Automatic Query Research Articles

A catalog of ethanol-producing microbes in humans.

Aim: Endogenous ethanol production emerges as a mechanism of nonalcoholic steatohepatitis, obesity, diabetes and auto-brewery syndrome. Methods: To identify ethanol-producing microbes in humans, we used the NCBI taxonomy browser and the PubMed database with an automatic query and manual verification. Results: 85 ethanol-producing microbes in human were identified. Saccharomyces cerevisiae, Candida and Pichia were the most represented fungi. Enterobacteriaceae was the most represented bacterial family with mainly Escherichia coli and Klebsiellapneumoniae. Species of the Lachnospiraceae and Clostridiaceae family, of the Lactobacillales order and of the Bifidobacterium genus were also identified. Conclusion: This catalog will help the study of ethanol-producing microbes in human in the pathophysiology, diagnosis, prevention and management of human diseases associated with endogenous ethanol production.

Rapid pre-typhoon safety inspection system for improving construction site resilience

Coastal cities are vulnerable to typhoon disasters due to their unique geographical location. Although significant progress has been made in the safety early warning system for construction sites, there are few studies on site safety management under typhoon disasters. Moreover, traditional manual methods of on-site emergency management are lagging behind and rigid, making it difficult to respond quickly to safety inspections manually. This study aims to automatically identify safety hazards and provide effective response measures during typhoon warnings, ultimately enhancing the safety of construction sites. Therefore, based on the MATLAB platform, this study developed a rapid safety inspection system (RSS-Typh) for the construction site during the typhoon warning period. In addition, the standard operating procedures (SOP) related to the system are formulated to assist the comprehensive safety inspection of the site before the typhoon and improve the inspection efficiency. The specific operation can be divided into three steps: 1.Using a combination of drones and handheld laser scanners to complete the complete construction site data acquisition in less than 2 h, and complete the modeling and refinement of the point cloud model in 5 h; 2.Using the developed hidden danger detection algorithm based on automatic point cloud to effectively identify the deformation-related hidden dangers at the construction site; 3.Use the designed automatic query security countermeasure GUI system to make rapid rectification. This study assists site managers in swiftly implementing effective safety management on construction sites, elevating the safety level of the site before typhoon. Furthermore, it serves to advance the automation of safety management in construction sites, promoting further developments in this field.

Urban noise exposure assessment based on principal component analysis of points of interest

Accurate qualitative and quantitative information on the characteristics of traffic noise exposure in densely populated urban areas is an important prerequisite for reasonable traffic noise control. The primary objective of this study is the development and application of a traffic noise exposure evaluation method based on points of interest (POIs). First, an automatic query arithmetic is used to acquire geospatial information, POIs data, building and network information from the webmap. Second, the attribute matrix of preprocessed POIs for the population is constructed. And the population distribution is obtained by principal component analysis (PCA) of POIs and Gaussian decomposition of demographic data. Then, the modified traffic noise line-source model is applied to calculate the noise distribution considering attenuation among buildings based on measured traffic flow parameters. Finally, with the help of the proposed noise evaluation indicators, and considering the noise function requirements (NFRs, which can be divided into four classes according to different area land-use types), traffic noise evaluation is realized. The proposed method is applied to a typical region with four NFR classes. It is concluded that the characteristics of traffic noise exposure are affected by traffic conditions, buildings, NFR classes and population distribution. And the crowds exposed to noise present aggregation effects, which are usually centered around specific buildings. In addition, POI types which people actives related suffer more serious noise exposure, and exposure is overestimated at low requirement regions without considering crowd distribution of the setting scenario.

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

Abstract: The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space Model (VSM), and Probabilistic Models, explaining their principles and applications. The study also presents experimental results highlighting the limitations of monolingual IR models and the effectiveness of crosslingual techniques, such as translation and query expansion, in improving CLIR, making it a valuable tool for accessing information across languages.

Developing a Semantically Based Query Recommendation for an Electronic Medical Record Search Engine: Query Log Analysis and Design Implications.

An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated "query recommendation" into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. The study demonstrated the potential contribution of the SBQR in shaping participants' positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

A Demonstration of DLBD: Database Logic Bug Detection System

Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.

Query Refinement into Information Retrieval Systems

Query, expressing the user need and requirement, has an important role, in aninformation retrieval system, for reaching a high accuracy search. In this paper, wepresent an overview of the different refinement operations that the query may undergo,in the sake to enhance performance of an information retrieval system, such as:automatic query formulation through words prevision, query reformulation, queryexpansion, and query optimization.

