Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

BACKGROUND: Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first. OBJECTIVE: We aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation-that is, creating lay definitions for these terms. METHODS: Adapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data. RESULTS: The ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target-domain training examples. The 2 ADS systems both performed significantly better than the baseline systems (P \u3c .001 for all measures and all conditions). Using a rich set of learning features contributed to ADS\u27s performance substantially. CONCLUSIONS: ADS can effectively rank terms mined from EHRs. Transfer learning improved ADS\u27s performance even with a small number of target-domain training examples. EHR terms prioritized by ADS were used to expand a lay language resource that supports patient EHR comprehension. The top 10,000 EHR terms ranked by ADS are available upon request

An update of additive manufacturing (3D printing) technology in dentistry.

Background: The development of procedures known as additive manufacturing, which aim to produce more complex items with a lower overall material consumption compared to processes known as subtractive manufacturing. In addition, in recent years there has been a significant rise in the quantity of dental materials that are produced via the use of these techniques. As a consequence of this, scientific research has been concentrating more and more on such technologies, particularly in order to shed light on the methodology, indicators, and boundaries of the emerging technology. Methods: The purpose of this paper is to provide a narrative assessment of the state-of-the-art in the area of these popular additive manufacturing methods, as well as the appropriate dental applications, by using scientific literature analysis and references to the authors\u27 clinical experience. In addition, the purpose of this study is to evaluate the appropriate dental applications. Results: The end result was a tremendous amount of data, most of it is conflicting, is now available for viewing. In tests conducted both in vitro and in vivo, the following additive manufacturing procedures were shown to be effective: Milling results in a number of negative side effects, including the loss of material, increased costs associated with equipment maintenance, and wasted production time. Additive manufacturing, often known as 3D printing, allows for the production of prostheses and models at a quicker rate and with less waste material. Conclusions: In order to successfully manufacture complex component geometries, CAM configuration and process design must be carefully considered. As a consequence of this, the speed at which the process is carried out is of equal importance to the interaction between the individual components. When dealing with geometry that is more complicated, 3D printing beats CAM

Fracture strength of endocrown maxillary restorations using different preparation designs and materials.

PURPOSE This study investigated the impact of preparation design and material types on fracture strength in maxillary premolars endocrowns after thermodynamic aging. MATERIALS AND METHODS Eighty two-rooted maxillary premolar crowns underwent endodontic treatment (N = 80, n = 10). The teeth were categorized into ten groups (4-mm deep with no intracanal extension lithium disilicate glass ceramic & multilayer zirconia endocrowns (LE0 & ZE0); 4-mm deep with 4-mm intracanal extension in one canal (LE1 & ZE1); 4-mm deep with 2-mm intracanal extensions in both canals (LE2 & ZE2); flat overlays with no endocore (LO & ZO); glass fiber reinforced post & core and crown (LC & ZC)). After cementation, all specimens were subjected to 1500 thermocycles and 1,200,000 chewing cycles with an axial occlusal load of 49 N. A static loading test was performed at a non-axial 45° loading using a universal testing machine and failure modes (Type I: restoration debonding; Type II: restoration fracture; Type III: restoration/tooth complex fracture above bone level; Type IV: restoration/tooth complex fracture below bone level) were evaluated using a stereoscope. Data were ananalzed using 2-way ANOVA and Tukey's tests (alpha = 0.05). RESULTS The endocrowns manufactured from multilayered zirconia and pressed lithium disilicate glass ceramic exhibited a fracture load ranging between 1334 ± 332 N and 756 ± 150 N, with ZC presenting the highest and LE2 the lowest values. The differences were not statistically significant (p > 0.05). CONCLUSION All endocrowns tested in this study performed similar considering the different designs and materials tested. The distribution of fracture modes did not differ significantly depending on the design of the restoration and the type of material used

Blended learning in undergraduate dental education: a global pilot study.

