scholarly journals Causal Pathway Extraction from Web-Board Documents

2021 ◽  
Vol 11 (21) ◽  
pp. 10342
Author(s):  
Chaveevan Pechsiri ◽  
Rapepun Piriyakul
Keyword(s):  
Cartesian Product ◽  
Supervised Machine Learning ◽  
Simple Sentence ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Concept Pair ◽  
Causative Effect ◽  
Pair Matching ◽  
Effect Relation ◽  
Dynamic Template

This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents.

scholarly journals Event-Concept Pair Series Extraction to Represent Medical Complications from Texts

2018 ◽  
Vol 12 (3) ◽  
pp. 1320
Author(s):  
Chaveevan Pechsiri ◽  
Sumran Phainoun
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Medical Complications ◽  
Bayes Classifier ◽  
Simple Sentence ◽  
Research Results ◽  
Verb Phrases ◽  
Concept Pair ◽  
Causative Effect ◽  
Effect Relation

<p>This research aims to determine an event-concept pair series as consequent events, particularly a Cause-Effect-concept pair (called ‘CEpair’) series on disease documents downloaded from hospital-web-boards. CEpair series are used for representing medical/disease complications which benefit for Solving system. Each causative/effect event concept is expressed by a verb phrase of an elementary discourse unit (EDU) which is a simple sentence. The research has three problems; how to determine each adjacent-EDU pair having the cause-effect relation, how to determine a CEpair series mingled with non-causeeffect-relation EDUs, and how to identify the complication of several extracted CEpair series from the documents. Therefore, we extract NWordCo-concept set having the causative/effect concepts from EDUs’ verb phrases including a support vector machine to solve each NWordCo size. We apply the Naïve Bayes classifier to learn and extract an NWordCoconcept pair set as a knowledge template having the cause-effect relation from the documents. We then propose using the knowledge template to extract several CEpair series. We also apply the intersection of the NWordCo-concept sets to identify the commoncause/effect for representing the complication-development parts of the extracted-CEpair series. The research results provide the high percent correctness of the CEpair-series determination from the documents.</p>

scholarly journals Extraction of cause-effect-concept pair series from web documents

2020 ◽  
Vol 18 (2) ◽  
pp. 970
Author(s):  
Chaveevan Pechsiri ◽  
Titirut Mekbunditkul
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Simple Sentence ◽  
Web Documents ◽  
Disease Causation ◽  
Concept Pair ◽  
Causative Effect ◽  
Effect Relation ◽  
Cue Words ◽  
Concept Pairs

<span>This research aims to extract a cause-effect-concept pair series of consequent event occurrences in health information of hospital web-boards. The extracted cause-effect-concept pair series representing a disease causation pathway benefits for the automatic diagnosis and solving system. Where each causative/effect event concept is expressed by an elementary discourse unit (EDU which is a simple sentence). The research has three problems; how to determine causative/effect concept EDUs from the documents containing some EDU occurrences with both causative concepts and effect concepts, how to determine the cause-effect relation between two adjacent EDUs having the discourse cue ambiguity, and how to extract cause-effect-concept pair series mingled with either a stimulation relation EDU or other non-cause-effect relation EDUs from the documents. Therefore, we apply annotated NWordCo pairs with causative-effect concepts to represent EDU pairs with causative-effect concept where the NWordCo size solved by Naïve Bayes. We also apply Naïve Bayes to solve NWordCo-concept pairs having the cause-effect relation from the adjacent EDU pairs. We then propose using cue words and the collected NWordCo-concept pairs with the cause-effect relation to extract the cause-effect-concept pair series. The research results provide the high precision of the cause-effect-concept pair series determination from the documents. </span>

Why Causal Inference Matters to Nurses: The Case of Nurse Staffing and Patient Outcomes

2016 ◽  
Vol 21 (2) ◽  
Author(s):  
Deena Costa ◽  
Olga Yakusheva
Keyword(s):  
Causal Inference ◽  
Patient Outcomes ◽  
Nurse Staffing ◽  
Future Research ◽  
Nursing Research ◽  
Cross Sectional ◽  
Meaningful Improvement ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Study Designs

