CN112053784B - Prediction system for predicting occurrence of calcium oxalate kidney stones - Google Patents
Prediction system for predicting occurrence of calcium oxalate kidney stones Download PDFInfo
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- 206010029148 Nephrolithiasis Diseases 0.000 title claims abstract description 42
- 208000000913 Kidney Calculi Diseases 0.000 title claims abstract description 41
- QXDMQSPYEZFLGF-UHFFFAOYSA-L calcium oxalate Chemical compound [Ca+2].[O-]C(=O)C([O-])=O QXDMQSPYEZFLGF-UHFFFAOYSA-L 0.000 title claims abstract description 25
- MUBZPKHOEPUJKR-UHFFFAOYSA-N Oxalic acid Chemical compound OC(=O)C(O)=O MUBZPKHOEPUJKR-UHFFFAOYSA-N 0.000 claims abstract description 42
- QTBSBXVTEAMEQO-UHFFFAOYSA-N Acetic acid Chemical compound CC(O)=O QTBSBXVTEAMEQO-UHFFFAOYSA-N 0.000 claims abstract description 39
- 238000007637 random forest analysis Methods 0.000 claims abstract description 30
- 241001135750 Geobacter Species 0.000 claims abstract description 20
- 241000207304 Kroppenstedtia Species 0.000 claims abstract description 19
- 241000266824 Oscillospira Species 0.000 claims abstract description 15
- 210000003608 fece Anatomy 0.000 claims abstract description 15
- 235000006408 oxalic acid Nutrition 0.000 claims abstract description 14
- 210000002700 urine Anatomy 0.000 claims abstract description 13
- 241000949716 Sphaerochaeta Species 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 230000006870 function Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 6
- 230000000694 effects Effects 0.000 abstract description 4
- 238000003759 clinical diagnosis Methods 0.000 abstract description 3
- 238000013528 artificial neural network Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 5
- 238000012706 support-vector machine Methods 0.000 description 5
- 206010033645 Pancreatitis Diseases 0.000 description 4
- 206010033647 Pancreatitis acute Diseases 0.000 description 4
- 201000003229 acute pancreatitis Diseases 0.000 description 4
- 201000010099 disease Diseases 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 2
- 208000002193 Pain Diseases 0.000 description 2
- 230000001580 bacterial effect Effects 0.000 description 2
- 229910052791 calcium Inorganic materials 0.000 description 2
- 239000011575 calcium Substances 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000001338 necrotic effect Effects 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 108020004465 16S ribosomal RNA Proteins 0.000 description 1
- 208000004998 Abdominal Pain Diseases 0.000 description 1
- 241000193833 Bacillales Species 0.000 description 1
- 241000193830 Bacillus <bacterium> Species 0.000 description 1
- 208000008035 Back Pain Diseases 0.000 description 1
- 208000002881 Colic Diseases 0.000 description 1
- LEVWYRKDKASIDU-QWWZWVQMSA-N D-cystine Chemical compound OC(=O)[C@H](N)CSSC[C@@H](N)C(O)=O LEVWYRKDKASIDU-QWWZWVQMSA-N 0.000 description 1
- 241001135761 Deltaproteobacteria Species 0.000 description 1
- 208000008930 Low Back Pain Diseases 0.000 description 1
- 206010053159 Organ failure Diseases 0.000 description 1
- 241000192142 Proteobacteria Species 0.000 description 1
- 241000095588 Ruminococcaceae Species 0.000 description 1
- 241000589970 Spirochaetales Species 0.000 description 1
- 241001180364 Spirochaetes Species 0.000 description 1
- LEHOTFFKMJEONL-UHFFFAOYSA-N Uric Acid Chemical compound N1C(=O)NC(=O)C2=C1NC(=O)N2 LEHOTFFKMJEONL-UHFFFAOYSA-N 0.000 description 1
- TVWHNULVHGKJHS-UHFFFAOYSA-N Uric acid Natural products N1C(=O)NC(=O)C2NC(=O)NC21 TVWHNULVHGKJHS-UHFFFAOYSA-N 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 229960005069 calcium Drugs 0.000 description 1
- 208000003980 calcium oxalate nephrolithiasis Diseases 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 229960003067 cystine Drugs 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 210000003734 kidney Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000022437 nephrolithiasis susceptibility caused by SLC26A1 Diseases 0.000 description 1
- 229940116315 oxalic acid Drugs 0.000 description 1
- 230000001314 paroxysmal effect Effects 0.000 description 1
- 230000037081 physical activity Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 229940116269 uric acid Drugs 0.000 description 1
- 230000002485 urinary effect Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
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Abstract
The invention provides a prediction system for predicting occurrence of calcium oxalate kidney stones, and belongs to the field of neural networks. The prediction system comprises an input module, a calculation module and an output module: I. the input module is used for transmitting the following information of the patient to the calculation module: gender, oxalic acid concentration in urine, acetic acid concentration in feces, relative abundance value of g __ Geobacter, relative abundance value of g __ Kroppenstedtia, relative abundance value of g __ Sphaerochaeta, relative abundance value of g __ Oscillospira; II, a trained prediction network model for predicting the occurrence of calcium oxalate kidney stones is arranged in the calculation module; and III, the output module is used for outputting the probability value Y. The invention combines 3 clinical indexes and the relative abundance values of 4 genera, and can accurately predict the occurrence of the kidney stone by using a conventional algorithm. Wherein the random forest algorithm has the most accurate prediction effect. The prediction method can accurately predict the occurrence of the kidney stone, particularly the occurrence of the kidney stone of calcium oxalate, and can provide a basis for clinical diagnosis and treatment of the kidney stone.
