System and method for predicting disease risk in female

Abstract

This disclosure provides a system and method for predicting disease risk in female subjects with a need for such prediction, comprising an input module, a sub-outcome analysis module, an outcome analysis module, an outcome display module, and a sub-outcome display module. The input module is configured to input characteristic data of the female subject. The sub-outcome analysis module is configured to generate sub-outcomes corresponding to the comorbidity risk of the disease based on the characteristic data. The outcome analysis module is configured to generate a result corresponding to the risk of the disease based on a comprehensive assessment of the sub-outcomes. The outcome display module is configured to present the result on a first user interface. The sub-outcome display module is configured to present the sub-outcome on a second user interface in response to input selections made via the first user interface.

Claims

1. A system for predicting disease risk in a female subject with a need, comprising: an input module configured to input characteristic data of the female subject with a need; a sub-outcome analysis module coupled to the input module and configured to generate a sub-outcome corresponding to the comorbidity risk of a disease based on the characteristic data; an outcome analysis module coupled to the sub-outcome analysis module and configured to generate a result corresponding to the risk of the disease based on a comprehensive assessment of the sub-outcome; an outcome display module coupled to the outcome analysis module and configured to present the result on a first operating interface of the outcome display module; and a sub-outcome display module coupled to the sub-outcome analysis module and the outcome display module and configured to present the sub-outcome on a second operating interface of the sub-outcome display module in response to an input selection made via the first operating interface.

2. The system as described in claim 1, wherein, The characteristic data includes at least one of the following: demographic data, cancer status, presence of comorbidities, treatment received, medications received, laboratory test data, and any combination thereof.

3. The system as described in claim 2, wherein, The demographic data includes age, body mass index, smoking status, alcohol consumption status, or betel nut chewing status; the cancer status includes tumor size, cancer stage, human epidermal growth factor receptor 2 expression, estrogen receptor expression, luteinizing hormone receptor expression, or Ki-67 proliferation index; the presence of comorbidities includes hypertension, hyperlipidemia, kidney disease, lung disease, diabetes, cerebrovascular disease, liver disease, congestive heart failure, dementia, rheumatic disease, or peptic ulcer disease, or the presence of comorbidities is indicated by the Charson Comorbidity Index score; the treatment is surgery or radiation therapy. The drug is a biguanide, a 3-hydroxy-3-methylpentadiazine coenzyme A reductase inhibitor, a platelet aggregation inhibitor excluding heparin, a beta-blocker, a dihydropyridine derivative, angiotensin II receptor blocker, a benzodiazepine derivative, a dipeptidyl peptidase-4 inhibitor, a sulfonylurea, or a cyclooxygenase-2 inhibitor; or the laboratory test data are albumin concentration, creatinine concentration, bilirubin concentration, aspartate aminotransferase concentration, alanine aminotransferase concentration, fasting blood glucose concentration, white blood cell count, red blood cell count, or platelet count.

4. The system as described in claim 1, wherein, The disease could be bone density-related disorder, metabolic syndrome, or cancer.

5. The system as described in claim 1, wherein, The comorbidities include bone density disorder, osteoporosis, hypertension, hyperlipidemia, hyperglycemia, ovarian cancer, endometrial cancer, cervical cancer, colorectal cancer, breast cancer, or drug reactions associated with ongoing hormone replacement therapy.

6. The system as described in claim 1, wherein, The disease is a bone mineral density-related disease, and the comorbidity is a bone mineral density condition, osteoporosis, or a drug response related to ongoing hormone replacement therapy, wherein the bone mineral density condition is normal, predicted to worsen, or predicted to improve; the disease is a metabolic syndrome, and the comorbidity is hypertension, hyperlipidemia, hyperglycemia, or a drug response related to ongoing hormone replacement therapy; or the disease is cancer, and the comorbidity is ovarian cancer, endometrial cancer, cervical cancer, colorectal cancer, breast cancer, or a drug response related to ongoing hormone replacement therapy.

7. The system as described in claim 1, wherein, The sub-result analysis module is configured to generate the sub-result based on the feature data using an artificial intelligence model, wherein the artificial intelligence model is configured to execute algorithms including logistic regression, linear discriminant analysis, lightweight gradient boosting machine, gradient boosting machine, extreme gradient boosting machine, adaptive boosting machine, support vector classifier, voting ensemble, random forest, artificial neural network, or any combination thereof.

8. The system as described in claim 1, wherein, The results analysis module is configured to generate the results by means of an artificial intelligence model based on a comprehensive evaluation of the sub-results, wherein the artificial intelligence model is configured to execute algorithms including logistic regression, linear discriminant analysis, lightweight gradient boosting machine, gradient boosting machine, extreme gradient boosting machine, adaptive boosting machine, support vector classifier, voting ensemble, random forest, artificial neural network, or any combination thereof.

9. A method for predicting disease risk in a female subject with a need, comprising: providing a system as described in any one of claims 1 to 8; the input module receiving the characteristic data of the female subject; the system generating a sub-result corresponding to the comorbidity risk of the disease based on the characteristic data; the system generating a result corresponding to the risk of the disease based on a comprehensive assessment of the sub-result; displaying the result on a first operating interface of the result display module; and the system displaying the sub-result on a second operating interface of the sub-result display module in response to an input selection made via the first operating interface.

10. The method as described in claim 9, wherein, The sub-result and at least one of the results are generated by an artificial intelligence model configured to execute an algorithm selected from a group consisting of logistic regression, linear discriminant analysis, lightweight gradient boosting machine, gradient boosting machine, extreme gradient boosting machine, adaptive boosting machine, support vector classifier, voting ensemble, random forest, artificial neural network, and any combination thereof.

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