or most similar, and is not limited herein.
After determining the target patient treatment regimen and the recommended medication regimen, S106 is performed to build the PPI network based on the cause of the disease, the treatment regimen, and the recommended medication regimen.
The PPI network herein is specifically a graph of a relationship formed by interactions among the cause of the disease, the treatment regimen, and the recommended regimen.
Specifically, based on the cause of the disease, the treatment scheme and the recommended medication scheme, a PPI network is constructed by adopting a GO analysis method and a KEGG analysis method.
Where GO analysis is to learn which functions or processes are most pronounced under given experimental conditions by comparing the genome in genomic blood experimental data with functional annotations in the GO database, he helps researchers find biological functions, pathways or components that are relevant to the particular experimental conditions.
In the invention, the GO analysis method is adopted, and the cause of the disease is taken as a gene, so that the relation between the cause of the disease and the treatment scheme and the recommended medication is analyzed.
KEGG analysis is an information base integrating genome, chemical and system functional information, and is mainly used for revealing genetic and chemical blueprints of life phenomena, and KEGG analysis is mainly used for analyzing metabolic pathways in organisms, and a plurality of metabolic pathways and relations among the pathways are introduced by using graphs.
In the present invention, the KEGG assay is used to explore various effects of the treatment regimen and recommended medication regimen employed on the body.
Based on these two methods, a relational network graph, i.e. a PPI network, is thus constructed. Fig. 2 shows a schematic diagram of a PPI network for a suspected PE (pulmonary arterial embolism) patient.
For different patients, there are differences in the constructed PPI network due to the disease cause, the treatment regimen and the recommended medication. Moreover, there may be differences for the same concurrent reasons, the same treatment regimen, and recommended medication.
Next, S107 is performed to derive the disease cause, drug resistance and influence of external environmental factors of the target patient based on the PPI network.
The medical knowledge is combined with the PPI network to deduce the front cause and the back cause of the disease, and of course, the front cause and the back cause can have various conditions, then the data in the history diagnosis and treatment database are combined, and the improved symptoms and the aggravated or unchanged symptoms can be seen according to the factors such as the type, the duration and the like of the taken medicine, and the influence of the change of the surrounding environment on the disease condition is considered, so that the influence of the disease cause, the drug resistance and the external environment factors of the target patient can be deduced. The disease cause and drug resistance of the target patient and the influence of external environmental factors are finally deduced without the cooperation of doctors according to the current PPI network and the historical diagnosis and treatment data by the software program, and are used for providing reference basis for the doctors.
Next, S108 is performed to derive a condition change of the target patient based on the disease cause of the target patient, the drug resistance of the drug, and the influence of external environmental factors.
Specifically, a Cox regression analysis method is adopted to analyze the influence of disease occurrence cause, drug resistance and external environmental factors of a target patient, and the influence of the disease occurrence cause, drug resistance and external environmental factors of the target patient on the survival rate of the target patient is determined;
among them, the Cox regression analysis is a widely used statistical method in life analysis, which can analyze the influence of a plurality of factors on life time at the same time, and has no specific requirement on data distribution. Specifically, the rationale for Cox regression analysis is to assume the risk function of the patient:
Wherein h (t) is a risk function, h 0 (t) is a reference risk function, Is a variable,/>And as for regression coefficients corresponding to the variables, the influence of the research factors on the risk rate can be estimated through the risk function. Each study factor is herein referred to as the cause of disease in the target patient, the resistance to the drug, and the influence of external environmental factors. Wherein, fig. 3 shows the survival probability of low risk, medium risk and high risk, and fig. 4 shows the risk index of low risk, medium risk and high risk.
After determining the influence of disease cause of a target patient, drug resistance of a drug and external environment factors on the survival rate of the target patient, adopting LASSO algorithm to carry out screening so as to deduce the condition change condition of the target patient.
The LASSO algorithm is an algorithm for regression analysis, and can realize feature selection and regularization in regression analysis, so that the prediction accuracy and the interpretation of a model are improved. The algorithm is often used for processing a high-dimensional data set, so that the most important features can be effectively selected, the complexity of a model is reduced, and the generalization capability is improved.
By adopting the algorithm, key factors in the influence of disease occurrence causes of target patients, drug resistance of drugs and external environmental factors on the survival rate of the target patients are extracted, and minor factors and other unimportant factors are screened out, so that the trend of the condition change of the target patients is deduced according to the influence of the key factors. As shown in fig. 5. This fig. 5 shows four target patient conditions, including: direct exacerbation, acute exacerbation, smooth development, and slow development.
After deriving a trend of the condition change of the target patient, the disease progression in which the target patient is currently located can be determined from the trend. So that some measures are taken to improve at this stage.
Thus, after deriving the condition change of the target patient, it further comprises:
based on the condition change of the target patient and the image report, the complications are determined.