Since the early 1990s researchers have steadily built a broad evidence base for the association between nurse staffing and patient outcomes. However, the majority of the studies in the literature employ designs that are unable to robustly examine causal pathways to meaningful improvement in patient outcomes. A focus on causal inference is essential to moving the field of nursing research forward, and as part of the essential skill-set for all nurses as consumers of research. In this article, we aim to describe the importance of causal inference in nursing research and discuss study designs that are more likely to produce causal findings. We first review the conceptual framework supporting this discussion and then use selected examples from the literature, typifying three key study designs – cross-sectional, longitudinal, and randomized control trials (RCTs). The discussion will illustrate strengths and limitation of existing evidence, focusing on the causal pathway between nurse staffing and outcomes. The article conclusion considers implications for future research.

Methodological, Theoretical, Infrastructural, and Design Issues in Conducting Good Outcome Studies

2011 ◽  
Vol 21 (6) ◽  
pp. 644-653 ◽  
Author(s):  
Michael P. Kelly ◽  
Tessa A. Moore
Keyword(s):  
Best Practice ◽  
Outcome Studies ◽  
Controlled Trials ◽  
Evidence Based ◽  
Good Outcome ◽  
Randomized Controlled ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Based Medicine

This article outlines a set of methodological, theoretical, and other issues relating to the conduct of good outcome studies. The article begins by considering the contribution of evidence-based medicine to the methodology of outcome research. The lessons which can be applied in outcome studies in nonmedical settings are described. The article then examines the role of causal pathways between interventions and outcomes and especially the importance of delineating them in advance of undertaking investigations. The development of designs based on randomized controlled trials (RCTs) with fully articulated causal pathways is described. Ways of supplementing RCTs with methods to highlight elements in the causal pathway in outcome studies are indicated. The importance of adhering to best practice in reporting and analysis is also noted.

scholarly journals Replicating the Resource Curse: A Qualitative Replication of Ross 2004

2021 ◽  
Author(s):  
Megan Becker ◽  
Jonathan Markowitz ◽  
Sarah Orsborn ◽  
Srividya Dasaraju ◽  
Lindsay Lauder ◽  
...  
Keyword(s):  
Qualitative Research ◽  
Natural Resources ◽  
Political Science ◽  
Pathway Analysis ◽  
Resource Curse ◽  
Civil Conflict ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Conflict Intensity

What are the causal pathways through which natural resources are linked to civil conflict? The most comprehensive answer to this question comes from Ross (2004), who conducted the first qualitative causal pathway analysis on this issue. Despite the study's prominence, its findings have never been replicated due to the challenge of re- coding thirteen hypotheses across thirteen cases. To overcome this, we conduct the first qualitative replication in Political Science to employ an original codebook, a team of coders, and inter-coder reliability checks. We find that 24% of Ross' codings fail to replicate, a large enough share to alter his core findings. Contrary to Ross, we find that resources affect conflict onset through the pathways of both greed and grievance. Additionally, we find that resources generally increase conflict intensity and duration. Our methods and approach can be broadly applied to future qualitative research, especially medium-N causal pathway analysis.

scholarly journals Improving Comparative Effectiveness Research of Complex Health Interventions: Standards from the Patient-Centered Outcomes Research Institute (PCORI)

2020 ◽  
Vol 35 (S2) ◽  
pp. 875-881 ◽  
Author(s):  
Laura C. Esmail ◽  
Rebecca Barasky ◽  
Brian S. Mittman ◽  
David H. Hickam
Keyword(s):  
Comparative Effectiveness Research ◽  
Outcomes Research ◽  
Comparative Effectiveness ◽  
Health Interventions ◽  
Effectiveness Research ◽  
Patient Centered ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Healthcare Interventions ◽  
Research Institute

