RESUMO
The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important corner-stone technology of natural language processing - has been studied well in the general medical field. However, there is still a lack of effective NER methods to handle diabetes data. Briefly, there are three challenges in the real world, including 1) the large volume of diabetes-related data to be processed, 2) the lack of labeled data, and 3) the high costs of manual labeling. To mitigate those challenges, this paper proposes a novel NER method based on semi-supervised learning, namely SNER, for diabetes data processing. It utilizes large amounts of unlabeled data to solve the problem of lack of labeled data. Specifically, it filters the predicted labels based on their confidence and uncertainty scores to reduce the noise entering the model and divide them into positive pseudo-labels and negative pseudo-labels. Also, it utilizes negative pseudo-labels reasonably to improve the training effect of pseudo-labels. Experiments on two public diabetes datasets show that SNER achieves the best performance compared with existing state-of-the-art models.
RESUMO
BACKGROUND: Evidence of a role for type 2 diabetes in overall cancer risk is limited in ethnic Chinese populations. We therefore investigated whether there is an association between diabetes and cancer incidence. METHODS: All type 2 diabetes and cancer hospitalized patients from the First Affiliated Hospital of Nanjing Medical University between 2006.01 and 2013.12 were eligible for the study. Our research used healthcare information technology and statistical methods to analyze the clinical data of hospitalized patients and explored the relationship between diabetes and cancer. Participants with fasting glucose ≥126 mg/dL, or taking hypoglycemic medications, were classed as having type 2 diabetes. Cancer incidence was established through regular follow-up interviews and medical records. Data were entered into Excel and a database was set up with ACCESS software. Clinical information such as demographics like gender, age, occupation, marriage, insurance and etc., diagnoses, and prescription record were chosen and analyzed. SPSS software was also used for statistical analysis. RESULTS: The number of patients with both diabetes and cancer rose from 220 cases in 2006 to 1,623 cases in 2013. The proportion of cancer patients with diabetes has also increased every year. Younger participants (aged ≤50 years) with diabetes had a greater risk of all cancers [P<0.005, odds ratio (OR) >3.4]. And cancer patients with diabetes occurs more frequently in male patients than in female patients, especially since 2009 the proportion has increased more evidently (P<0.005, OR >1.4). Further analysis showed that the level of blood lipid in patients with diabetes mellitus and cancer was significantly different from that in patients with simple diabetes mellitus (P<0.05). CONCLUSIONS: Our results clearly demonstrate a positive association between diabetes and cancer, especially in younger individuals aged less than 50 years. This finding highlights a need for greater awareness among public health workers and physicians of the importance of effective control of diabetes in the younger population.