Specifically, determining the position of the current condition of the target patient in the condition change based on the condition change condition of the target patient and the image report;
acquiring historical diagnosis and treatment data of a historical patient;
And comparing the position of the current illness state of the target patient in the illness state change with the historical diagnosis and treatment data, and determining the complication.
Firstly, the image report can represent the current disease degree most, and then, the position of the current disease degree in the disease change is determined according to the current disease degree. And determining the complications of the patient in the later period similar to the current disease degree of the target patient by searching the historical diagnosis and treatment data of the historical patient, thereby determining the complications as the complications of the target patient. For example, late diabetic complications may occur in cerebral stroke, cerebral infarction, angina pectoris, coronary heart disease, myocardial infarction, myocardial ischemia, etc.
Finally, after determining the complications of the target patient, further comprising:
prior to the onset of complications, control intervention is performed for a preset period of time.
In the development process of the disease, effective periods capable of intervening in time exist, and the effective periods are effectively mastered, so that the disease condition can be controlled as early as possible, or the possibility of curing is achieved. For example, after the determination of diabetes, the patient is prompted to exercise by controlling the diet with low sugar, so as to perform control intervention, thereby preventing the deterioration of the condition.
Therefore, by adopting the scheme of the invention, the workload of medical staff can be reduced, the medical staff can be helped to diagnose rapidly, the medical service quality is improved, the illness state can be determined rapidly and accurately, and the patient can be better treated by medical treatment.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
The invention provides a method for analyzing emergency conditions, which comprises the following steps: obtaining complaint information of a target patient; determining a preliminary diagnosis based on the complaint information; based on the preliminary diagnosis, medical image examination is opened, and an image report is obtained; determining the cause of the illness of the target patient based on the complaint information, the preliminary diagnosis and the image report; searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for a target patient; constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme; deducing an image of disease occurrence cause, drug resistance and external environmental factors of a target patient based on the PPI network; deducing the condition change condition of the target patient based on the disease cause of the target patient, the drug resistance of the drug and the images of external environment factors, and rapidly assisting a doctor in making a diagnosis report in the emergency treatment process so that the condition can be effectively controlled.
Embodiment two:
based on the same inventive concept, the embodiment of the invention also provides a device for analyzing the illness state of emergency treatment, as shown in fig. 6, comprising:
An obtaining module 601, configured to obtain complaint information of a target patient;
a first determining module 602, configured to determine a preliminary diagnosis based on the complaint information;
An obtaining module 603, configured to set up a medical image examination based on the preliminary diagnosis, to obtain an image report;
A second determining module 604, configured to determine a cause of the disease of the target patient based on the complaint information, the preliminary diagnosis, and the image report;
A third determining module 605, configured to search a history database based on the cause of the disease, and determine a treatment plan and a recommended medication plan for the target patient;
A construction module 606, configured to construct a PPI network based on the disease cause, the treatment plan, and the recommended medication plan;
A first deriving module 607, configured to derive an influence of a disease cause, a drug resistance, and an external environmental factor of the target patient based on the PPI network;
A second deriving module 608, configured to derive a condition change of the target patient based on the disease cause of the target patient, the drug resistance of the drug, and the influence of external environmental factors.
In an alternative embodiment, the second determining module 604 is configured to:
Acquiring historical diagnosis and treatment data of a historical patient, wherein the historical diagnosis and treatment data comprises historical complaint information, historical preliminary diagnosis, a historical image report and a historical diagnosis result of the historical patient;
constructing a disease cause prediction model based on the historical diagnosis and treatment data of the historical patient;
and determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis, the image report and the disease cause prediction model.
In an alternative embodiment, the second determining module 604 is configured to:
And carrying out fuzzy matching query in the history medical record database by taking the combination of the main judgment basis and the auxiliary judgment basis as query conditions to obtain diagnosis and treatment condition data similar to the target patient, wherein the main judgment basis comprises: the primary diagnosis and the image report, and the auxiliary judgment basis comprises the complaint information;
Sequencing the diagnosis and treatment condition data according to the priority to obtain a sequencing result;
and determining the disease cause of the target patient based on the sequencing result.
In an alternative embodiment, the construction module 606 is configured to:
Based on the disease cause, the treatment scheme and the recommended medication scheme, a PPI network is constructed by adopting a GO analysis method and a KEGG analysis method.
In an alternative embodiment, the second deriving module 608 is configured to:
Analyzing the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors by using a Cox regression analysis method, and determining the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors on the survival rate of the target patient;
and screening by adopting a LASSO algorithm based on the influence of disease occurrence cause of the target patient, drug resistance of the drug and external environment factors on the survival rate of the target patient, and deducing the condition change condition of the target patient.
In an alternative embodiment, the method further comprises: a third determining module, configured to:
based on the condition change of the target patient and the image report, a complication is determined.
In an alternative embodiment, the method further comprises: an intervention module for:
control intervention is performed for a preset period of time prior to the onset of the complications.
