CN112259219A - System, equipment and storage medium for predicting diseases based on upper gastrointestinal hemorrhage - Google Patents

System, equipment and storage medium for predicting diseases based on upper gastrointestinal hemorrhage Download PDF

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CN112259219A
CN112259219A CN202011059143.1A CN202011059143A CN112259219A CN 112259219 A CN112259219 A CN 112259219A CN 202011059143 A CN202011059143 A CN 202011059143A CN 112259219 A CN112259219 A CN 112259219A
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杜登斌
杜乐
杜小军
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Wuzheng Intelligent Technology Beijing Co ltd
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Abstract

The invention discloses a system, a device and a storage medium for predicting diseases based on upper gastrointestinal hemorrhage, wherein the system comprises: a data acquisition module: a case sample for extracting the above gastrointestinal hemorrhage from the electronic case library as a clinical first complaint symptom; a feature extraction module: the system is used for extracting symptom information of upper gastrointestinal hemorrhage, related biological index data and corresponding disease names in each case sample to form case sample feature vectors; a disease clustering module: clustering the characteristic vectors of the case samples by adopting a K-means clustering algorithm optimized by an improved bird swarm algorithm; a joint diagnosis module: the method is used for clustering cases to be diagnosed and establishing a combined diagnosis model based on symptom information of upper gastrointestinal hemorrhage and biological index data. The method carries out combined diagnosis and screening based on the symptom information and effective biological indexes of the gastrointestinal hemorrhage on the basis of improving the clustering algorithm, and can accurately predict the cause and the result of the upper gastrointestinal hemorrhage.

Description

System, equipment and storage medium for predicting diseases based on upper gastrointestinal hemorrhage
Technical Field
The invention relates to the field of disease auxiliary diagnosis equipment, in particular to a system, equipment and a storage medium for predicting diseases based on upper gastrointestinal hemorrhage.
Background
The upper gastrointestinal hemorrhage refers to the hemorrhage of esophagus, stomach, duodenum and upper jejunum, and is one of the clinically common emergencies, and clinically manifested as hematemesis, dark stool, anemia and the like. The most common reasons for upper gastrointestinal bleeding are peptic ulcer, erosive gastritis, portal hypertension due to liver cirrhosis, esophageal variceal bleeding, gastric cancer, and liver trauma. Clinical manifestations depend on the nature, location, amount and rate of bleeding lesions and are closely related to the general condition of the patient as it bleeds.
In the clinical work of upper gastrointestinal hemorrhage, the diagnosis of upper gastrointestinal hemorrhage mainly depends on clinical symptomatology, lacks objective and effective biological diagnosis indexes, and only depends on clinical diagnosis, so that the cause and result of the upper gastrointestinal hemorrhage are difficult to accurately predict.
Disclosure of Invention
In view of the above, the present invention provides a system, a device, and a storage medium for predicting diseases based on upper gastrointestinal hemorrhage, which are used to combine the symptom characteristic information of upper gastrointestinal hemorrhage with the objective index of effective biological index for joint diagnosis and screening, so as to accurately analyze the cause and result of upper gastrointestinal hemorrhage.
In a first aspect of the invention, a system for predicting a disease based on upper gastrointestinal bleeding, the system comprising:
a data acquisition module: a case sample for extracting the above gastrointestinal hemorrhage from the electronic case library as a clinical first complaint symptom;
a feature extraction module: the system is used for extracting symptom information of upper gastrointestinal hemorrhage, related biological index data and corresponding disease names in each case sample to form case sample feature vectors;
a disease clustering module: the clustering method is used for determining the clustering category and clustering the characteristic vectors of the case samples by adopting an improved K-means clustering algorithm optimized by a bird swarm algorithm;
a joint diagnosis module: the method is used for acquiring the cases to be diagnosed, clustering the cases to be diagnosed and establishing a combined diagnosis model of semantic similarity between symptom information of upper gastrointestinal hemorrhage between the cases to be diagnosed and the case samples in the cluster and Euclidean distance between biological index data.
Preferably, in the feature extraction module, feature items of upper gastrointestinal hemorrhage symptom information in each case sample are extracted through TF-IDF, and feature vectors of the upper gastrointestinal hemorrhage symptom information are established; the relevant biological index data comprises pulse, blood pressure, bowel sound, urine volume, liver function change data and fiberscope examination data, and the biological index data is normalized to form a biological index vector; the case sample feature vector is composed of a feature vector of symptom information of upper gastrointestinal hemorrhage and a biological index vector.