Description
Technical Field
The invention relates to the field of neural networks, in particular to a prediction system for predicting occurrence of calcium oxalate kidney stones.
Background
Renal calculus (renal calculi) is caused by abnormal accumulation of crystal substances (such as calcium, oxalic acid, uric acid, cystine and the like) in the kidney, is a common disease and frequently encountered disease of the urinary system, is more frequently encountered in males than in females, is frequently encountered in young and middle-aged years, has no obvious difference in morbidity on the left side and the right side, and contains 90 percent of calcium. Among them, calcium oxalate calculus is the most common one of kidney calculus, and accounts for more than 80% of kidney calculus. 40-75% of patients with renal calculus have lumbago of different degrees. Large stones with small mobility, manifested as soreness and distension of the waist, or dull pain during increased physical activity. The colic caused by small stones often suddenly causes severe pain of incised sample of waist and abdomen, and is paroxysmal. If the occurrence of calcium oxalate kidney stones can be predicted, the method can provide help for clinical treatment of the calcium oxalate kidney stones.
The development of disease prediction systems provides important technical tools for the prevention and treatment of diseases. Such as: with regard to a necrotic acute pancreatitis patient operation time prediction model (CN111081377A), the optimal operation time is provided for the necrotic acute pancreatitis patient; a prediction model (CN111243752A) for acute pancreatitis-induced organ failure provides a technical means for further predicting the progress of acute pancreatitis and adopting a prevention/treatment method in advance.
At present, no biomarker capable of predicting the occurrence of renal calculus is available, and no prediction system for predicting the occurrence of calcium oxalate renal calculus is available.
Disclosure of Invention
In order to solve the above problems, the present invention provides a system for predicting occurrence of calcium oxalate kidney stones, comprising an input module, a calculation module, and an output module:
I. the input module is used for transmitting the following information of the patient to the calculation module: gender, oxalic acid concentration in urine, acetic acid concentration in feces, relative abundance value of g __ Geobacter, relative abundance value of g __ Kroppenstedtia, relative abundance value of g __ Sphaerochaeta, relative abundance value of g __ Oscillospira;
II, a trained prediction network model for predicting the occurrence of calcium oxalate kidney stones is arranged in the calculation module;
and III, the output module is used for outputting the probability value Y.
Wherein g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeeta and g __ Oscillospira are four genera.
g __ Geobacter are all called:
g__Geobacter_f__Geobacteraceae_o__Desulfuromonadales_c__Deltaproteo bacteria_p__Proteobacteria。
g __ Kroppenstedtia collectively referred to as:
g__Kroppenstedtia_f__Thermoactinomycetaceae_o__Bacillales_c__Bacilli_p__Firmicutes；
g __ Sphaerochaeta is collectively referred to as:
g__Sphaerochaeta_f__Spirochaetaceae_o__Spirochaetales_c__Spirochaetia_p__Spirochaetes；
g __ Oscillospira are all called:
g__Oscillospira_f__Ruminococcaceae_o__Clostridiales_c__Clostridia_p__Firmicutes。
further, the relative abundance values are relative abundance values of four genera, g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeta and g __ Oscilllospira, detected from the patient's feces.
Further, the detection is detection by using a 16SrRNA technology.
Further, the predictive network model uses randomforest, xgboost, glmnet, e1071, caret and/or MASS packages in the R language.
Further, the predictive network model uses randomforest packets in the R language.
Further, the predictive network model uses random forest, gradient boosting tree, support vector machine, lasso, ridge regression, elastic network, k-nearest neighbor, or linear discriminant analysis methods.
Further, the predictive network model uses a random forest approach.
Further, the random forest method for predicting the occurrence of calcium oxalate kidney stones comprises the following steps:
(1) establishing a random forest model: calling randomForest packet, inputting Y value and gender, oxalic acid concentration in urine, acetic acid concentration in feces and relative abundance values of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerohaeta and g __ Oscilospira, calling randomForest () function, and establishing random forest model by using default parameters of ntree 500, import FALSE, localImp FALSE, nPerm 1, replace TRUE, oob.