Abstract Introduction Complex health interventions (CHIs) are increasingly studied in comparative effectiveness research (CER), and there is a need for improvements in CHI research practices. The Patient-Centered Outcomes Research Institute (PCORI) Methodology Committee (MC) launched an effort in 2016 to develop formal guidance on this topic. Objective To develop a set of minimal standards for scientifically valid, transparent, and reproducible CER studies of CHIs. The standards are intended to apply to research examining a broad range of healthcare interventions including delivery system, behavior change, and other non-pharmacological interventions. Methods We conducted a literature review, reviewed existing methods guidance, and developed standards through an iterative process involving the MC, two panels of external research methods experts, and a 60-day public comment period. The final standards were approved by the PCORI MC and adopted by the PCORI Board of Governors on April 30, 2018. Results The final standards include the following: (1) fully describe the intervention and comparator and define their core functions, (2) specify the hypothesized causal pathways and their theoretical basis, (3) specify how adaptations to the form of the intervention and comparator will be allowed and recorded, (4) plan and describe a process evaluation, and (5) select patient outcomes informed by the causal pathway. Discussion The new standards offer three major contributions to research: (1) they provide a simple framework to help investigators address the major methodological features of a CHI study, (2) they emphasize the importance of the causal model and the need to understand how a CHI achieves its effects rather than simply measuring these effects, and (3) they require description of a CHI using the concepts of core functions and forms. While these standards apply formally to PCORI-funded CER studies, they have broad applicability.

scholarly journals Bayesian causal mediation analysis with multiple ordered mediators

2018 ◽  
Vol 19 (6) ◽  
pp. 634-652
Author(s):  
Tianming Gao ◽  
Jeffrey M Albert
Keyword(s):  
Mediation Analysis ◽  
Mixed Model ◽  
Dental Health ◽  
Health Study ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Causal Pathways ◽  
Sensitivity Parameter ◽  
Causal Pathway ◽  
Causal Mediation Analysis

Causal mediation analysis provides investigators insight into how a treatment or exposure can affect an outcome of interest through one or more mediators on causal pathway. When multiple mediators on the pathway are causally ordered, identification of mediation effects on certain causal pathways requires a sensitivity parameter to be specified. A mixed model-based approach was proposed in the Bayesian framework to connect potential outcomes at different treatment levels, and identify mediation effects independent of a sensitivity parameter, for the natural direct and indirect effects on all causal pathways. The proposed method is illustrated in a linear setting for mediators and outcome, with mediator-treatment interactions. Sensitivity analysis was performed for the prior choices in the Bayesian models. The proposed Bayesian method was applied to an adolescent dental health study, to see how social economic status can affect dental caries through a sequence of causally ordered mediators in dental visit and oral hygiene index.

scholarly journals Developing logic models to inform public health policy outcome evaluation: an example from tobacco control

2020 ◽  
Author(s):  
Tessa Langley ◽  
Duncan Gillespie ◽  
Sarah Lewis ◽  
Katie Eminson ◽  
Alan Brennan ◽  
...  
Keyword(s):  
Public Health ◽  
Health Policy ◽  
Tobacco Control ◽  
Public Health Policy ◽  
Large Scale ◽  
Outcome Evaluation ◽  
Causal Pathways ◽  
Logic Models ◽  
Intermediate Outcomes ◽  
Causal Pathway

Abstract Background The evaluation of large-scale public health policy interventions often relies on observational designs where attributing causality is challenging. Logic models—visual representations of an intervention’s anticipated causal pathway—facilitate the analysis of the most relevant outcomes. We aimed to develop a set of logic models that could be widely used in tobacco policy evaluation. Methods We developed an overarching logic model that reflected the broad categories of outcomes that would be expected following the implementation of tobacco control policies. We subsequently reviewed policy documents to identify the outcomes expected to result from the implementation of each policy and conducted a literature review of existing evaluations to identify further outcomes. The models were revised according to feedbacks from a range of stakeholders. Results The final models represented expected causal pathways for each policy. The models included short-term outcomes (such as policy awareness, compliance and social cognitive outcomes), intermediate outcomes (such as changes in smoking behaviour) and long-term outcomes (such as mortality, morbidity and health service usage). Conclusions The use of logic models enables transparent and theory-based planning of evaluation analyses and should be encouraged in the evaluation of tobacco control policy, as well as other areas of public health.