Preferably, the disease clustering module specifically includes:
an initialization unit: initializing a population, calculating a fitness value, and selecting an initialized global optimal value;
a population updating unit: generating a random number a between (0,1) and a constant P, when a > P, selecting foraging, otherwise keeping alert;
when a is larger than P, the foraging position is updated in a nonlinear weight adjustment mode, and the position updating formula is as follows:
Figure BDA0002711505430000021
Figure BDA0002711505430000022
Figure BDA0002711505430000023
wherein the content of the first and second substances,
Figure BDA0002711505430000024
respectively showing the position of the ith bird in the jth dimension space at the time t, C1And C2Respectively a perception coefficient and a social evolution coefficient, Pi,jFor the optimal position for the i-th bird to pass, Gi,jAnd the optimal position of the population is obtained.
When a < P, the bird group remains alert and the position update formula is:
Figure BDA0002711505430000025
Figure BDA0002711505430000031
Figure BDA0002711505430000032
wherein, a1、a2∈[0,2]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, epsilon is a constant, meanjIs the average fitness value of the population;
allowing the bird group to migrate periodically, generating a producer and an entrepressor, and updating the positions of the producer and the entrepressor;
an evaluation unit: calculating a new fitness value, updating a historical optimal value, returning to the population updating unit until a set iteration number is reached, and outputting an optimal position as a clustering center point.
Preferably, in the disease clustering module, the fitness function of the evaluation unit is the sum of the intra-class distances, that is
Figure BDA0002711505430000033
Where K is the number of cluster categories, d (X)i,Cj) For each data object X in class jiTo a corresponding cluster center point CjThe distance of (c).
Preferably, the combined diagnosis module specifically comprises:
a clustering unit: performing feature extraction on the acquired case to be diagnosed to obtain a case feature vector to be diagnosed, calculating Euclidean distances from the case feature vector to be diagnosed to each cluster central point, and selecting a cluster with the minimum Euclidean distance;
a calculation unit: calculating cosine similarity alpha between characteristic vectors of symptom information of upper gastrointestinal hemorrhage between a case to be diagnosed and case samples in the cluster; calculating the Euclidean distance beta of a biological index vector between a case to be diagnosed and a case sample in a cluster;
a judging unit: calculating the joint similarity s, s-w of each case sample1α+w2(1-β)，w1+w2And (1) taking the disease type corresponding to the case sample with the maximum joint similarity as the disease prediction result of the case to be diagnosed.
In a second aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, which are invoked by the processor to implement the system of the first aspect of the invention.
In a third aspect of the invention, a computer-readable storage medium is disclosed, which stores computer instructions for causing a computer to implement the system of the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) the improved bird swarm algorithm is combined with the K mean value algorithm to perform case clustering, the population position is updated in a nonlinear weight adjustment mode, the learning capacity of excellent individuals is enhanced in the iterative learning process, the population can be efficiently optimized in the foraging initial stage, and the convergence speed is increased; and disease category subdivision is carried out based on the aggregation result, so that the disease screening range can be reduced, and the disease prediction speed can be accelerated.
2) The invention combines the symptom information of upper gastrointestinal hemorrhage with the objective index of effective biological index, carries out combined diagnosis and screening by establishing a characteristic information semantic similarity calculation model and a diagnosis model constructed by biological cognitive index, can accurately predict the result of upper gastrointestinal hemorrhage, can analyze the cause of upper gastrointestinal hemorrhage based on the big data information of an electronic disease case library, and is simple and practical auxiliary diagnosis equipment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the system for predicting disease based on upper gastrointestinal hemorrhage according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present invention provides a system for predicting a disease based on upper gastrointestinal hemorrhage, which includes a data acquisition module 100, a feature extraction module 200, a disease clustering module 300, and a joint diagnosis module 400;
the data acquisition module 100: a case sample for extracting the above gastrointestinal hemorrhage from the electronic case library as a clinical first complaint symptom;
the diseases of upper gastrointestinal hemorrhage mainly include: 1) esophageal diseases including esophagitis, esophageal ulcer, esophageal tumor, esophageal cardia mucosa tear and physical/chemical injury; 2) gastric and duodenal diseases including peptic ulcer, esophageal and gastric variceal rupture, portal hypertension gastropathy, acute hemorrhagic erosive gastritis, gastric vascular abnormality (arteriovenous malformation), gastric cancer and other tumors, acute gastric dilatation, duodenitis and diverticulitis, diaphragmatic hernia, gastric torsion, ancylostomia, jejunal ulcer and anastomotic stomal ulcer after gastrointestinal anastomosis; 3) liver and gallbladder diseases including intrahepatic localized chronic infection, liver abscess, liver cancer, hepatic hemangioma rupture, and hepatic parenchymal rupture caused by trauma can cause intrahepatic biliary tract hemorrhage. But also damage to the bile duct itself; 4) diseases of organs or tissues adjacent to the upper gastrointestinal tract include pancreatic disease involving the duodenum; thoracic or abdominal aortic aneurysms break into the digestive tract; mediastinal tumors or abscesses break into the esophagus; 5) systemic diseases manifest bleeding in the gastrointestinal tract. Hematologic disorders including leukemia, aplastic anemia, hemophilia; vascular diseases; connective tissue disease and vasculitis; stress-related gastric mucosal injury; acute infectious diseases include epidemic hemorrhagic fever, leptospirosis; uremia, etc.