(2) Predicting calcium oxalate kidney stone occurrence: inputting the sex, the concentration of oxalic acid in urine, the concentration of acetic acid in feces and the relative abundance values of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerocaeta and g __ Oscillospira, and calling a predict () function to predict the occurrence of calcium oxalate kidney stones to obtain a probability value Y.
The invention also provides the application of the reagent for detecting 3 clinical indexes and 4 genus indexes in preparing the reagent for predicting the occurrence of calcium oxalate kidney stones;
the 3 clinical indexes are sex, oxalic acid concentration in urine and acetic acid concentration in feces;
the 4 genus indices are relative abundance value of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerocaeta and g __ Oscilllospira.
Further, the relative abundance values are relative abundance values of four genera, g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeta and g __ Oscilllospira, detected from the feces of the patient;
preferably, the detection is by the 16s rrna technique.
The invention combines 3 clinical indexes (sex, oxalic acid concentration in urine and acetic acid concentration in feces) and relative abundance values of 4 genera (g __ Geobacter, g __ Kroppensteptia, g __ Sphaerochaeta and g __ Oscillospira), and can accurately predict the occurrence of renal calculus by using a conventional algorithm. Wherein the random forest algorithm has the most accurate prediction effect. The prediction method can accurately predict the occurrence of the kidney stone, particularly the occurrence of the kidney stone of calcium oxalate, and can provide a basis for clinical diagnosis and treatment of the kidney stone.
Obviously, many modifications, substitutions, and variations are possible in light of the above teachings of the invention, without departing from the basic technical spirit of the invention, as defined by the following claims.
The present invention will be described in further detail with reference to the following examples. This should not be understood as limiting the scope of the above-described subject matter of the present invention to the following examples. All the technologies realized based on the above contents of the present invention belong to the scope of the present invention.
Drawings
Fig. 1 is a result of verifying AUC values of a random forest model using a test set.
Detailed Description
Example 1 prediction method for predicting occurrence of calcium oxalate kidney stones
The invention combines 3 clinical indexes and 4 genus indexes to establish a prediction model to predict the occurrence of calcium oxalate kidney stones.
The 3 clinical indicators were: sex, concentration of oxalic acid in urine and concentration of acetic acid in feces;
the 4 genus indices are:
relative abundance value of g __ Geobacter _ f __ Geobacteriaceae _ o __ Del furamonoanales _ c __ Deltaproteo bacteria _ p __ Proteobacteria (abbreviated as g __ Geobacter),
The relative abundance value of g __ Kroppenstedtia _ f __ Thermoactinomyceteae _ o __ Bacillales _ c __ Bacillus _ p __ firmutes (abbreviated as g __ Kroppenstedtia),
Relative abundance value of g __ Sphaerochaeta _ f __ Sphaetaceae _ o __ Spirochaetales _ c __ Spirochaetaia _ p __ Spirochaetes (abbreviated as g __ Sphaechaeta),
Relative abundance value of g __ Oscillospira _ f __ Ruminococcaceae _ o __ clones _ c __ clones _ p __ firmicites (abbreviated as g __ Oscillospira).
Using randomfortest package, xgboost package, glmnet package, e1071 package, caret package and MASS package in R language, using random forest, gradient lifting tree, support vector machine, lasso, ridge regression, elastic network, k neighbor and linear discriminant analysis 8 machine learning methods. And respectively combining the relative abundance values of 4 bacterial genera, the values of 3 clinical indexes and the relative abundance values of 4 bacterial genera with the values of 3 clinical indexes as input data to establish a model. The performance of each model was evaluated from the AUC values.
It was found that when modeled with only 4 genera, AUC values for random forests, gradient boosting trees, support vector machines, lasso, ridge regression, elastic networks, k-nearest neighbors, and linear discriminant analysis were 0.72, 0.73, 0.61, 0.68, 0.73, and 0.68, respectively.
When modeled with only 3 clinical indices, AUC values for random forests, gradient boosting trees, support vector machines, lasso, ridge regression, elastic networks, k-nearest neighbors, and linear discriminant analysis were 0.89, 0.87, 0.83, 0.84, 0.85, and 0.83, respectively.
When modeled using 4 genera of genera in combination with 3 clinical indices, the AUC values for random forests, gradient boosting trees, support vector machines, lasso, ridge regression, elastic networks, k-nearest neighbors, and linear discriminant analysis were 0.91, 0.90, 0.83, 0.86, 0.81, and 0.85, respectively.
The modeling result shows that when 4 genera are used for modeling in combination with 3 clinical indexes, the modeling effect of 8 methods is generally superior to that of 4 genera or 3 clinical indexes. And the AUC value of random forest was the highest, 0.91 (table 1).
