With the rapid development of artificial intelligence technology, building a medical knowledge graph has become one of the important tasks in the field of modern medicine. Medical knowledge graph is a structured knowledge storage and representation framework that plays an important role in medical research and clinical practice. Building a medical knowledge graph can help doctors better understand and apply medical knowledge and improve the diagnosis and treatment of diseases. This article will introduce the three stages of constructing a medical knowledge graph, including automatically extracting medical knowledge by machine, collecting doctor’s experience as a supplement, and using audio interpretation robots to automatically collect clinical diagnosis and treatment audio.
The first stage: the machine automatically extracts medical knowledge from existing corpus
The first step in building a medical knowledge graph is to automatically extract medical knowledge from existing medical corpus. These corpora can include medical books, guides, electronic medical records, and drug instructions. Through technologies such as natural language processing and machine learning, machines can automatically analyze and extract relevant information from these corpus and store it in a structured knowledge graph. For example, machines can identify key information such as disease names, disease symptoms, drug uses and dosages, thereby establishing medical entities and the relationships between them.
The second stage: collecting unwritten doctor’s experience as a supplement
In addition to extracting medical knowledge from existing corpora, building a medical knowledge graph also requires considering the rich experience of doctors. Doctors have accumulated a large amount of valuable experience in long-term clinical practice, which is often difficult to store and share in traditional ways. Therefore, the focus of the second phase is to collect and organize their unwritten experiences through communication and cooperation with doctors. Doctors’ insights, techniques, and treatment strategies in specific fields can be collected through face-to-face interviews, questionnaires, and expert discussions, and added to the knowledge map. This can make full use of doctors' clinical experience and provide more comprehensive and in-depth content for the knowledge graph.
The third stage: automatically collect clinical diagnosis and treatment audio through hardware equipment-clinic listening and interpretation robot
The third stage of building a medical knowledge graph is to use hardware devices, such as clinic listening and interpretation robots, to automatically collect clinical diagnosis and treatment audio. With the advancement of technology, the diagnosis and treatment process in the clinic can be recorded and analyzed in real time by smart devices. The listening and interpretation robot can convert the conversation between doctors and patients into text information through speech recognition and natural language processing technology, and add it to the medical knowledge graph. Doing so captures rich clinical practice data, including case descriptions, symptom presentation, diagnostic procedures, and treatment options. By analyzing these data, deeper medical knowledge and patterns can be explored, thereby improving doctors’ diagnostic capabilities and treatment effects.
In short, building a medical knowledge graph is a multi-stage process. First, the machine can automatically extract medical knowledge from existing corpus and establish medical entities and relationships. Then, through communication and cooperation with doctors, we collect their unwritten experiences and enrich the content of the knowledge map. Finally, use hardware devices, such as listening and interpretation robots, to automatically collect clinical diagnosis and treatment audio and capture conversation information between doctors and patients. These three stages complement each other and jointly build a rich and comprehensive medical knowledge map. By building a medical knowledge graph, the level of research and practice in the medical field can be improved, and the quality of medical services and patient health status can be further improved.