106 research outputs found
Structural Effect in Ionic Liquids Is the Vital Role to Enhance the Corrosion Protection of Metals in Acid Cleaning Process
Various kinds of methods have been developed and used to overcome different types of corrosion throughout the world. One possible and easy way to avert corrosion is use of an inhibitor. An inhibitor can be applicable to any type of metal irrespective of medium (acid, alkaline, and neutral). Still, several inhibitors are emerging day by day in the corrosion world and most of them are heterocyclic compounds. In this respect, ionic liquid is attracting the attention of the research community. Because of ionic liquid’s salient feature of melting and boiling points, it is being employed as a solvent in various types of reaction. In recent years, synthesizing and functionalizing the structure of ionic liquids in such a way to attain the desire requirement have become significant key factors in the field. By altering the cationic part or anionic part (halogen group), the chemical property of ionic liquids will change considerably. Besides, it will enhance the tendency of the electron-donating nature of the cationic part. This behavior equips them to be employed in the field of corrosion. While it meets the metal surface in the aggressive medium it will be attracted, leads to better surface protection from metal dissolution
Comparative Study on Multivariate Methods Using Chronic Kidney Disease
The human being is currently one of the most serious illnesses in the modern world, and accurate diagnosis is necessary as soon as possible. In this modern world, there are numerous diseases that exist. Chronic kidney disease is regarded as the most serious of these disorders in humans. There are several methods in the medical area for disease diagnosis, and the prediction criterion is also significant in the medical field for determining the consequences of the study in the future. Many statistical methods are employed in order to forecast the medical dataset and provide accurate and reliable findings. A lot of models are available in multivariate methods to predict the dataset. In this paper, the computational algorithms for detecting CKD using Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR) are reviewed. The first, based on the association, inference for the study. Decision tree and logistic regression approaches are used to more correctly diagnose chronic renal disease based on the results of the association. Finally, the study came to the conclusion that greatest fit for forecasting chronic renal disease
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
Factual correctness is often the limiting factor in practical applications of
natural language generation in high-stakes domains such as healthcare. An
essential requirement for maintaining factuality is the ability to deal with
rare tokens. This paper focuses on rare tokens that appear in both the source
and the reference sequences, and which, when missed during generation, decrease
the factual correctness of the output text. For high-stake domains that are
also knowledge-rich, we show how to use knowledge to (a) identify which rare
tokens that appear in both source and reference are important and (b) uplift
their conditional probability. We introduce the ``utilization rate'' that
encodes knowledge and serves as a regularizer by maximizing the marginal
probability of selected tokens. We present a study in a knowledge-rich domain
of healthcare, where we tackle the problem of generating after-visit care
instructions based on patient-doctor dialogues. We verify that, in our dataset,
specific medical concepts with high utilization rates are underestimated by
conventionally trained sequence-to-sequence models. We observe that correcting
this with our approach to knowledge injection reduces the uncertainty of the
model as well as improves factuality and coherence without negatively impacting
fluency.Comment: ACL 2023 (main conference
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Medical conversations between patients and medical professionals have
implicit functional sections, such as "history taking", "summarization",
"education", and "care plan." In this work, we are interested in learning to
automatically extract these sections. A direct approach would require
collecting large amounts of expert annotations for this task, which is
inherently costly due to the contextual inter-and-intra variability between
these sections. This paper presents an approach that tackles the problem of
learning to classify medical dialogue into functional sections without
requiring a large number of annotations. Our approach combines pseudo-labeling
and human-in-the-loop. First, we bootstrap using weak supervision with
pseudo-labeling to generate dialogue turn-level pseudo-labels and train a
transformer-based model, which is then applied to individual sentences to
create noisy sentence-level labels. Second, we iteratively refine
sentence-level labels using a cluster-based human-in-the-loop approach. Each
iteration requires only a few dozen annotator decisions. We evaluate the
results on an expert-annotated dataset of 100 dialogues and find that while our
models start with 69.5% accuracy, we can iteratively improve it to 82.5%. The
code used to perform all experiments described in this paper can be found here:
https://github.com/curai/curai-research/tree/main/functional-sections.Comment: Changed the github link as it was invali
Discovering Topical Aspects in Microblogs
Abstract We address the problem of discovering topical phrases or "aspects" from microblogging sites like Twitter, that correspond to key talking points or buzz around a particular topic or entity of interest. Inferring such topical aspects enables various applications such as trend detection and opinion mining for business analytics. However, mining high-volume microblog streams for aspects poses unique challenges due to the inherent noise, redundancy and ambiguity in users' social posts. We address these challenges by using a probabilistic model that incorporates various global and local indicators such as "uniqueness", "diversity" and "burstiness" of phrases, to infer relevant aspects. Our model is learned using an EM algorithm that uses automatically generated noisy labels, without requiring manual effort or domain knowledge. We present results on three months of Twitter data across different types of entities to validate our approach
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