TABLE 1.8 AUC values for machine learning methods
Example 2 prediction of calcium oxalate nephrolithiasis occurrence Using random forest model
Calling a randomfortest package, using the existence of the kidney stones of the 123 samples (57 kidney stone patients and 66 healthy people) as class labels, adopting 5-fold cross validation, dividing the 123 samples into 5 subsets, randomly selecting 1 subset as a test, and using the rest 4 subsets as training. Inputting Y values and gender of 4 subsets, oxalic acid concentration in urine, acetic acid concentration in feces, and relative abundance values of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeta, and g __ Oscillospira, calling randomForest () function, establishing random forest model using default parameters of ntree 500, import FALSE, locaimp 1, replace TRUE, oob. The gender of 1 subset, the concentration of oxalic acid in urine, the concentration of acetic acid in feces, and the relative abundance values of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeta, and g __ Oscillospira were entered, and the predict () function was called to predict the class of the remaining subset.
For the random forest model, validation was performed using 30 test sets and the AUC value was found to reach 0.86 (fig. 1).
In conclusion, the present invention combines 3 clinical indicators (sex, oxalic acid concentration in urine and acetic acid concentration in feces) and the relative abundance values of 4 genera (g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerochaeta and g __ Oscillospira), and using a conventional algorithm, can accurately predict the occurrence of renal calculus. Wherein the random forest algorithm has the most accurate prediction effect. The prediction method can accurately predict the occurrence of the kidney stone, particularly the occurrence of the kidney stone of calcium oxalate, and can provide a basis for clinical diagnosis and treatment of the kidney stone.
Claims (3)
1. A prediction system for predicting occurrence of calcium oxalate kidney stones, comprising an input module, a calculation module and an output module, wherein:
I. the input module is used for transmitting the following information of the patient to the calculation module: gender, oxalic acid concentration in urine, acetic acid concentration in feces, relative abundance value of g __ Geobacter, relative abundance value of g __ Kroppenstedtia, relative abundance value of g __ Sphaerochaeta, relative abundance value of g __ Oscillospira;
II, a trained prediction network model for predicting the occurrence of calcium oxalate kidney stones is arranged in the calculation module;
III, the output module is used for outputting the probability value Y;
the relative abundance value is relative abundance value of four genera including g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerhaeta and g __ Oscilllospira detected from the excrement of a patient; the detection is carried out by utilizing a 16SrRNA technology;
the g __ Geobacter is totally called as:
g__Geobacter_f__Geobacteraceae_o__Desulfuromonadales_c__Deltaproteobacteria_p__Proteobacteria；
the g __ Kroppenstedtia is collectively referred to as:
g__Kroppenstedtia_f__Thermoactinomycetaceae_o__Bacillales_c__Bacilli_p__Firmicutes；
the g __ Sphaerochaeta is collectively referred to as:
g__Sphaerochaeta_f__Spirochaetaceae_o__Spirochaetales_c__Spirochaetia_p__Spirochaetes；
the g __ Oscillospira are all called:
g__Oscillospira_f__Ruminococcaceae_o__Clostridiales_c__Clostridia_p__Firmicutes；
the prediction network model uses a random forest method; the random forest method for predicting the occurrence of calcium oxalate kidney stones comprises the following steps:
(1) establishing a random forest model: calling randomForest package, inputting Y value and gender, oxalic acid concentration in urine, acetic acid concentration in feces and relative abundance value of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerohaeta and g __ Oscillospira, calling randomForest () function, and establishing random forest model by using default parameters ntree =500, import = FALSE, localImp = FALSE, nPerm =1, replace = TRUE, oob.
(2) Predicting calcium oxalate kidney stone occurrence: inputting the sex, the concentration of oxalic acid in urine, the concentration of acetic acid in feces and the relative abundance values of g __ Geobacter, g __ Kroppenstedtia, g __ Sphaerocaeta and g __ Oscillospira, and calling a predict () function to predict the occurrence of calcium oxalate kidney stones to obtain a probability value Y.
2. The prediction system of claim 1, wherein: the predictive network model uses randomforest, xgboost, glmnet, e1071, caret and/or MASS packages in the R language.
3. The prediction system of claim 2, wherein: the predictive network model uses randomforest packets in the R language.
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| CN109402022A (en) * | 2018-12-05 | 2019-03-01 | 上海上药信谊药厂有限公司 | A kind of Oxalobacter formigenes and its application |
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| AU7009598A (en) * | 1997-06-13 | 1998-12-17 | Flinders Medical Centre | Pharmaceutical agent and method of treatment |
| CN109402022A (en) * | 2018-12-05 | 2019-03-01 | 上海上药信谊药厂有限公司 | A kind of Oxalobacter formigenes and its application |
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| Title |
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