How the Quality of Maintenance Tasks is Affected by Criteria for Selecting Engineers for Collaboration

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.

Operating algorithms of the "TRAP" automated group measuring device controller

The article highlights the importance of measuring the production capacity of wells (oil, water, gas) in the process of operation to ensure efficient development of oil and gas fields. Modernization of hardware and software by the Institute of Control Systems of "Trap"-type automated group measuring devices (AGMU), widely used in the oil fields of the Azerbaijan Republic, is demonstrated. An overall block diagram of the operating algorithms compiled during implementation of the new controller module in the upgraded "Trap" control device for measuring oil-well production rate as well as its separate sub-components are given: Initial parameter setting service; Automatic and manual query identification service; Manual command execution service; Service connecting (i)-th well to the measuring tank; Service of monitoring the filling of the tank over time (mi); Service of disconnecting (i)-th well from the measuring tank and waiting for fluid settling; Production rate measurement and calculation service; Automatic query analysis and execution service, their algorithms are described, and the principles of their operation are explained.

A NOVEL EQUATION SUCCESSFULLY CALCULATES TIDAL VOLUMES FOR LUNG PROTECTIVE VENTILATION

BackgroundEarly application of low-tidal-volume ventilation (LTVV) has been associated with improved outcomes in the emergency department (ED) and intensive care unit (ICU), but is not consistently applied. The perceived complexity of calculating an ideal body weight (IBW)-based tidal volume (Vt) may contribute to this disparity. We hypothesized that a simplified equation could successfully predict LTVV. ObjectiveTo create a memorable, single-step, sex-independent equation to estimate LTVV based on height. MethodsWe conducted a retrospective observational cohort study of patients who received mechanical ventilation (MV) at 2 EDs from January 2016 to June 2019. Data were abstracted by automatic query. Patients < 18 years old, < 60 inches in height, and with implausible or incomplete data were excluded. LTVV was defined as ≤ 8 mL/kg IBW. We created a formula predicting a 6–8-mL/kg IBW Vt. We applied this formula to a population of ICU patients in the same health care system who received MV from January 2017 to December 2019 using the same exclusion criteria. The outcome was whether the equation predicted a 6–8-mL/kg IBW Vt. ResultsA total of 982 ED patients were included; 753 (76.7%) had an initial Vt < 8 mL/kg IBW. The equation Vt = 20*(Ht-60) + 300 was derived. A total of 3720 ICU patients were included. The Vt equation successfully predicted a Vt of 6–8 mL/kg IBW in 3720 (100%) of ICU patients. ConclusionsA novel equation successfully predicted a 6–8-mL/kg IBW Vt in a cohort of patients with height ≥ 60 inches.

Automated Question Generator using NLP

Abstract: The purpose of the Automated Paper Generator is to automatize the winning guide machine with the aid of the help of computerized gadgets and full-fledged laptop code, pleasurable their requirements, simply so their precious facts/data can be maintained for an extended quantity with clean gaining access to and manipulation of the desired software program and hardware are honestly available on the market and easy to discern with. automatic query Paper Generator, as represented above, will result in blunders loss, at ease, reliable, and brief control system. it can help the person to not forget their opportunity sports but rather to concentrate on file preserving. So, it's going to assist an organization in the higher utilization of sources. The enterprise will keep computerized records while not redundant entries. which means that one needn't be distracted by info it is now not applicable while having the ability to acquire the statistics. The goal is to automatize its existing guide gadget by way of the assistance of picked equipment and complete-fledged laptop software, pleasurable their requirements, just so their precious facts/statistics can be preserved for an extended quantity with sincere access to and manipulation of the equal. on the whole, the venture describes the manner to manipulate permanently overall performance and better offerings for the customers. Texts with capacity academic well-worth have ended up on the market through the net. however, the victimization of those new texts in lecture rooms introduces several challenges, one in every of which is that they typically lack compliance with physical activities and assessments. right here, we will be inclined to address part of this task by way of automating the creation of a specific form of assessment object and the use of various questions developing with taxonomies. specially, we concentrate on mechanically generating actual WH queries. Our purpose is to make an automatic gadget if you want to take as enter a text and flip out as output questions for assessing a reader's understanding of the records within the text. The questions would possibly then be supplied to a trainer, who would possibly pick and revise people who she or he judges to be beneficial.