Embodiment III:
Based on the same inventive concept, an embodiment of the present invention provides a computer device, as shown in fig. 7, including a memory 704, a processor 702, and a computer program stored in the memory 704 and capable of running on the processor 702, where the processor 702 executes the steps of the emergency treatment condition analysis method described above.
Where in FIG. 7a bus architecture (represented by bus 700), bus 700 may comprise any number of interconnected buses and bridges, with bus 700 linking together various circuits, including one or more processors, as represented by processor 702, and memory, as represented by memory 704. Bus 700 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 706 provides an interface between bus 700 and receiver 701 and transmitter 703. The receiver 701 and the transmitter 703 may be the same element, i.e. a transceiver, providing a unit for communicating with various other apparatus over a transmission medium. The processor 702 is responsible for managing the bus 700 and general processing, while the memory 704 may be used to store data used by the processor 702 in performing operations.
Embodiment four:
Based on the same inventive concept, an embodiment of the present invention provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the emergency condition analyzing method described above.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each embodiment. Rather, as each embodiment reflects, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in a specific implementation, any of the claimed embodiments may be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of an emergency medical device, computer apparatus, according to embodiments of the present invention. The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (10)
1. A method for analyzing an emergency condition, comprising:
Obtaining complaint information of a target patient;
Determining a preliminary diagnosis based on the complaint information;
Based on the preliminary diagnosis, medical image examination is conducted, and an image report is obtained;
determining a cause of the illness of the target patient based on the complaint information, the preliminary diagnosis and the image report;
Searching a historical medical record database based on the disease cause, and determining a treatment scheme and a recommended medication scheme for the target patient;
Constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme;
Deducing influence of disease occurrence cause, drug resistance and external environmental factors of the target patient based on the PPI network;
Deducing the condition change condition of the target patient based on the disease cause of the target patient, the drug resistance of the drug and the influence of external environment factors.
2. The method of claim 1, wherein the determining a cause of the disease for the target patient based on the complaint information, the preliminary diagnosis, and the image report comprises:
Acquiring historical diagnosis and treatment data of a historical patient, wherein the historical diagnosis and treatment data comprises historical complaint information, historical preliminary diagnosis, a historical image report and a historical diagnosis result of the historical patient;
constructing a disease cause prediction model based on the historical diagnosis and treatment data of the historical patient;
and determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis, the image report and the disease cause prediction model.
3. The method of claim 1, wherein the determining a cause of the disease for the target patient based on the complaint information, the preliminary diagnosis, and the image report comprises:
And carrying out fuzzy matching query in the history medical record database by taking the combination of the main judgment basis and the auxiliary judgment basis as query conditions to obtain diagnosis and treatment condition data similar to the target patient, wherein the main judgment basis comprises: the primary diagnosis and the image report, and the auxiliary judgment basis comprises the complaint information;
Sequencing the diagnosis and treatment condition data according to the priority to obtain a sequencing result;
and determining the disease cause of the target patient based on the sequencing result.
4. The method of claim 1, wherein the constructing the PPI network based on the cause of the disease, the treatment regimen, and the recommended medication regimen comprises:
Based on the disease cause, the treatment scheme and the recommended medication scheme, a PPI network is constructed by adopting a GO analysis method and a KEGG analysis method.
5. The method of claim 1, wherein deriving the change in condition of the target patient based on the predisposition to the disease, the resistance to the drug, and the influence of external environmental factors comprises:
Analyzing the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors by using a Cox regression analysis method, and determining the influence of the disease origin of the target patient, the drug resistance of the drug and the external environment factors on the survival rate of the target patient;
and screening by adopting a LASSO algorithm based on the influence of disease occurrence cause of the target patient, drug resistance of the drug and external environment factors on the survival rate of the target patient, and deducing the condition change condition of the target patient.
6. The method of claim 1, further comprising, after deriving the change in condition of the target patient based on the predisposition to the disease, the resistance to the drug, and the influence of external environmental factors:
based on the condition change of the target patient and the image report, a complication is determined.
7. The method of claim 6, further comprising, after said determining complications based on said patient condition change condition of said subject patient and said image report:
control intervention is performed for a preset period of time prior to the onset of the complications.
8. An emergency condition analyzing device, comprising:
The acquisition module is used for acquiring the complaint information of the target patient;
The first determining module is used for determining preliminary diagnosis based on the complaint information;
the obtaining module is used for opening medical image examination based on the preliminary diagnosis to obtain an image report;
The second determining module is used for determining the disease cause of the target patient based on the complaint information, the preliminary diagnosis and the image report;
the third determining module is used for searching a historical medical record database based on the disease cause and determining a treatment scheme and a recommended medication scheme for the target patient;
the construction module is used for constructing a PPI network based on the disease cause, the treatment scheme and the recommended medication scheme;
a first deriving module, configured to derive an influence of a disease cause, a drug resistance, and an external environmental factor of the target patient based on the PPI network;
The second deducing module is used for deducing the condition change condition of the target patient based on the influence of the disease initiation cause, the drug resistance of the target patient and external environment factors.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when the program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-7.
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