Different diseases have different characteristic information and biological index data. For example, the clinical symptom characteristic information of erosive gastritis generally includes upper abdominal pain, severe stress states such as anemia, syncope and hemorrhagic shock can appear when blood is repeatedly hematemesis, and abdominal distension, anorexia, upper abdominal fullness feeling, acid regurgitation, dark stool and the like are presented secondly; the normal or increased number of leukocytes and an increased proportion of neutrophils or lymphocytes are observed in the blood routine. Combining blood of a patient infected by the whole body to culture bacteria which can be positive; and (3) checking hidden red blood cells or hemoglobin in the excrement, wherein the positive excrement occult blood test is an index of the gastrointestinal hemorrhage. The electronic case library comprises various diseases corresponding to hemorrhage in the digestive tract, and the disease etiology and result prediction analysis is carried out according to big data in the electronic case library.
The feature extraction module 200: the system is used for extracting symptom information of upper gastrointestinal hemorrhage, related biological index data and corresponding disease names in each case sample to form case sample feature vectors;
the upper gastrointestinal hemorrhage is usually acute and severe, and is usually analyzed mainly according to the change conditions of the medical history, the hematemesis and bloody stool conditions, the pulse, the blood pressure, the bowel sound, the urine volume, the liver function and the like, and is usually combined with fiberscope examination, selective arteriography and the like. Here, the clinical manifestations of upper gastrointestinal hemorrhage mainly include hematemesis and dark stool, and the concrete manifestations depend on the bleeding speed, amount of bleeding, time of blood staying in the digestive tract and bleeding part, and other symptoms also can cause hemorrhagic shock, and sometimes can also cause symptoms such as anemia and fever; the biological index data herein mainly include: 1) and (5) blood convention. Early in bleeding, there was no significant change in red blood cell count, hemoglobin amount, and hematocrit. After 3-4 hours, the value of hemoglobin and red blood cells is reduced due to the dilution because of the volume expansion treatment or the compensatory infiltration of tissue fluid into blood vessels to supplement the plasma volume. 2-5 hours after the upper digestive tract is subjected to massive hemorrhage, the white blood cell count can be increased to 10-20 multiplied by 109/L, and the normal state can be recovered two to three days after the hemorrhage stops; 2) and renal function. Due to the decomposition of hemoglobin, the glomerular filtration rate is reduced, and the blood urea nitrogen is increased, reaches a peak within 24-48 hours and is reduced to normal within 3-4 days generally. A blood urea nitrogen/blood creatinine ratio greater than 25:1 indicates upper gastrointestinal bleeding; 3) and liver function. Some patients are accompanied by elevated bilirubin and transaminase; 4) and the feces are conventional. The fecal occult blood test is positive, and directly prompts gastrointestinal hemorrhage and the like; in specific implementation, objective index data (such as pulse, blood pressure, bowel sounds, urine volume, liver function and other changes) of part of biological indexes can be screened as the related biological index data of the invention.
Extracting characteristic items of upper gastrointestinal hemorrhage symptom information in each case sample through TF-IDF, and establishing a characteristic vector A of the upper gastrointestinal hemorrhage symptom information; normalizing the biological index data to form a biological index vector B; the case sample feature vector consists of a feature vector of symptom information of upper gastrointestinal hemorrhage and a biological index vector, wherein C is [ A, B ].
