Background: Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)–based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications. Objective: This study aims to meticulously evaluate the performance of various NER models in the context of medical text analysis, focusing on how complex medical terminology affects entity recognition accuracy. Additionally, we explored the influence of macrofactors on model performance, seeking to provide insights for refining NER models and enhancing their reliability for medical applications. Methods: This study comprehensively evaluated 7 NER models—hidden Markov models, conditional random fields, BERT for Biomedical Text Mining, Big Transformer Models for Efficient Long-Sequence Attention, Decoding-enhanced BERT with Disentangled Attention, Robustly Optimized BERT Pretraining Approach, and Gemma—across 3 medical datasets: Revised Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), BioCreative V CDR, and Anatomical Entity Mention (AnatEM). The evaluation focused on prediction accuracy, resource use (eg, central processing unit and graphics processing unit use), and the impact of fine-tuning hyperparameters. The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. Results: The fine-tuned BERT for Biomedical Text Mining, with balanced resource use, generally achieved the highest prediction accuracy across the Revised JNLPBA and AnatEM datasets, with microaverage (AVG_MICRO) scores of 0.932 and 0.8494, respectively, highlighting its superior proficiency in identifying medical entities. Gemma, fine-tuned using the low-rank adaptation technique, achieved the highest accuracy on the BioCreative V CDR dataset with an AVG_MICRO score of 0.9962 but exhibited variability across the other datasets (AVG_MICRO scores of 0.9088 on the Revised JNLPBA and 0.8029 on AnatEM), indicating a need for further optimization. In addition, our analysis revealed that 2 macrofactors, entity phrase length and the number of entity words in each entity phrase, significantly influenced model performance. Conclusions: This study highlights the essential role of NER models in medical informatics, emphasizing the imperative for model optimization via precise data targeting and fine-tuning. The insights from this study will notably improve clinical decision-making and facilitate the creation of more sophisticated and effective medical NER models.

Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI’s development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI’s actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation.

Background: With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem. Objective: This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics. Methods: This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer. Results: Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of “rationality of schema structure,” “scalability,” and “readability of results,” the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its “practicability” score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor. Conclusions: We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG’s potential.

Background: Although many studies have analyzed the mental health problems (MHP) of Chinese adolescents, the associations of Chinese high school students’ MHP with personal and family circumstances, parents’ educational level, physical activity, and school adaptation are rarely discussed. Methods: The participants were 9398 students who were randomly recruited from 30 high schools in Shandong, China. Self-reported questionnaires were used to collect data. Multivariate logistic regression models were used to investigate associations between MHP and related factors. Results: The positive rate of MHP among high school students was 27.0%. Female, intimate friends of the opposite sex, poor family economic status, father’s educational level of primary school or below, low social competence, and high antisocial behavior were significantly associated with higher odds of having MHP (p < 0.05). Students’ self-reported odds of having obsessive–compulsive disorder, interpersonal sensitivity, and depression were inversely related to their mother’s educational level. Compared with students who did not exercise, students who engaged in more than 30 min of physical activity 5–6 times per week had 60% lower self-reported odds of depression. Conclusions: We recommend strengthening the prevention, early detection, and treatment of MHP in high school students, especially those whose parents have low educational attainment, inactivity, and high disruptive behaviors.

Background: The rapid spread of the COVID-19 pandemic in the United States has made people uncertain about their perceptions of the threat of COVID-19 and COVID-19 response measures. To mount an effective response to this epidemic, it is necessary to understand the public's perceptions, behaviors, and attitudes. Objective: We aimed to test the hypothesis that people’s perceptions of the threat of COVID-19 influence their attitudes and behaviors. Methods: This study used an open dataset of web-based questionnaires about COVID-19. The questionnaires were provided by Nexoid United Kingdom. We selected the results of a questionnaire on COVID-19–related behaviors, attitudes, and perceptions among the US public. The questionnaire was conducted from March 29 to April 20, 2020. A total of 24,547 people who lived in the United States took part in the survey. Results: In this study, the average self-assessed probability of contracting COVID-19 was 33.2%, and 49.9% (12,244/24,547) of the respondents thought that their chances of contracting COVID-19 were less than 30%. The self-assessed probability of contracting COVID-19 among women was 1.35 times that of males. A 5% increase in perceived infection risk was significantly associated with being 1.02 times (OR 1.02, 95% CI 1.02-1.02; P<.001) more likely to report having close contact with >10 people, and being 1.01 times (OR 1.01, 95% CI 1.01-1.01; P<.001) more likely to report that cohabitants disagreed with taking steps to reduce the risk of contracting COVID-19. However, there was no significant association between participants who lived with more than 5 cohabitants or less than 5 cohabitants (P=.85). Generally, participants who lived in states with 1001-10,000 COVID-19 cases, were aged 20-40 years, were obese, smoked, drank alcohol, never used drugs, and had no underlying medical conditions were more likely to be in close contact with >10 people. Most participants (21,017/24,547, 85.6%) agreed with washing their hands and maintaining social distancing, but only 20.2% (4958/24,547) of participants often wore masks. Additionally, male participants and participants aged <20 years typically disagreed with washing their hands, maintaining social distancing, and wearing masks. Conclusions: This survey is the first attempt to describe the determinants of the US public’s perception of the threat of COVID-19 on a large scale. The self-assessed probability of contracting COVID-19 differed significantly based on the respondents’ genders, states of residence, ages, body mass indices, smoking habits, alcohol consumption habits, drug use habits, underlying medical conditions, environments, and behaviors. These findings can be used as references by public health policy makers and health care workers who want to identify populations that need to be educated on COVID-19 prevention and health.

Background: The trend of physical fitness (PF) and physical activity (PA) among Chinese adolescents is not optimistic, and unhealthy dietary behaviors are common. PA and dietary patterns (DPs) have been linked to PF in adolescents, but the associations between DPs and PF with PF in Chinese adolescents are rarely discussed. Methods: A total of 8796 adolescents aged 11–18 were enrolled from Shandong Province, China. The CNSPFS battery was applied to assess PF. PA levels and diet quality were determined using the Physical Activity Questionnaire for Adolescents and the modified Chinese Diet Quality Questionnaire, respectively. This study used factor analysis to identify DPs and linear regression models to investigate the association between PF and related factors. Results: The average PF score of the participants was 75.67. Adolescents who were girls, lived in rural areas and were active in PA performed better on the PF test (p < 0.05). Boys whose fathers were university educated or above had a higher probability of achieving higher PF scores (OR 4.36, 95% CI 1.32–14.36); however, if their mothers were university educated or above, they had a lower probability of achieving higher PF scores (OR 0.22, 95% CI 0.063–0.76). Unhealthy dietary pattern was negatively correlated with cardiorespiratory fitness in boys (OR 0.56, 95% CI 0.31–0.98). The association between unhealthy dietary pattern and girls’ BMI became significant after adjustment for PA (p < 0.05). Conclusions: Girls performed better in PF than boys. Highly educated fathers could contribute to improve the PF performance in boys. There were four DPs among adolescents in Shandong Province, and different DPs may have different effects on PF in boys and girls.