An NLP Analysis of Health Advice Giving in the Medical Research Literature

Health advice – clinical and policy recommendations – plays a vital role in guiding medical practices and public health policies. Whether or not authors should give health advice in medical research publications is a controversial issue. The proponents of actionable research advocate for the more efficient and effective transmission of science evidence into practice. The opponents are concerned about the quality of health advice in individual research papers, especially that in observational studies. Arguments both for and against giving advice in individual studies indicate a strong need for identifying and accessing health advice, for either practical use or quality evaluation purposes. However, current information services do not support the direct retrieval of health advice. Compared to other natural language processing (NLP) applications, health advice has not been computationally modeled as a language construct either. A new information service for directly accessing health advice should be able to reduce information barriers and to provide external assessment in science communication. This dissertation work built an annotated corpus of scientific claims that distinguishes health advice according to its occurrence and strength. The study developed NLP-based prediction models to identify health advice in the PubMed literature. Using the annotated corpus and prediction models, the study answered research questions regarding the practice of advice giving in medical research literature. To test and demonstrate the potential use of the prediction model, it was used to retrieve health advice regarding the use of hydroxychloroquine (HCQ) as a treatment for COVID-19 from LitCovid, a large COVID-19 research literature database curated by the National Institutes of Health. An evaluation of sentences extracted from both abstracts and discussions showed that BERT-based pre-trained language models performed well at detecting health advice. The health advice prediction model may be combined with existing health information service systems to provide more convenient navigation of a large volume of health literature. Findings from the study also show researchers are careful not to give advice solely in abstracts. They also tend to give weaker and non-specific advice in abstracts than in discussions. In addition, the study found that health advice has appeared consistently in the abstracts of observational studies over the past 25 years. In the sample, 41.2% of the studies offered health advice in their conclusions, which is lower than earlier estimations based on analyses of much smaller samples processed manually. In the abstracts of observational studies, journals with a lower impact are more likely to give health advice than those with a higher impact, suggesting the significance of the role of journals as gatekeepers of science communication. For the communities of natural language processing, information science, and public health, this work advances knowledge of the automated recognition of health advice in scientific literature. The corpus and code developed for the study have been made publicly available to facilitate future efforts in health advice retrieval and analysis. Furthermore, this study discusses the ways in which researchers give health advice in medical research articles, knowledge of which could be an essential step towards curbing potential exaggeration in the current global science communication. It also contributes to ongoing discussions of the integrity of scientific output. This study calls for caution in advice-giving in medical research literature, especially in abstracts alone. It also calls for open access to medical research publications, so that health researchers and practitioners can fully review the advice in scientific outputs and its implications. More evaluative strategies that can increase the overall quality of health advice in research articles are needed by journal editors and reviewers, given their gatekeeping role in science communication

Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation

A cloud server spent a lot of time, energy and money to train a Viola-Jones type object detector with high accuracy. Clients can upload their photos to the cloud server to find objects. However, the client does not want the leakage of the content of his/her photos. In the meanwhile, the cloud server is also reluctant to leak any parameters of the trained object detectors. 10 years ago, Avidan & Butman introduced Blind Vision, which is a method for securely evaluating a Viola-Jones type object detector. Blind Vision uses standard cryptographic tools and is painfully slow to compute, taking a couple of hours to scan a single image. The purpose of this work is to explore an efficient method that can speed up the process. We propose the Random Base Image (RBI) Representation. The original image is divided into random base images. Only the base images are submitted randomly to the cloud server. Thus, the content of the image can not be leaked. In the meanwhile, a random vector and the secure Millionaire protocol are leveraged to protect the parameters of the trained object detector. The RBI makes the integral-image enable again for the great acceleration. The experimental results reveal that our method can retain the detection accuracy of that of the plain vision algorithm and is significantly faster than the traditional blind vision, with only a very low probability of the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul 14, 2017, Hong Kong, Hong Kon

CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic Segmentation

2D RGB images and 3D LIDAR point clouds provide complementary knowledge for the perception system of autonomous vehicles. Several 2D and 3D fusion methods have been explored for the LIDAR semantic segmentation task, but they suffer from different problems. 2D-to-3D fusion methods require strictly paired data during inference, which may not be available in real-world scenarios, while 3D-to-2D fusion methods cannot explicitly make full use of the 2D information. Therefore, we propose a Bidirectional Fusion Network with Cross-Modality Knowledge Distillation (CMDFusion) in this work. Our method has two contributions. First, our bidirectional fusion scheme explicitly and implicitly enhances the 3D feature via 2D-to-3D fusion and 3D-to-2D fusion, respectively, which surpasses either one of the single fusion schemes. Second, we distillate the 2D knowledge from a 2D network (Camera branch) to a 3D network (2D knowledge branch) so that the 3D network can generate 2D information even for those points not in the FOV (field of view) of the camera. In this way, RGB images are not required during inference anymore since the 2D knowledge branch provides 2D information according to the 3D LIDAR input. We show that our CMDFusion achieves the best performance among all fusion-based methods on SemanticKITTI and nuScenes datasets. The code will be released at https://github.com/Jun-CEN/CMDFusion

Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

Recent advances in large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates the performance of LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples as proxies for two fundamental tasks in the clinical domain - classification and reasoning. The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice. The second task is causal relation detection from the biomedical literature. We compared LLMs with simpler models, such as bag-of-words (BoW) with logistic regression, and fine-tuned BioBERT models. Despite the excitement around viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks remained the best strategy. The simple BoW model performed on par with the most complex LLM prompting. Prompt engineering required significant investment.Comment: 28 pages, 2 tables and 4 figures. Submitting for revie

TESOL versus SLP Techniques for the Development of L2 English Pronunciation

This study investigated the techniques used in Teaching English of Speakers of Other Languages (TESOL) and Speech-language Pathology (SLP) fields for the development of English pronunciation. First, a four-week, one-on-one training (one hour twice a week) for three Chinese learners of English as a second language (ESL) was conducted, employing the application of minimal pairs within different approaches - TESOL and SLP - to examine their effectiveness on the accuracy of sound production. All together five sounds were targeted (i.e., the velar nasal /eta/, the voiceless interdental fricative /theta/, the voiced labiodental fricative /v/, the voiced labial-velar approximate /w/ and the voiced alveolar liquid /l/). Second, a semi-structured interview was conducted to explore the applications of pronunciation teaching techniques and methods by professionals in each field. A number of areas were explored: practitioners\u27 understanding of pronunciation/speech sound development, their preparedness for pronunciation teaching, and their attitudes towards practitioners in the other field. Our findings from the first study suggested that when the technique of minimal pairs was applied within a segment-focused SLP approach, ESL participants were able to achieve higher accuracy in speech of shorter units such as passive task (i.e., tasks with scripts). In contrast, when minimal pairs were incorporated implicitly within a communicative, goal-orientated TESOL approach, ESL speakers were able to achieve better performance in active spontaneous productions (i.e., tasks without scripts). The second study showed that the TESOL professionals interviewed believed that intelligibility was more important than accuracy; while the SLP professionals maintained the incredible importance of accurate pronunciation. Finally, overall, SLP participants expressed stronger confidence in their readiness to teach both L1 and L2 English pronunciation than did TESOL professionals

Evaluation of Barley and Malt Quality in the Eastern Spring Barley Nursery

In the northeastern United States, craft beer is on the rise. With local brewing increasing, the supply of local raw materials becoming an urgent problem in some northeastern states, like Michigan, New York, Ohio, Pennsylvania, and Vermont. The overall goal of the project is to determine which cultivars are best adapted to specific regions in the northeastern United States, and to detect the impact of different environment factors on the barley genotypes. In general, cultivars from Europe had better resistance to pre-harvest sprouting (PHS) and lower beta-glucan levels than two-rowed cultivars developed in North America. The varieties, Explorer, LCS Genie, LCS Odyssey, KWS Fantex, and KWS Beckie are candidates for production in the eastern United States because of their higher levels of resistance to PHS and malt extract, and their lowers levels of beta-glucan

TESOL versus SLP Techniques for the Development of L2 English Pronunciation

This study investigated the techniques used in Teaching English of Speakers of Other Languages (TESOL) and Speech-language Pathology (SLP) fields for the development of English pronunciation. First, a four-week, one-on-one training (one hour twice a week) for three Chinese learners of English as a second language (ESL) was conducted, employing the application of minimal pairs within different approaches - TESOL and SLP - to examine their effectiveness on the accuracy of sound production. All together five sounds were targeted (i.e., the velar nasal /eta/, the voiceless interdental fricative /theta/, the voiced labiodental fricative /v/, the voiced labial-velar approximate /w/ and the voiced alveolar liquid /l/). Second, a semi-structured interview was conducted to explore the applications of pronunciation teaching techniques and methods by professionals in each field. A number of areas were explored: practitioners\u27 understanding of pronunciation/speech sound development, their preparedness for pronunciation teaching, and their attitudes towards practitioners in the other field. Our findings from the first study suggested that when the technique of minimal pairs was applied within a segment-focused SLP approach, ESL participants were able to achieve higher accuracy in speech of shorter units such as passive task (i.e., tasks with scripts). In contrast, when minimal pairs were incorporated implicitly within a communicative, goal-orientated TESOL approach, ESL speakers were able to achieve better performance in active spontaneous productions (i.e., tasks without scripts). The second study showed that the TESOL professionals interviewed believed that intelligibility was more important than accuracy; while the SLP professionals maintained the incredible importance of accurate pronunciation. Finally, overall, SLP participants expressed stronger confidence in their readiness to teach both L1 and L2 English pronunciation than did TESOL professionals

Perspectiva escolar

Después de dar una definición provisional del trabajo para proyectos y situar esta metodología en sus orígenes y en el marco actual, que presenta un uso más ambicioso, el autor expone los principales objetivos que se pueden esperar y beneficios que se pueden sacar.CataluñaUniversitat de Barcelona. Biblioteca de Ciències de l'Educació; Passeig de la Vall d'Hebron, 171; 08035 Barcelona; +34934021035; +34934021034;ES