New platform of data analytics for mental health

2016 ◽  
Vol 33 (S1) ◽  
pp. S33-S33
Author(s):  
K. Suzuki
Keyword(s):  
Large Scale ◽  
Recommendation System ◽  
Clinical Decision Making ◽  
Heterogeneous Data ◽  
Individual Risk ◽  
Data Sets ◽  
Healthcare Outcomes ◽  
Life Years ◽  
The Right ◽  
Data Analytic

IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benefiting managers, clinicians, and patients.AimsThe aim is to enable a data analytic approach towards a value-based healthcare system via health informatics. The project generates knowledge from heterogeneous data sources to obtain patterns assisting clinical decision-making.MethodsThis project leverages a data analytic platform named HIKARI (“light” in Japanese) to deliver the “right” information, to the “right” people, at the “right” time. HIKARI consists of a data-driven and evidence-based Decision Support and Recommendation System (DSRS), facilitating identification of patterns in large-scale hospital and open data sets and linking data from different sources and types.ResultsUsing multiple, heterogeneous data sets, HIKARI detects correlations from retrospective data and would facilitate early intervention when signs and symptoms prompt immediate actions. HIKARI also analyses resource consumption patterns and suggests better resource allocation, using real-world data.ConclusionsWith the advance of ICT, especially data-intensive computing paradigm, approaches mixing individual risk assessment and environmental conditions become increasingly available. As a key tool, HIKARI DSRS can assist clinicians in the daily management of mental disorders.Disclosure of interestThe author has not supplied his declaration of competing interest.

scholarly journals A generalization of partial least squares regression and correspondence analysis for categorical and mixed data: An application with the ADNI data

2019 ◽  
Author(s):  
Derek Beaton ◽  
Gilbert Saporta ◽  
Hervé Abdi ◽  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Correspondence Analysis ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Multivariate Techniques ◽  
Data Sets ◽  
Complex Data ◽  
Least Squares Regression

AbstractCurrent large scale studies of brain and behavior typically involve multiple populations, diverse types of data (e.g., genetics, brain structure, behavior, demographics, or “mutli-omics,” and “deep-phenotyping”) measured on various scales of measurement. To analyze these heterogeneous data sets we need simple but flexible methods able to integrate the inherent properties of these complex data sets. Here we introduce partial least squares-correspondence analysis-regression (PLS-CA-R) a method designed to address these constraints. PLS-CA-R generalizes PLS regression to most data types (e.g., continuous, ordinal, categorical, non-negative values). We also show that PLS-CA-R generalizes many “two-table” multivariate techniques and their respective algorithms, such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank regression).

Dashboard to Support the Decision-Making within a Chronic Disease

2012 ◽  
pp. 160-171 ◽  
Author(s):  
Leonor Teixeira ◽  
Vasco Saavedra ◽  
João Pedro Simões
Keyword(s):  
Decision Making ◽  
Chronic Disease ◽  
Real Time ◽  
Clinical Decision Making ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Heterogeneous Data ◽  
Information And Communications Technologies ◽  
The Right ◽  
Real Time Information

This chapter describes a monitoring system based on alerts and Key Performance Indicators (KPIs), applied in clinical context, within a chronic disease (haemophilia). This kind of disease follows the patient through his/her life, and its treatment requires an almost permanent exchange of data/information with healthcare professional (HCPs), with the information and communications technologies (ICTs) a key contribution in this process. However, most applications based on those ICTs do not allow the analysis of heterogeneous data in real-time, requiring the availability of clinicians to check the data and analyze the information to support the clinical decision process. Since time is a scarce resource in the context of healthcare providers, and information a crucial resource in the decision support process, real-time monitoring systems can help finding the right balance between those two resources, presenting the key information in an appropriate format, through alerts and KPIs. The system described in this chapter, named hemo@care_dashboard, aims to support clinical decision-making of healthcare professionals of a specific chronic disease, providing real-time information in a push-logic through alerts and KPIs, displayed on a dashboard.

