Online Disease Identification and Diagnosis and Treatment Based on Machine Learning Technology

Abstract

The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recommendation system. The system integrates machine learning algorithms and knowledge graph technology to help patients conduct online consultations. The system analyses the misclassification data of different departments through high-frequency word analysis. The study found that the accuracy rate of our machine learning algorithm model to identify entities in electronic medical records reached 96.29%. This type of model can effectively screen out the most important pathogenic features.

The application of health recommender systems based on knowledge graph: a scoping review

“…The target population involved the general public and clinicians, with 75% of the systems developed for clinicians. Hao and Zhen12 constructed a knowledge graph of “disease symptom” to provide users with disease self-diagnosis services. They also linked user reviews and medical service quality, and used the deep learning model to give the doctors’ service quality evaluation model, which provided users with a more open and reasonable recommendation service.…”

“…Then, a total of 1035 articles were excluded after the title and abstract screening, and 200 articles were excluded after the full-text screening. Finally, 16 studies were included in this scoping review [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] . We also utilized a similar process to retrieve 222 grants from the NSFC official and relevant websites, and 4 records identified from other sources.…”

“…In this review, 8 included studies used the KGRS in disease decision support 12,14,15,19,[22][23][24]27 . The KGRS was mainly used to provide a good environment and technical basis for sharing and disseminating medical knowledge, standardizing treatments, and providing scientific advices for clinical decision making.…”

“…The results all showed good model performance 12,[14][15][16][17][18][19][20][22][23][24][25]27 . In addition, 7 studies utilized questionnaires, case, follow-up, or pilot studies based on performance metrics to provide a comprehensive and systematic assessment of KGRS performance from the user's perspectives 12,14,15,17,18,21,22 . For example, Shang et al 14 evaluated the performance of the KGRS as an aid for chronic kidney disease diagnosis in a follow-up study.…”

“…Among the 16 included studies, 13 studies evaluated KGRS using performance metrics, such as accuracy, recall, F1 scores, and method of area under the curve (AUC). The results all showed good model performance 12,[14][15][16][17][18][19][20][22][23][24][25]27 . In addition, 7 studies utilized questionnaires, case, follow-up, or pilot studies based on performance metrics to provide a comprehensive and systematic assessment of KGRS performance from the user's perspectives 12,14,15,17,18,21,22 .…”