The disease clustering module 300: the clustering method is used for determining the clustering category and clustering the characteristic vectors of the case samples by adopting an improved K-means clustering algorithm optimized by a bird swarm algorithm;
upper gastrointestinal bleeding disorders generally fall into five major categories: 1) (iii) esophageal disorders; 2) gastric and duodenal diseases; 3) liver and gallbladder diseases; 4) diseases of the upper gastrointestinal tract adjacent organs or tissues; 5) the invention relates to a method for clustering general diseases, and the like, therefore, the clustering class number K is 5, and the disease clustering module specifically comprises:
an initialization unit: initializing a population, respectively selecting and calculating fitness values, and selecting an initialized global optimal value; each population individual represents a case sample data.
A population updating unit: generating a random number a between (0,1) and a constant P, when a > P, selecting foraging, otherwise keeping alert;
when a is larger than P, the foraging position is updated in a nonlinear weight adjustment mode, and the position updating formula is as follows:
Figure BDA0002711505430000071
Figure BDA0002711505430000072
Figure BDA0002711505430000073
wherein the content of the first and second substances,
Figure BDA0002711505430000074
respectively showing the position of the ith bird in the jth dimension space at the time t, C1And C2Respectively a perception coefficient and a social evolution coefficient, Pi,jFor the optimal position for the i-th bird to pass, Gi,jAnd the optimal position of the population is obtained.
When a < P, the bird group remains alert and the position update formula is:
Figure BDA0002711505430000075
Figure BDA0002711505430000076
Figure BDA0002711505430000077
wherein, a1、a2∈[0,2]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, epsilon is a constant, meanjIs the average fitness value of the population;
allowing the bird group to migrate periodically, generating a producer and an entrepressor, and updating the positions of the producer and the entrepressor;
the position updating formula for the producer and the food entrepreneur is as follows:
Figure BDA0002711505430000078
Figure BDA0002711505430000079
where randn (0,1) represents a Gaussian distribution with a mean of 0 and a variance of 1, and FL ∈ [0,2 ].
An evaluation unit: calculating a new fitness value, the fitness function being the sum of the intra-class distances, i.e.
Figure BDA0002711505430000081
Where K is the number of cluster categories, d (X)i,Cj) For each data object X in class jiTo a corresponding cluster center point CjThe distance of (c).
And after the fitness is obtained through calculation, selecting the position with the best fitness, updating the historical optimal value, returning to the population updating unit until the set iteration times are reached, and outputting the optimal position as a clustering center point.
The improved bird swarm algorithm is combined with the K mean value algorithm to perform case clustering, the population position is updated in a nonlinear weight adjustment mode, the learning capacity of excellent individuals is enhanced in the iterative learning process, the population can be efficiently optimized in the foraging initial stage, and the convergence speed is increased; and disease category subdivision is carried out based on the aggregation result, so that the disease screening range can be reduced, and the disease prediction speed can be accelerated.
The joint diagnostic module 400: the method is used for acquiring the cases to be diagnosed, clustering the cases to be diagnosed and establishing a combined diagnosis model of semantic similarity between symptom information of upper gastrointestinal hemorrhage between the cases to be diagnosed and the case samples in the cluster and Euclidean distance between biological index data. The combined diagnosis module specifically comprises:
a clustering unit: performing feature extraction on the acquired case to be diagnosed to obtain a case feature vector to be diagnosed, calculating Euclidean distances from the case feature vector to be diagnosed to each cluster central point, and selecting a cluster with the minimum Euclidean distance;
a calculation unit: calculating cosine similarity alpha between characteristic vectors of symptom information of upper gastrointestinal hemorrhage between a case to be diagnosed and case samples in the cluster; calculating the Euclidean distance beta of a biological index vector between a case to be diagnosed and a case sample in a cluster;
a judging unit: calculating the joint similarity s, s-w of each case sample1α+w2(1-β)，w1、w2Is a weight coefficient, w1+w2And (1) taking the disease type corresponding to the case sample with the maximum joint similarity as the disease prediction result of the case to be diagnosed.
The invention combines the symptom information of the upper gastrointestinal hemorrhage with the objective index of the effective biological index, carries out combined diagnosis and screening by establishing a characteristic information semantic similarity calculation model and a diagnosis model constructed by biological cognitive indexes, can accurately predict the result of the upper gastrointestinal hemorrhage, and can analyze the cause of the upper gastrointestinal hemorrhage based on the big data information of the electric power case library.
The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, and the processor calls the program instructions to realize a data acquisition module, a feature extraction module, a disease clustering module and a joint diagnosis module in the system.
The invention also discloses a computer readable storage medium which stores computer instructions, wherein the computer instructions enable the computer to realize the data acquisition module, the feature extraction module, the disease clustering module and the joint diagnosis module in the system. The storage medium includes: u disk, removable hard disk, ROM, RAM, magnetic disk or optical disk, etc.