The Changing Context for Broadband Evaluation

2021 ◽  
pp. 25-44
Author(s):  
William Lehr
Keyword(s):  
Heterogeneous Data ◽  
Green Energy ◽  
Academic Disciplines ◽  
Data Sets ◽  
Healthcare Outcomes ◽  
Social Scientists ◽  
Ongoing Research ◽  
Broadband Service ◽  
The Social ◽  
Basic Infrastructure

Broadband Internet access is now widely recognized as basic infrastructure, like roads, water, and electricity. Identifying and reaching consensus on what constitutes an appropriate level of broadband service requires ongoing research to evaluate the social and economic impacts of broadband and broadband policies. As broadband has evolved, so too must the research focus. While availability and adoption of ever more advanced broadband will remain a concern, more of the focus should be on understanding how broadband is used and its effects on things like improved healthcare outcomes, educational performance, green/energy efficiency, and improving the quality of life for all society. We need more sector-specific and micro-studies of usage, and we need to tap into the different perspectives of the multiple academic disciplines. This will require multidisciplinary engagement among social scientists, network researchers, and policymakers. The research will require novel and more dynamic and heterogeneous data sets, metrics, and analytics tools.

scholarly journals Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases

2021 ◽  
Author(s):  
Maryam Panahiazar ◽  
Nolan Chen ◽  
Dmytro Lituiev ◽  
Dexter Hadley
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Healthcare Providers ◽  
Heterogeneous Data ◽  
Error Rates ◽  
Use Cases ◽  
Healthcare Outcomes ◽  
Breast Cancer Data ◽  
Cancer Data

AbstractIn healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating “smart data” which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions.

scholarly journals Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

2016 ◽  
Author(s):  
Joseph N. Paulson ◽  
Cho-Yi Chen ◽  
Camila M. Lopes-Ramos ◽  
Marieke L Kuijjer ◽  
John Platig ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Transcriptional Profiling ◽  
R Package ◽  
Heterogeneous Data ◽  
Data Sets ◽  
List Type ◽  
Complex Data ◽  
Rna Seq ◽  
Data Set

AbstractAlthough ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.HighlightsOverview of assumptions used in preprocessing and normalizationPipeline for preprocessing, quality control, and normalization of large heterogeneous dataA Bioconductor package for the YARN pipeline and easy manipulation of count dataPreprocessed GTEx data set using the YARN pipeline available as a resource

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Why Has There Been No People’s Power Rebellion in North Korea?

2018 ◽  
pp. 1-34
Author(s):  
Andrew Jackson
Keyword(s):  
Rural Areas ◽  
North Korea ◽  
Large Scale ◽  
Political Elite ◽  
Information Control ◽  
Al Jazeera ◽  
The North ◽  
New Information ◽  
Effective System ◽  
The Right

One scenario put forward by researchers, political commentators and journalists for the collapse of North Korea has been a People’s Power (or popular) rebellion. This paper analyses why no popular rebellion has occurred in the DPRK under Kim Jong Un. It challenges the assumption that popular rebellion would happen because of widespread anger caused by a greater awareness of superior economic conditions outside the DPRK. Using Jack Goldstone’s theoretical expla-nations for the outbreak of popular rebellion, and comparisons with the 1989 Romanian and 2010–11 Tunisian transitions, this paper argues that marketi-zation has led to a loosening of state ideological control and to an influx of infor-mation about conditions in the outside world. However, unlike the Tunisian transitions—in which a new information context shaped by social media, the Al-Jazeera network and an experience of protest helped create a sense of pan-Arab solidarity amongst Tunisians resisting their government—there has been no similar ideology unifying North Koreans against their regime. There is evidence of discontent in market unrest in the DPRK, although protests between 2011 and the present have mostly been in defense of the right of people to support themselves through private trade. North Koreans believe this right has been guaranteed, or at least tacitly condoned, by the Kim Jong Un government. There has not been any large-scale explosion of popular anger because the state has not attempted to crush market activities outright under Kim Jong Un. There are other reasons why no popular rebellion has occurred in the North. Unlike Tunisia, the DPRK lacks a dissident political elite capable of leading an opposition movement, and unlike Romania, the DPRK authorities have shown some flexibility in their anti-dissent strategies, taking a more tolerant approach to protests against economic issues. Reduced levels of violence during periods of unrest and an effective system of information control may have helped restrict the expansion of unrest beyond rural areas.