Mental Health Disorders in Children and Young People: Scope, Cause and Prevention

2000 ◽  
Vol 34 (4) ◽  
pp. 570-578 ◽  
Author(s):  
Stephen R. Zubrick ◽  
Sven R. Silburn ◽  
Paul Burton ◽  
Eve Blair
Keyword(s):  
Mental Health ◽  
Young People ◽  
Mental Health Problems ◽  
Preventive Intervention ◽  
Mental Health Disorders ◽  
Environment Interaction ◽  
Children And Young People ◽  
Causal Pathways ◽  
Causal Pathway ◽  
Health Disorders

Objective: To review the scope and characteristics of mental health disorders in children and young people in Australia; detail some emerging concepts of the causal pathways of mental health disorders in children and young people; and discuss aspects of the prevention of mental health disorders and the promotion of mental health in children and young people. Method: An integrated review of selected literature. Results: (i) While as many as one in five Australian children aged from four to 17 have significant mental health problems there remains a need for prevalence estimates in subsections of the population, notably children and young people of Aboriginal and Torres Strait Islander descent; (ii) appropriate studies of gene-environment interaction will require better measurement and developmental exposition of those risk exposures that are known to be on the causal pathway to mental health disorder; and (iii) universal, selective and indicated prevention trials and evaluations directed at anxiety, depression and conduct disorder are needed. Conclusion: Preventive intervention and promotion in mental health must entail effective collaboration at national, state and local levels between health, welfare and education sectors. These sectors must be informed by high quality epidemiology and a knowledge of the causal pathways of mental health disorders. Such intervention must also improve the movement of scientific knowledge to political policy on one hand and to praxis on the other. This will require a clear and persistent vision of the urgency, costs and consequences of mental health disorders in children and young people coupled with effective leadership and political resolve.

scholarly journals Lipids, Anthropometric Measures, Smoking and Physical Activity Mediate the Causal Pathway from Education to Breast Cancer in Women: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Hongkai Li ◽  
Lei Hou ◽  
Yuanyuan Yu ◽  
Xiaoru Sun ◽  
Xinhui Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mendelian Randomization ◽  
Sensitivity Analyses ◽  
Causal Mechanism ◽  
Level Of Education ◽  
Causal Pathways ◽  
Causal Pathway

Abstract Background Our objective is to investigate whether obtaining a higher level of education was causally associated with lower breast cancer risk and to identify the causal mechanism linking them. Methods Firstly, we performed a meta-analysis for educational attainment on breast cancer using 33 MR studies, including 15 case-control studies, 10 cross-sectional studies, and 8 cohort studies. Secondly, the main data analysis used publicly available summary-level data from two large GWAS consortia (Breast Cancer Association Consortium [BCAC] and the Social Science Genetic Association Consortium [SSGAC]). Mendelian randomization (MR) analysis used 65 genetic variants derived from the SSGAC as instrumental variables for years of schooling. The outcomes were the overall breast cancer risk (122,977 cases/105,974 controls in women) and its two subtypes: estrogen receptor (ER)-positive breast cancer (ER+: 69,501 cases) and ER-negative breast cancer (ER-: 21,468 cases). Furthermore, six additional consortia were analyzed to investigate the causal pathways from education to breast cancer. The fixed and random effects inverse variance weighted methods were used to estimate the causal effects, along with other additional MR methods as sensitivity analyses. Results The results showed that each additional standard deviation of 4.2 years of education was causally associated with a 27% lower risk of ER- (OR 0.73, 95% CI [0.64, 0.84]; P-value < 0.001). However, very weak causal relationship was found with overall breast cancer and no causal association with ER + risk, consistent with the sensitivity analyses. A genetic predisposition for higher education was causally associated with lower ER- risk and was found to be partially related to hip circumference, body mass index, triglyceride and HDL levels, smoking, and physical activity. Conclusion A low level of education is a causal risk factor in the development of ER- as it is associated with a poor lipid profile, anthropometric measurements, smoking, and types of physical activity.

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