The above-described system embodiments are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Some or all of the modules may be selected according to the actual Xian to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A system for predicting a disease based on upper gastrointestinal bleeding, the system comprising:
a data acquisition module: a case sample for extracting the above gastrointestinal hemorrhage from the electronic case library as a clinical first complaint symptom;
a feature extraction module: the system is used for extracting symptom information of upper gastrointestinal hemorrhage, related biological index data and corresponding disease names in each case sample to form case sample feature vectors;
a disease clustering module: the clustering method is used for determining the clustering category and clustering the characteristic vectors of the case samples by adopting an improved K-means clustering algorithm optimized by a bird swarm algorithm;
a joint diagnosis module: the method is used for obtaining cases to be diagnosed, clustering the cases to be diagnosed, establishing a combined diagnosis model of semantic similarity between symptom information of upper gastrointestinal hemorrhage between the cases to be diagnosed and case samples in the cluster and Euclidean distance between biological index data, and predicting the cases to be diagnosed.
2. The system according to claim 1, wherein the feature extraction module extracts feature items of the symptom information of upper gastrointestinal hemorrhage in each case sample through TF-IDF to establish feature vectors of the symptom information of upper gastrointestinal hemorrhage; the relevant biological index data comprises blood routine, liver function, kidney function and feces routine, and the biological index data is normalized to form a biological index vector; the case sample feature vector is composed of a feature vector of symptom information of upper gastrointestinal hemorrhage and a biological index vector.
3. The system for predicting disease based on upper gastrointestinal hemorrhage of claim 1, wherein the disease clustering module specifically comprises:
an initialization unit: initializing a population, calculating a fitness value, and selecting an initialized global optimal value;
a population updating unit: generating a random number a between (0,1) and a constant P, when a > P, selecting foraging, otherwise keeping alert;
when a is larger than P, the foraging position is updated in a nonlinear weight adjustment mode, and the position updating formula is as follows:
Figure FDA0002711505420000011
Figure FDA0002711505420000012
Figure FDA0002711505420000013
wherein the content of the first and second substances,
Figure FDA0002711505420000021
respectively showing the position of the ith bird in the jth dimension space at the time t, C1And C2Respectively a perception coefficient and a social evolution coefficient, Pi,jFor the optimal position for the i-th bird to pass, Gi,jAnd the optimal position of the population is obtained.
When a < P, the bird group remains alert and the position update formula is:
Figure FDA0002711505420000022
Figure FDA0002711505420000023
Figure FDA0002711505420000024
wherein, a1、a2∈[0,2]K is [1, N ]]And k ≠ i, fi、fkThe fitness values of the ith and the k wolfs are respectively; sumf is the sum of fitness of the whole population, epsilon is a constant, meanjIs the average fitness value of the population;
allowing the bird group to migrate periodically, generating a producer and an entrepressor, and updating the positions of the producer and the entrepressor;
an evaluation unit: calculating a new fitness value, updating a historical optimal value, returning to the population updating unit until a set iteration number is reached, and outputting an optimal position as a clustering center point.
4. The system of claim 3, wherein the disease clustering module is configured to determine the fitness function of the evaluation unit as the sum of the intra-class distances
Figure FDA0002711505420000025
Where K is the number of cluster categories, d (X)i,Cj) For each data object X in class jiTo a corresponding cluster center point CjThe distance of (c).
5. The system for predicting a disease based on upper gastrointestinal bleeding according to claim 1, wherein the joint diagnosis module specifically comprises:
a clustering unit: performing feature extraction on the acquired case to be diagnosed to obtain a case feature vector to be diagnosed, calculating Euclidean distances from the case feature vector to be diagnosed to each cluster central point, and selecting a cluster with the minimum Euclidean distance;
a calculation unit: calculating cosine similarity alpha between characteristic vectors of symptom information of upper gastrointestinal hemorrhage between a case to be diagnosed and case samples in the cluster; calculating the Euclidean distance beta of a biological index vector between a case to be diagnosed and a case sample in a cluster;
a judging unit: calculating the joint similarity s, s-w of each case sample1α+w2(1-β)，w1+w2And (1) taking the disease type corresponding to the case sample with the maximum joint similarity as the disease prediction result of the case to be diagnosed.
6. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the system of any one of claims 1-5.
7. A computer readable storage medium storing computer instructions which cause a computer to implement the system of any one of claims 1 to 5.
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