Purpose/Significance The paper discusses the application of artificial intelligence technology to the key entity recognition ofunstructured text data in the electronic medical records of lymphedema patients.Method/Process It expounds the solution of model fine-tuning training under the background of sample scarcity,a total of 594 patients admitted to the department of lymphatic surgery of Beijing Shijitan Hospital,Capital Medical University are selected as the research objects.The prediction layer of the GlobalPointer model is fine-tuned according to 15 key entity categories labeled by clinicians,nested and non-nested key entities are identified with its glob-al pointer.The accuracy of the experimental results and the feasibility of clinical application are analyzed.Result/Conclusion After fine-tuning,the average accuracy rate,recall rate and Macro_F1 ofthe model are 0.795,0.641 and 0.697,respectively,which lay a foundation for accurate mining of lymphedema EMR data.

Despite the achievements obtained worldwide in the control of tuberculosis in recent years, many countries and regions including China still face challenges such as low diagnosis rate, high missed diagnosis rate, and delayed diagnosis of the disease. The discovery strategy of tuberculosis in China has changed from "active discovery by X-ray examination" to "passive discovery by self-referral due to symptoms", and currently the approach is integrated involving self-referral due to symptoms, active screening, and physical examination. Active screening could help to identify early asymptomatic and untreated cases. With the development of molecular biology and artificial intelligence-assisted diagnosis technology, there are more options for active screening among the large-scale populations. Although the implementation cost of a population-based active screening strategy is high, it has great value in social benefits, and active screening in special populations can obtain better benefits. Active screening of tuberculosis is an important component of the disease control. It is suggested that active screening strategies should be optimized according to the specific conditions of the regions to ultimately ensure the benefit of the tuberculosis control.
Humans ; Artificial Intelligence ; Tuberculosis/prevention & control* ; Mass Screening ; China