Automatic SQL to HQL-NoSQL Querying using PostgreSQL and Integrated Hive-Hbase

The amount of digital data is constantly growing in almost all fields. This data is divided into two categories, structured and unstructured data. Non-structural databases known as NoSQL became one of the main fields of big data. Many companies are still using relational databases like PostgreSQL and MySQL. But with the rapid evolution and diversity of stored data, companies find themselves obliged to use big data tools like HBase or Hive. Big data is characterized by its capacity, speed, and ability to store diverse types of data. Data analysis and high storage capacity are the main reasons for companies to search for new database systems. Data migration to new systems is associated with the modification of the existing data and applications. This process costs a lot to adopt new specialists to handle this transition. Furthermore, due to different sources of data in old systems, e.g., real-time applications that are continuously collecting new data, companies will not be able to leave relational databases. For this reason, we present a system, termed Automatic Query Language, or AQL in short form, for migrating data from PostgreSQL to integrated HBase/Hive databases. In addition, we provide a platform that allows any user to query automatically PostgreSQL, Hive, and HBase databases using SQL query only. Querying the system is related to where each big data tool’s performance is better. After the platform was completed, we were able to insert and select data from both relational databases and big data components. Join operation was not a problem because complex queries for analysis were executed using Hive which was integrated with HBase. The tested AQL system proved that HBase can insert data with more efficiency than PostgreSQL and Hive, and that select query in Hive has a better performance than PostgreSQL for big data size, whereas, for small data size, the performance of PostgreSQL is better.

Achieving new SQL query performance levels through parallel execution in SQL Server

This article provides an in-depth look at implementing parallel SQL query processing using the Microsoft SQL Server database management system. It examines how parallelism can significantly accelerate query execution by leveraging multi-core processors and clustered environments. The article explores SQL Server's sophisticated parallel processing capabilities including automatic query parallelization, intra-query parallelism techniques like parallel joins and parallel data aggregation, as well as inter-query parallelism for concurrent query execution. It covers key considerations around effective parallelization such as managing concurrency and locks, handling data skew, resource governance, and monitoring. Challenges like debugging parallel plans and potential bottlenecks from excessive parallelism are also discussed along with mitigation strategies. Real-world examples demonstrate how judicious application of parallel processing helps optimize complex analytics workloads involving massive datasets. The insights presented provide guidance to database developers and administrators looking to enable parallel SQL query execution in SQL Server environments for substantial performance gains and scalability.

A Relative Information Gain-based Query Performance Prediction Framework with Generated Query Variants

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it is usually observed that the RSVs are often less reliable for QPP because they are bounded within short intervals, different from the situation for statistical models. To address this limitation, we propose a model-agnostic QPP framework that gathers additional evidences by leveraging information from the characteristic patterns of RSV distributions computed over a set of automatically generated query variants, relative to that of the current query. Specifically, the idea behind our proposed method—Weighted Relative Information Gain (WRIG), is that a substantial relative decrease or increase in the standard deviation of the RSVs of the query variants is likely to be a relative indicator of how easy or difficult the original query is. To cater for the absence of human-annotated query variants in real-world scenarios, we further propose an automatic query variant generation method. This can produce variants in a controlled manner by substituting terms from the original query with new ones sampled from a weighted distribution, constructed either via a relevance model or with the help of an embedded representation of query terms. Our experiments on the TREC-Robust, ClueWeb09B, and MS MARCO datasets show that WRIG, by the use of this relative changes in QPP estimate, leads to significantly better results than a state-of-the-art baseline method that leverages information from (manually created) query variants by the application of additive smoothing [ 64 ]. The results also show that our approach can improve the QPP effectiveness of neural retrieval approaches in particular.

