Research On Diabetes Diagnosis Model For Smart Healthcare

Diabetes is a common chronic disease that endangers the health of our people.Early diagnosis and treatment of this disease are of great significance to improve the health of our people.The diabetes diagnosis model constructed under the smart healthcare pattern that combines artificial intelligence technology and medical data knowledge can learn the rich medical knowledge contained in the disease text,and doctors can rapidly and precisely arrive at diagnostic decisions by using this model,which provides Patients can provide more accurate treatment plans,which can not only improve the patient’s treatment experience,but also reduce the incidence of medical errors.Traditional machine learning methods have limitations when dealing with unstructured medical text classification tasks,because these methods require complex feature engineering,have certain requirements for professional knowledge and experience,and are difficult to cope with the needs of complex and diverse medical scenarios.In contrast,deep learning techniques can create extensive associations between data elements by utilizing artificial neural networks,making feature extraction more convenient and efficient,and better handling medical text classification tasks.When dealing with text classification tasks in the field of natural language processing,convolutional neural networks and recurrent neural networks are often used in combination to take advantage of their respective unique advantages.However,most classification models combine these two neural networks in series.Although higher-dimensional features can be extracted in this way,some effective information may be lost between connections of different structures.In order to solve this problem,this paper improves the tandem model and proposes a dual-channel-based Bi LSTM-CNN diabetes diagnosis model,which can be classified and diagnosed according to whether there is diabetes.In this model,Bi LSTM with attention mechanism and CNN are used to classify and diagnose disease texts in a parallel manner.CNN is used to extract the features of the order and reverse order of the disease text respectively,so as to obtain more effective information in the disease text.The follow-up experimental results proved the effectiveness of the proposed dual-channel-based diabetes diagnosis model.This paper uses the disease text data in electronic medical records for diabetes diagnosis research,but the disease text is a short medical text,so there are problems such as vague context information and unusual words.To address these issues,this paper proposes a Bi LSTM-CNN diabetes diagnosis model incorporating external knowledge,which uses external knowledge to enhance the semantic representation of disease texts on the basis of the dual-channel-based Bi LSTM-CNN diabetes diagnosis model.First,find out the relevant entities in the disease text through the entity linking method,then find out the conceptual knowledge related to these entities through the external knowledge base,and then encode the conceptual knowledge.In this process,in order to measure the importance of external knowledge,multiple attention mechanisms are introduced,and finally the disease text and conceptual knowledge representation are fused to achieve semantic enhancement of the disease text.It has been verified by experiments that the effective information in the disease text can be fully mined through external knowledge,and it is especially effective when dealing with uncommon medical vocabulary.Therefore,the model can further improve the accuracy of diabetes diagnosis and has a certain degree of interpretability.