Risk Stratification and Prognosis Using Predictive Modelling and Big Data Approaches

2019 ◽  
pp. 87-105
Author(s):  
Shyam Visweswaran ◽  
Gregory F. Cooper
Keyword(s):  
Big Data ◽  
Risk Stratification ◽  
Predictive Modelling

scholarly journals Big data and predictive modelling for the opioid crisis: existing research and future potential

2021 ◽  
Vol 3 (6) ◽  
pp. e397-e407
Author(s):  
Chrianna Bharat ◽  
Matthew Hickman ◽  
Sebastiano Barbieri ◽  
Louisa Degenhardt
Keyword(s):  
Big Data ◽  
Predictive Modelling ◽  
Opioid Crisis ◽  
Future Potential

scholarly journals Social Media Big Data Analytics for Demand Forecasting

2020 ◽  
Vol 28 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Positioning big data analytics capabilities towards financial service agility

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeeku Sam Edu
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Business Models ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Financial Service ◽  
Content Type ◽  
It Capability

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts

2021 ◽  
Author(s):  
Georgia Papacharalampous ◽  
Hristos Tyralis
Keyword(s):  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Large Scale ◽  
Predictive Modelling ◽  
Global Scale ◽  
Multiple Models ◽  
Forecast Combinations ◽  
Hydroclimatic Variability ◽  
Hydroclimatic Variables

<p>We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.</p>

Development of a clinicomolecular risk stratification system for nonmetastatic nasopharyngeal carcinoma using Epstein–Barr virus DNA and TNM stage: A “Big data” analysis of 9,160 endemic cases.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6043-6043
Author(s):  
Fo-Ping Chen ◽  
Guan-Qun Zhou ◽  
Ying-Shan Luo ◽  
Kuan Rui Lloyd Tan ◽  
Sze Huey Tan ◽  
...  
Keyword(s):  
Big Data ◽  
Nasopharyngeal Carcinoma ◽  
Risk Stratification ◽  
Epstein Barr Virus ◽  
Tnm Stage ◽  
Barr Virus ◽  
Stage Classification ◽  
Epstein Barr ◽  
Stratification System ◽  
Risk Stratification System

6043 Background: To construct a clinicomolecular index integrating circulating Epstein-barr virus (cfEBV) DNA with T- and N- categories for better prognostication in nasopharyngeal carcinoma (NPC). Methods: Clinical and treatment records of 9,160 biopsy-proven, non-metastatic NPC cases were identified from an institutional “Big-data” platform. Decision tree modeling (DTM), recursive partitioning analysis (RPA) and adjusted hazard ratio (AHR) methods were used to generate clinicomolecular risk models. Outcome prediction of the models were compared against 8th edition TNM stage and two RPA-original models. Results: We observed linearity between cfEBV DNA and DFS; cfEBV DNA of > 2,000 copies was consistent for risk discretisation (HR > 1.0) for DFS, OS and DMFS in our cohort of 9,160 patients. DTM, RPA-new and AHR modelling using a two-tiered stratification by cfEBV DNA (≤2,000 and > 2,000 copies) and T- and N- categories yielded five risk groups with significantly disparate DFS (P < 0.001 for all subgroup comparisons). AHR model outperformed all other models and the TNM stage classification with better hazard consistency, hazard discrimination, explained variation, sample size balance and likelihood difference. Importantly, our clinicomolecular AHR groupings were significantly associated with the efficacy of different therapeutic regimes. Outcomes were comparable between concurrent chemoradiotherapy and intensity-modulated radiotherapy (IMRT) and IMRT alone for AHR1-2 (3-y DFS [AHR1] = 96.2% [IMRT] vs 95.3% [chemo-IMRT]; 3-y DFS [AHR2] = 91.1% vs 90.4%). Neoadjuvant chemotherapy and chemo-IMRT was superior to chemo-IMRT alone for AHR4-5 (AHRDFS 0.77[0.65-0.93], P = 0.005; 0.77[0.61-0.97], P = 0.027), but not for AHR3 (AHRDFS 1.07[0.86-1.34]). Conclusions: Here, we present a robust clinicomolecular risk stratification system that outperforms the TNM stage classification in non-metastatic NPC. Our clinicomolecular model is associated with the efficacy of different therapeutic regimes.

Social Media Big Data Analytics for Demand Forecasting

2022 ◽  
pp. 902-920
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Sign in / Sign up

Export Citation Format

Share Document