CREATE: Concept Representation and Extraction from Heterogeneous Evidence

Traditional information retrieval methodology is guided by document retrieval paradigm, where relevant documents are returned in response to user queries. This paradigm faces serious drawback if the desired result is not explicitly present in a single document. The problem becomes more obvious when a user tries to obtain complete information about a real world entity, such as person, company, location etc. In such cases, various facts about the target entity or concept need to be gathered from multiple document sources. In this work, we present a method to extract information about a target entity based on the concept retrieval paradigm that focuses on extracting and blending information related to a concept from multiple sources if necessary. The paradigm is built around a generic notion of concept which is defined as any item that can be thought of as a topic of interest. Concepts may correspond to any real world entity such as restaurant, person, city, organization, etc, or any abstract item such as news topic, event, theory, etc. Web is a heterogeneous collection of data in different forms such as facts, news, opinions etc. We propose different models for different forms of data, all of which work towards the same goal of concept centric retrieval. We motivate our work based on studies about current trends and demands for information seeking. The framework helps in understanding the intent of content, i.e. opinion versus fact. Our work has been conducted on free text data in English. Nevertheless, our framework can be easily transferred to other languages

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Childhood Mishaps and Its Cognizance among Nepalese Mothers of Parsa District for Its Prevention, Small Cross-sectional Study

Introduction: Several studies have shown that there several unintentional causes for the unwanted childhood accidents. In addition, Nepal demographic health survey via West University of England revealed at 2006 A.D, 11% death casualties of under- five aged children are due to unintentional injuries 1. This particular study is extremely useful to health care planner, provider and researcher to have grand design to be produced by government of Nepal, such that; there shall be minimal rate of casualties of deceased children due to accidents. Methods: This study is descriptive cross sectional study carried out in Parsa district of Nepal where the respondents were mother to assess their awareness of cause of childhood accidents and its prevention. Computer software SPSS is use to scrupulous analysis of study where the chi-square test is used with 95% level of confidence (p=0.05). Results: Poisoning 96% cases is the cause of childhood accident unintentionally, followed by 94% foreign body aspiration, 85% flame burn. Unsupervised children are more prone to injury than supervised children. Finally and foremost the crucial correlation of parents level of awareness with childhood are as follows; inadequate level of knowledge have higher percentage of accident (58%), followed by moderately adequate (32%) and adequate (10%). Conclusions: This study though done on small scale on small part of Parsa district can play key role to the policy to have vigilantive and supervision power to see the loopholes that need to be detected and dealing in curative manner. Keywords: accident ; childhood ; injuries ;Nepal

Towards a structured representation of generic concepts and relations in large text corpora

Extraction of structured information from text corpora involves identifying entities and the relationship between entities expressed in unstructured text. We propose a novel iterative pattern induction method to extract relation tuples exploiting lexical and shallow syntactic pattern of a sentence. We start with a single pattern to illustrate how the method explores additional paterns and tuples by itself with increasing amount of data. We apply frequency and correlation based filtering and ranking of relation tuples to ensure the correctness of the system. Experimental evaluation compared to other state of the art open extraction systems such as Reverb, textRunner and WOE shows the effectiveness of the proposed system

Childhood Mishaps and Its Cognizance among Nepalese Mothers of Parsa District for Its Prevention, Small Cross-sectional Study

Introduction: Several studies have shown that there several unintentional causes for the unwanted childhood accidents. In addition, Nepal demographic health survey via West University of England revealed at 2006 A.D, 11% death casualties of under-  ve aged children are due to unintentional injuries 1. This particular study is extremely useful to health care planner, provider and researcher to have grand design to be produced by government of Nepal, such that; there shall be minimal rate of casualties of deceased children due to accidents. Methods: This study is descriptive cross sectional study carried out in Parsa district of Nepal where the respondents were mother to assess their awareness of cause of childhood accidents and its prevention. Computer software SPSS is use to scrupulous analysis of study where the chi-square test is used with 95% level of con dence (p=0.05). Results: Poisoning 96% cases is the cause of childhood accident unintentionally, followed by 94% foreign body aspiration, 85%  ame burn. Unsupervised children are more prone to injury than supervised children. Finally and foremost the crucial correlation of parents level of awareness with childhood are as follows; inadequate level of knowledge have higher percentage of accident (58%), followed by moderately adequate (32%) and adequate (10%). Conclusion: This study though done on small scale on small part of Parsa district can play key role to the policy to have vigilantive and supervision power to see the loopholes that need to be detected and dealing in curative manner.  Keywords: accident ; childhood ; injuries ; Nepal

Characterizing comment spam in the blogosphere through content analysis

Spams are no longer limited to emails and webpages. The increasing penetration of spam in the form of comments in blogs and social networks has started becoming a nuisance and potential threat. In this work, we explore the challenges posed by this type of spam in the blogosphere with substantial generalization regarding other social media. Thus, we investigate the characteristics of comment spam in blogs based on their content. The framework uses some of the previously explored methods developed to effectively extract the features of the blog spam and also introduces a novel method of active learning from the raw data without requiring training instances. This makes the approach more flexible and realistic for such applications. We also incorporate the concept of co-training for supervised learning to get accurate results. The preliminary evaluation of the proposed framework shows promising results. © 2009 IEEE

Classification of Clinical Conditions: A Case Study on Prediction of Obesity and Its Co-morbidities

Abstract. We investigate a multiclass, multilabel classification problem in medical domain in the context of prediction of obesity and its co-morbidities. Challenges of the problem not only lie in the issues of statistical learning such as high dimensionality, interdependence between multiple classes but also in the characteristics of the data itself. In particular, narrative medical reports are predominantly written in free text natural language which confronts the problem of predominant synonymy, hyponymy, negation and temporality. Our work explores the comparative evaluation of both traditional statistical learning based approach and information extraction based approach for the development of predictive computational models. In addition, we propose a scalable framework which combines both the statistical and extraction based methods with appropriate feature representation/selection strategy. The framework leads to reliable results in making correct classification. The framework was designed to participate in the second i2b2 Obesity Challenge