Radiology report generation through chest radiography interpretation is a time-consuming task that involves the interpretation of images by expert radiologists. It is common for fatigue-induced diagnostic error to occur, and especially difficult in areas of the world where radiologists are not available or lack diagnostic expertise. In this research, we proposed a multi-objective deep learning model called CT2Rep (Computed Tomography to Report) for generating lung radiology reports by extracting semantic features from lung CT scans. A total of 458 CT scans were used in this research, from which 107 radiomics features and 6 slices of segmentation related nodule features were extracted for the input of our model. The CT2Rep can simultaneously predict position, margin, and texture, which are three important indicators of lung cancer, and achieves remarkable performance with an F1-score of 87.29%. We conducted a satisfaction survey for estimating the practicality of CT2Rep, and the results show that 95% of the reports received satisfactory ratings. The results demonstrate the great potential in this model for the production of robust and reliable quantitative lung diagnosis reports. Medical personnel can obtain important indicators simply by providing the lung CT scan to the system, which can bring about the widespread application of the proposed framework.

Evidence for clinical screening and intervention for depression in cancer and the effect of this intervention on cancer prognosis is suboptimal. This study substantialized a complete model with universal screening and intervention for major depressive disorder (MDD) and explored its effect on survival in patients. This longitudinal study recruited cancer patients routinely screened for MDD with a two-stage model. Data including sex, age, cancer diagnosis, first diagnosis date, date of death, cancer stage, and MDD diagnosis and treatment were collected from medical records and the national registration system for cancer. Kaplan–Meier’s survival analysis and the Cox proportional hazards regression model were applied to analyze the effects of associated factors on survival. Further subgroup analysis for 14 types of cancer primary site was also performed. Overall, the hazard for patients adhering to psychiatric treatment for MDD before cancer diagnosis was not statistically different from that for patients without MDD (hazard ratio (HR) = 1.061, 95% CI: 0.889–1.267, p = 0.512). The hazard for patients adhering to psychiatric treatment after cancer diagnosis was significantly lower than that for patients without MDD (HR = 0.702, 95% CI: 0.607–0.812, p < 0.001). Those who were diagnosed with MDD after cancer diagnosis and adhered poorly to psychiatric treatment had the greatest hazard (HR = 1.829, 95% CI: 1.687–1.984, p < 0.001). The effect of intervention for MDD varied across different primary cancer types.

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system’s performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.

BACKGROUND Globalization and environmental changes have increased the emergence and re-emergence of infectious diseases worldwide. The collaboration of regional infectious disease surveillance systems is critical but difficult to achieve because of the different transparency levels of health information sharing systems among countries. ProMED-mail is the most comprehensive expert-curated platform that provides rich outbreak information among humans, animals, and plants from different countries. However, owing to unstructured text content in reports, it is difficult to analyze them for further applications. Therefore, we have devised an idea to develop an automatic summary of the alerting articles from ProMED-mail. In this research, we propose a text summarization method that uses natural language processing to extract important sentences automatically from alert articles in ProMED emails to generate summaries of dengue outbreaks in Southeast Asia. Our method, can be used to capture crucial information quickly and make decisions for epidemic surveillance. OBJECTIVE To generate automatic summaries of unstructured text content from reports. METHODS Our materials come from the ProMED-mail website, spanning a period from 1994 to 2019. The collected data were annotated by professionals to establish a unique Taiwan dengue corpus through, which achieved almost perfect agreement (90% Cohen’s Kappa statistic). To generate a ProMED-mail summary, we developed a dual-channel bidirectional long-short term memory with an attention mechanism that infuses latent syntactic features to identify crucial sentences from the alerting articles. RESULTS Our method is superior to many well-known machine learning and neural network approaches in identifying important sentences, achieving a macro average F1-score of 93%. Moreover, the method can successfully extract key information about dengue fever outbreaks in ProMED-mail, and help researchers or public health practitioners to capture important summaries quickly. Besides verifying the model, we also recruited five professional experts and five students from related fields to carry out a satisfaction survey on the generated summary. The results showed that 83.6% of the summaries received high satisfaction ratings. CONCLUSIONS The proposed approach successfully fuses latent syntactic features into a deep neural network to analyze syntactic, semantic, and content information in the text. It then exploits the derived information to identify the crucial sentences in ProMED-mail. The experimental results show that the proposed method is effective and outperforms the comparisons. In addition, our method demonstrated the potential for summary generation from ProMED-mail. When a new alerting article arrives, public health decision makers can identify the outbreak information in a lengthy article quickly and deliver immediate responses to disease control and prevention. CLINICALTRIAL NA