White Backlash

2015 ◽  
Author(s):  
Marisa Abrajano ◽  
Zoltan L. Hajnal
Keyword(s):  
Ethnic Diversity ◽  
Republican Party ◽  
Large Scale ◽  
White Americans ◽  
Political Beliefs ◽  
White Backlash ◽  
Political Spectrum ◽  
Racial Divide ◽  
The Right ◽  
Political Backlash

This book provides an authoritative assessment of how immigration is reshaping American politics. Using an array of data and analysis, it shows that fears about immigration fundamentally influence white Americans' core political identities, policy preferences, and electoral choices, and that these concerns are at the heart of a large-scale defection of whites from the Democratic to the Republican Party. The book demonstrates that this political backlash has disquieting implications for the future of race relations in America. White Americans' concerns about Latinos and immigration have led to support for policies that are less generous and more punitive and that conflict with the preferences of much of the immigrant population. America's growing racial and ethnic diversity is leading to a greater racial divide in politics. As whites move to the right of the political spectrum, racial and ethnic minorities generally support the left. Racial divisions in partisanship and voting, as the book indicates, now outweigh divisions by class, age, gender, and other demographic measures. The book raises critical questions and concerns about how political beliefs and future elections will change the fate of America's immigrants and minorities, and their relationship with the rest of the nation.

Evaluation of Ovaries in Patients with Polycystic Ovary Syndrome using Shear Wave Elastography

2020 ◽  
Vol 16 (5) ◽  
pp. 578-583
Author(s):  
Aysegul Altunkeser ◽  
Zeynep Ozturk Inal ◽  
Nahide Baran
Keyword(s):  
Shear Wave ◽  
Large Scale ◽  
Polycystic Ovary ◽  
Shear Wave Elastography ◽  
Left Ovary ◽  
Control Group ◽  
Tissue Elasticity ◽  
Group I ◽  
Pcos Group ◽  
The Right

Background: Shear wave electrography (SWE) is a novel non-invasive imaging technique which demonstrate tissue elasticity. Recent research evaluating the elasticity properties of normal and pathological tissues emphasize the diagnostic importance of this technique. Aims: Polycystic ovarian syndrome (PCOS), which is characterized by menstrual irregularity, hyperandrogenism, and polycystic overgrowth, may cause infertility. The aim of this study was to evaluate the elasticity of ovaries in patients with PCOS using SWE. Methods: 66 patients diagnosed with PCOS according to the Rotterdam criteria (PCOS = group I) and 72 patients with non-PCOS (Control = group II), were included in the study. Demographic and clinical characteristics of the participants were recorded. Ovarian elasticity was assessed in all patients with SWE, and speed values were obtained from the ovaries. The elasticity of the ovaries was compared between the two groups. Results: While there were statistically significant differences between the groups in body mass index (BMI), right and left ovarian volumes, luteinizing hormone and testosterone levels (p<0.05), no significant differences were found between groups I and II in the velocity (for the right ovary 3.89±1.81 vs. 2.93±0.72, p=0.301; for the left ovary 2.88±0.65 vs. 2.95±0.80, p=0.577) and elastography (for the right ovary 36.62±17.78 vs. 36.79±14.32, p=0.3952; for the left ovary 36.56±14.15 vs. 36.26±15.10, p=0.903) values, respectively. Conclusion: We could not obtain different velocity and elastography values from the ovaries of the patients with PCOS using SWE. Therefore, further large-scale studies are needed to elucidate this issue.

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