AIMS: To explore the global trends in blended learning in undergraduate dental education during the COVID pandemic and during the recovery phase by engaging with the students and faculty and evaluate the implications for dental education in the post-COVID era. METHODS: It was a pilot cross-sectional study which employed a convenience sampling technique to recruit representatives of dental faculty and undergraduate students in 80 dental institutions globally. A previously validated questionnaire consisting of a combination of closed and open-ended items was used for data collection. Responses to these online questionnaires were processed and analysed using the R statistical computing environment. RESULTS: A total of 320 dental students and 169 faculty members from 47 different dental institutions participated in the study. Video and Live Online Tutorials were considered to be the most effective method of online learning followed by online question banks by both groups. Significant differences were noted between faculty and students regarding time spent and effectiveness of online teaching and learning, respectively, both before and after the start of COVID. The results highlight the faculty need to engage more closely with the students to address their learning needs. Finally, the participants provided several recommendations regarding the future development of teaching and learning strategies as well as assessments in the post-pandemic era. CONCLUSIONS: This is the first study which explores blended learning in dental education with participants from multiple institutions in different regions of the globe. Compared to the faculty, students considered online learning to be less interactive and preferred learning activities and all assessments to be delivered face-to-face. The results underscore the need to adapt teaching practices to suit the learning needs of the students

Quality, effectiveness and outcome of blended learning in dental education during the COVID pandemic: Prospects of a post-pandemic implementation

Abstract: BackgroundBlended learning is growing in popularity particularly following the emergence of COVID-19 pandemic. One of the fields that the pandemic has substantially affected is dental education. Purpose: The aim of this study was to evaluate the quality and effectiveness of the online dental education. Students’ perceptions and experiences of blended learning were also investigated. Methods: A 28-question online survey was designed to gauge students’ perceptions of the effect of blended learning on their academic performance. Results: 314 participants in preclinical and clinical years completed the questionnaire (223 females and 91 males). The majority of students (89%) believed that clinical and practical courses cannot be given by the internet. In terms of students’ opinion in the assessment process, more females (65.8%) preferred traditional exams than males (50.5%) (p < 0.05). Most clinical students (83%) preferred a combination of online and traditional teaching compared to 72% of preclinical students (p < 0.05). Clinical year students were more willing to communicate electronically with their classmates and instructors. The majority of dental students (65%) reported that future dental courses should be blended. Conclusions: In the pandemic era, blended learning, should become the preferred method of education whereby theoretical knowledge is delivered through online tutorials and clinical training is resumed on-site, to ensure competency of dental graduates while maintaining safety of the dental team. Current facilities and course designs should be improved in order to improve students’ experiences with blended learning

Automatic extraction of informal topics from online suicidal ideation

Abstract Background Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users. Results In this work, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts. Our evaluation demonstrates that we are able to automatically reproduce many of the expertly determined risk factors for suicide. Moreover, we identify many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues. Conclusions These informal topics topics can be more specific or more general. Some of our topics express meaningful ideas not contained in the risk factors and some risk factors do not have complimentary latent topics. In short, our analysis of the latent topics extracted from social media containing suicidal ideations suggests that users of these systems express ideas that are complementary to the topics defined by experts but differ in their scope, focus, and precision of language.https://deepblue.lib.umich.edu/bitstream/2027.42/144214/1/12859_2018_Article_2197.pd

Frequent Closed Itemset Mining Using Prefix Graphs with an Efficient Flow-Based Pruning Strategy

This paper presents PGMiner, a novel graph-based algorithm for mining frequent closed itemsets. Our approach consists of constructing a prefix graph structure and decomposing the database to variable length bit vectors, which are assigned to nodes of the graph. The main advantage of this representation is that the bit vectors at each node are relatively shorter than those produced by existing vertical mining methods. This facilitates fast frequency counting of itemsets via intersection operations. We also devise several internode and intra-node pruning strategies to substantially reduce the combinatorial search space. Unlike other existing approaches, we do not need to store in memory the entire set of closed itemsets that have been mined so far in order to check whether a candidate itemset is closed. This dramatically reduces the memory usage of our algorithm, especially for low support thresholds. Our experiments using synthetic and real-world data sets show that PGMiner outperforms existing mining algorithms by as much as an order of magnitude and is scalable to very large databases. 1