Risk prediction method based on clinical examination and medication intervention data

Abstract

The invention relates to a risk prediction method based on clinical examination and medication intervention data, and the method comprises the steps: selecting start and end nodes from the clinical examination data in an individual observation period, carrying out the vectorization modeling, and obtaining an input vector x1; constructing an intervention dictionary, calculating the characteristic frequency of medication intervention, and performing vectorization modeling on individual medication intervention data to obtain an input vector x2; combining the input vector x1 and the input vector x2 to obtain an input feature vector X; inputting the input feature vector X into a prediction model, obtaining a real result Y through fitting, optimizing prediction model parameters, and obtaining a final prediction model; inputting the individual data into the final prediction model subjected to parameter adjustment and outputting model prediction results. According to the method, the design is reasonable, the relationship between different medication intervention combinations and the influence of the medication intervention combinations on the individual state can be explored, the prediction is accurate and reliable, and each evaluation index is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, in particular to a risk prediction method based on clinical test and medication intervention data. Background technique [0002] In the medical industry, risk prediction will set a specific time window for a prediction target based on the definition of a certain population, including the time point when the prediction is made and the time window to be predicted, and the probability of occurrence of the predicted target. [0003] Although traditional regression methods are widely used in risk prediction, there is still room for improvement in terms of prediction accuracy and model interpretability. In recent years, machine learning algorithms that perform well in learning and summarizing large amounts of data have used big data to establish many models with very ideal prediction results in predicting human activities and events. [0004] Since the clinical test data will be affected by o...

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Problems solved by technology

[0003] Although traditional regression methods are widely used in risk prediction, there is still room for improvement in terms of prediction accuracy and model interpretability of these methods

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