932: A SIMPLIFIED EQUATION TO ESTIMATE TIDAL VOLUME FOR LUNG-PROTECTIVE VENTILATION

Introduction: Low tidal volume ventilation (LTVV) has been associated with improved patient-centered outcomes in the emergency department (ED) and intensive care unit (ICU). However, disparities exist in the application of LTVV. Shorter, female, and obese patients are less likely to receive appropriate tidal volumes (Vt). Misestimation of patient size, “preset” tidal volumes, and the perceived complexity of calculating an IBW-based Vt may contribute to this disparity. The current approach to calculating an IBW-based Vt may not always be practical. We hypothesized that a single-step height-based formula could successfully predict LTVV. Methods: We first conducted a retrospective observational cohort study of patients who received mechanical ventilation (MV) at 2 EDs from January 2016 to June 2019. An automatic query abstracted patient demographics, MV, and outcome data. Patients < 18 years old, < 60 inches in height (lower limit of the Devine formula), and with implausible or incomplete MV or demographic data were excluded. LTVV was defined as ≤ 8mL/kg IBW as calculated by the Divine formula. We then created a formula that predicted a 6-8 mL/kg IBW Vt. Next, we applied this formula to a population of ICU patients in the same healthcare system who received MV from January 2017 to December 2019 using the same exclusion criteria. The primary outcome was whether the proposed equation predicted a Vt of 6-8 mL/kg IBW. Results: We included a total of 982 ED patients; 753 (76.7%) had an initial Vt < 8 mL/kg IBW. Initial Vt was set at 450, 500, or 550 mL in 82.8% of patients. The following equation for VtL was derived: VtL = 20 x (Htin-60) + 300. A total of 3720 ICU patients were included; 2511 (64.9%) had an initial Vt < 8mL/kg IBW. The VtL equation successfully predicted a Vt of 6-8 mL/kg IBW in 3720 (100%) patients. Conclusions: A simplified, single-step height-based equation successfully predicted tidal volumes for LTVV in a cohort of ICU patients ≥ 60 inches independent of sex.

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

SummaryDue to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy‐based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean‐Average Precision, F‐measure, Precision‐recall, and Precision rank are evaluated and analyzed by using TREC‐COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state‐of‐art works. For a total of 60 queries, the proposed model offers an F1‐Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1‐score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

Towards semantic-driven boolean query formalization for biomedical systematic literature reviews

ObjectiveStudy identification refers to formalizing an effective search over biomedical databases for retrieving all eligible evidence for a systematic review. Manual construction of queries, where a user submit a search query for which a biomedical search system such as PubMed would identify the most relevant documents, has been recognized as a very costly step in conducting systematic reviews. The objective of this paper is to present an automatic query generation approach to reduce the time and labor cost of manual biomedical study identification. Materials and MethodsThe evaluation benchmark is the widely adopted CLEF 2018 Technology Assisted Reviews (TAR) collection, with 72 systematic reviews on Diagnosis Test Accuracy. We use and fine-tune pre-trained language models for generating high-level key-phrases and their dense embeddings. We constructed and published a dataset consists of almost one million PubMed articles’ abstracts and their keywords for fine-tuning pre-trained language models. We also use concepts that are represented in the Unified Medical Language System, UMLS, for query expansion and embedding generation. We exploit and test different clustering methods, namely Agglomerative clustering, Affinity Propagation, and K-Means, over the generated embeddings to form query clauses. ResultsOur proposed methods outperform existing state-of-the-art automatic query generation models across Precision (0.0821 compared with 0.005), Recall (0.9676 compared with 0.878), and F-measures (0.2898 compared with 0.0356 in F3 measure). In addition, some of the proposed methods can even outperform the performance of the manually crafted queries in some specific measures. ConclusionThe proposed model in this paper can be utilized to form an effective initial search query that can be further refined and updated by human reviewers for achieving the desired performance. For future work, we would like to explore the application of the presented query formalization methods in existing study identification methodologies and techniques, especially those that iteratively train machine learning models based on the domain experts’ feedback on the relevancy of the retrieved studies.

UFTDRDH: A novel user-oriented solution for full-text database retrieval in Chinese digital humanities

Information technology boosts the development of database retrieval in the Chinese digital humanities domain. However, most database providers adopt a system-oriented design pattern, which fails to handle the problem of query gaps in users’ retrieval process. This issue seriously hinders the effective use of database retrieval functionalities, peculiarly among those historical and humanities researchers. To address it, we propose UFTDRDH, a novel user-oriented solution based on automatic query formulation (AQF) technologies, which integrates a human–machine interactive module for the selection of new query-related expansion terms and a powerful query expansion algorithmic component (UFTDRDH-QEV) optimised by a topic-enhancing relevance feedback model approach (ToQE). To verify the effectiveness of UFTDRDH, several comparative experiments are conducted, including quantitative evaluation for retrieval efficiency and user satisfaction, as well as qualitative studies for interpretative traceability. The empirical results are multidimensional and robust, which not only shows the positive effects of different AQFs on gap reduction, especially the importance of query expansion as the most effective technology, but also underlines the remarkably advantageous performance of UFTDRDH compared with traditional system-oriented automatic query expansion in different task contexts. We believe the application of UFTDRDH can further strengthen the research focus on user-centred design and improve the level of current full-text database retrieval in the field of Chinese digital humanities. Broadly speaking, this solution can be also extended to the full-text database retrieval in other languages and digital humanities domains.

