Objective To evaluate the effect of nurse-led follow-up on medication adherence and quality of life for breast cancer patients receiving adjuvant hormonal therapy.Methods A randomized controlled trial was conducted with 157 patients in the intervention group and 154 in the control group.A self-design web-database medication monitoring platform was designed for managing patients such as texting,reminding and mailing.Participants were randomized to follow-up care as usual(yearly outpatient clinic visits) or nurse-led telephone follow-up(monthly consultation with structured intervention).Telephone follow-up was performed by four trained breast care nurses (BCN) and consisted of a semi-structured interview including managing the side-effects of endocrine therapy,compliance with hormonal therapy and an open discussion of these issues.Patients' medication adherence and quality of life were evaluated by Morisky Medication Adherence Questionnaire(MAQ) and FACT-B at baseline and 3,6 18 and 24 months,respectively.Results The Nurse-led telephone follow-up did not significantly improve the quality of life(P＞0.05).MAQ score in the intervention group was significantly greater than that in the control group(P＜0.05) at 3,6,18 and 24 months.Conclusion Nurse-led follow-up using computer-based monitoring system can improve patients' medication adherence,but there is no obvious increase in quality of life.

Objective To explore the influence factors of medication adherence of hormonal therapy for breast cancer patients .Methods In-depth interviews were conducted to 15 healthcare professionals . Results The results of in-depth interviews for breast cancer experts showed that medication adherence of hormonal therapy of breast cancer patients was influenced by multiple factors , including patient-related, condition-related, therapy-related, family related and health system related factors .The balance between side effects and efficacy were the key point of medication adherence for breast cancer patients .The awareness on the importance of hormonal therapy was insufficient among breast cancer patients .Lack of dynamic monitoring indicators and channels to communicate with health professionals were the most significant influence on the adherence to hormonal therapy .Conclusions It is recommended that individualized and effective interventions should be implored to improve the adherence to hormonal therapy for breast cancer patients .

ObjectiveIn order to improve the recognition accuracy of named entities in medical record texts and realize the effective mining and utilization of medical record knowledge， a Bert-Radical-Lexicon（BRL） neural network model is constructed to recognize medical record entities with respect to the characteristics of medical record texts. MethodWe selected 408 medical records related to hypertension from the the Complete Library of Famous Medical Records of Chinese Dynasties and constructed a dataset consisting of 1 672 medical records by manually labeling. Then， we randomly divided the dataset into three subsets， including the training set（1 004 cases）， the testing set （334 cases） and the validation set（334 cases）. Based on this dataset， we built a BRL model that fused various text features of medical records， as well as its variants BRL-B， BRL-L and BRL-R， and a baseline model Base for experiments. During the model training phase， we trained the above models using the training set to reduce the risk of overfitting. We continuously monitored the performance of each model on the validation set during training and saved the model with the best performance. Finally， we evaluated the performance of these models on the testing set. ResultCompared with other models， the BRL model had the best performance in the medical records named entity recognition task， with an overall recognition precision of 90.09%， a recall of 90.61%， and the harmonic mean of the precision and recall（F1） of 90.35% for eight types of entities， including disease， symptom， tongue manifestation， pulse condition， syndrome， method of treatment， prescription and traditional Chinese medicine（TCM）. Compared with the Base model， the BRL model improved the overall F1 value of entity recognition by 5.22%， and the F1 value of pulse condition entity increased by 6.92%， which was the largest increase. ConclusionBy incorporating a variety of medical record text features in the embedding layer， the BRL neural network model has stronger named entity recognition ability， and thus extracts more accurate and reliable TCM clinical information.