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Background: IDH is a common and serious complication of chronic hemodialysis that can lead to adverse long-term outcomes, including increased cardiovascular and all-cause mortality. Predicting IDH in advance may aid in establishing an intervention plan. Method: This study used 1173783 hemodialysis sessions from 837 patients and 211 features (including time-span varying and basic information features) as datasets. We constructed 21 features (including ion changes before and after dialysis) by clinical physicians from the raw datasets. We selected 89 features by grid search recursive feature elimination (RFE) based on extreme gradient boosting (XGBoost) (Gs-RFE-XGBoost) and XGBoost model to predict IDH. We used the Shapley value to achieve an explainable result for the 20 features. Besides, we used the quartile 2.5 to quartile 97.5 to get the reference range of the important continuous features. Result: We found the most important 20 features that are blood pressure related (including the datasets of 4 features and 4 construct features), platelet count, pathway type, pre-dialysis dry weight, dialyzer type, hematocrit, basophils, platelet distribution width, pre-dialysis uric acid, KT/V, parathyroid hormone (PTH), changes in calcium before and after dialysis, dialysis age. We get 0.8446 of avg recall, 0.9149 of avg F1, 0.9410 of avg PRAUC, 0.8823 of avg precision, and 0.8944 of avg MCC to predict the IDH in real-time. Conclusion: The Gs-RFE-XGBoost-Shapley methods can be applied as a practical, explained, and accurate method to predict IDH. The feature’s reference range can provide hints for the clinical physician to prevent the IDH of maintenance hemodialysis (MHD).

High prevalence of hypertension and complicated medication knowledge have presented challenges to hypertension clinicians and general practitioners. Clinical decision support systems (CDSSs) are developed to aid clinicians in decision making. Current clinical knowledge is stored in fixed templates, which are not intuitive for clinicians and limit the knowledge reusability. Knowledge graphs (KGs) store knowledge in a way that is not only intuitive to humans but also processable by computers directly. However, existing medical KGs such as UMLS and CMeKG are general purpose and thus lack enough knowledge to enable hypertension medication.We first construct a KG specific to hypertension medication according to the Chinese hypertension guideline and then develop the corresponding CDSS to implement hypertension medication and knowledge management. Current advances in knowledge graph representation and modelling are researched and applied in the complex medical knowledge representation. Traditional knowledge representation and KG representation are innovatively combined in the storage of the KG to enable convenient knowledge management and easy application by the CDSS. Along a predefined reasoning path in the KG, the CDSS finally accomplishes the hypertension medication by applying knowledge stored in the KG. 124 health records of a hypertension Chief Physician from Beijing Anzhen Hospital, Capital Medical University, are collected to evaluate the system metrics on the single drug recommendation task.The proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support. With elaborate design on knowledge representation, knowledge management is intuitive and convenient. By virtue of the KG, medication recommendations are highly visualized and explainable. Experiments on 124 health records with 90% guideline compliance collected from hospitals in single class recommendation task achieve 91%, 83% and 77% on recall, hit@3 and MRR metrics respectively, which demonstrates the quality of the KG and effectiveness of the system.

Background: Among the problems caused by hypertension, the early renal damage is often ignored. It can not be diagnosed until the condition is serious and irreversible damage occurs. So we decide to screen and explore related risk factors for hypertensive patients with early renal damage, and establish early-warning model of renal damage based on the data-mining method to achieve early diagnosis for hypertensive patients with renal damage. Methods: With the aid of electronic information management system for hypertensive specialist out-patient, we collected 513 cases of original untreated hypertensive patients, and recorded their demographic data, ambulatory blood pressure parameters, blood routine index and blood biochemical index to establish the clinical database, then we screen risk factors for early renal damage through feature engineering, and use Random Forest, Extra-Trees and XGBoost to build a early-warning model respectively. Finally, we build a new model by model fusion based on Stacking strategy. We use cross validation to evaluate the stability and reliability of each mode to determine the best risk assessment model. Results: According to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age and HDL. Among the early-warning models of renal damage without model fusion, XGBoost has the best effect, the average accuracy of 5-fold cross validation is 0.90457. And the average accuracy of the two-dimensional fusion model based on Stacking strategy is 0.91428, which is greatly improved. Conclusions: Through feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.