Ketamine is not associated with more post-intubation hypotension than etomidate in patients undergoing endotracheal intubation

IntroductionEmergency department (ED) patients undergoing emergent tracheal intubation often have multiple physiologic derangements putting them at risk for post-intubation hypotension. Prior work has shown that post-intubation hypotension is independently associated with increased morbidity and mortality. The choice of induction agent may be associated with post-intubation hypotension. Etomidate and ketamine are two of the most commonly used agents in the ED, however, there is controversy regarding whether either agent is superior in the setting of hemodynamic instability. The goal of this study is to determine whether there is a difference in the rate of post-intubation hypotension who received either ketamine or etomidate for induction. Additionally, we provide a subgroup analysis of patients at pre-existing risk of cardiovascular collapse (identified by pre-intubation shock index (SI) > 0.9) to determine if differences in rates of post-intubation hypotension exist as a function of sedative choice administered during tracheal intubation in these high-risk patients. We hypothesize that there is no difference in the incidence of post-intubation hypotension in patients who receive ketamine versus etomidate. MethodsA retrospective cohort study was conducted on a database of 469 patients having undergone emergent intubation with either etomidate or ketamine induction at a large academic health system. Patients were identified by automatic query of the electronic health records from 1/1/2016–6/30/2019. Exclusion criteria were patients <18-years-old, tracheal intubation performed outside of the ED, incomplete peri-intubation vital signs, or cardiac arrest prior to intubation. Patients at high risk for hemodynamic collapse in the post-intubation period were identified by a pre-intubation SI > 0.9. The primary outcome was the incidence of post-intubation hypotension (systolic blood pressure < 90 mmHg or mean arterial pressure < 65 mmHg). Secondary outcomes included post-intubation vasopressor use and mortality. These analyses were performed on the full cohort and an exploratory analysis in patients with SI > 0.9. We also report adjusted odds ratios (aOR) from a multivariable logistic regression model of the entire cohort controlling for plausible confounding variables to determine independent factors associated with post-intubation hypotension. ResultsA total of 358 patients were included (etomidate: 272; ketamine: 86). The mean pre-intubation SI was higher in the group that received ketamine than etomidate, (0.97 vs. 0.83, difference: −0.14 (95%, CI -0.2 to −0.1). The incidence of post-intubation hypotension was greater in the ketamine group prior to SI stratification (difference: −10%, 95% CI −20.9% to −0.1%). Emergency physicians were more likely to use ketamine in patients with SI > 0.9. In our multivariate logistic regression analysis, choice of induction agent was not associated with post-intubation hypotension (aOR 1.45, 95% CI 0.79 to 2.65). We found that pre-intubation shock index was the strongest predictor of post-intubation hypotension. ConclusionIn our cohort of patients undergoing emergent tracheal intubation, ketamine was used more often for patients with an elevated shock index. We did not identify an association between the incidence of post-intubation hypotension and induction agent between ketamine and etomidate. Patients with an elevated shock index were at higher risk of cardiovascular collapse regardless of the choice of ketamine or etomidate.

An Intelligent Approach to Automatic Query Formation from Plain Text using Artificial Intelligence

Man have always been, inherently, curious creatures. They ask questions in order to satiate their insatiable curiosity. For example, kids ask questions to learn more from their teachers, teachers ask questions to assist themselves to evaluate student performance, and we all ask questions in our daily lives. Numerous learning exchanges, ranging from one-on-one tutoring sessions to thorough exams, as well as real-life debates, rely heavily on questions. One notable fact is that, due to their inconsistency in particular contexts, humans are often inept at asking appropriate questions. It has been discovered that most people have difficulty identifying their own knowledge gaps. This becomes our primary motivator for automating question generation in the hopes that the benefits of an automated Question Generation (QG) system will help humans achieve their useful inquiry needs. QG and Information Extraction (IE) have become two major issues for language processing communities, and QG has recently become an important component of learning environments, systems, and information seeking systems, among other applications. The Text-to-Question generation job has piqued the interest of the Natural Language Processing (NLP), Natural Language Generation (NLG), Intelligent Tutoring System (ITS), and Information Retrieval (IR) groups as a possible option for the shared task. A text is submitted to a QG system in the Text-to-Question generation task. Its purpose would be to create a series of questions for which the text has answers (such as a word, a set of words, a single sentence, a text, a set of texts, a stretch of conversational dialogue, an inadequate query, and so on).