Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation Ricardo Ribeiro, Aneesh Zutshi Journal of Medical Internet Research, 2026 Background YouTube is increasingly used for healthcasting, the sharing of evidence-based dietary and lifestyle interventions by domain experts. In the metabolic health domain, channels focused on therapeutic carbohydrate restriction have accumulated audiences of millions. A distinctive feature is the comment section, where viewers share first-person accounts of health changes, constituting a unique source of real-world outcome data at scale. However, extracting structured health information from unstructured comments presents computational challenges. Objective This observational, cross-sectional study aims to develop and validate a precision-optimized computational framework for extracting self-reported health outcomes from healthcasting YouTube comments and to characterize the prevalence, distribution across health aspects, and channel-level variation of reported outcomes across a large-scale metabolic health corpus. Methods This study analyzed 43,111 unique YouTube comments from 110 videos across 11 therapeutic carbohydrate restriction-focused healthcasting channels (37,458 unique authors; data span November 2013 to January 2026; collected via YouTube data application programming interface version 3). The methodology comprised 3 construction phases and 5 validation studies. The construction phases were (1) exploratory corpus characterization, (2) iterative development of a 35-aspect hierarchical health outcome ontology, and (3) precision-optimized rule-based classification, validated through precision validation (stratified sample of n=500), recall estimation (n=510), external validation on 5 held-out channels (n=12,653 comments), large language model–assisted interrater reliability assessment, and transformer baseline comparison against Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (ROBERTa) classifiers. A supplementary aspect–based sentiment analysis contextualized the positive-only design. Results The framework identified 1790 positive health outcome reports (1790/43,111, 4.15% prevalence), achieving 97.6% (488/500) precision (95% CI 95.7%-98.6%) and estimated 56.2% recall (95% CI 43.4%-67.9%). The reports described 6674 positive outcomes, distributed across 35 health aspects and 18 named disease conditions extending beyond weight loss: pain and inflammation reduction (1137/6674, 17%), type 2 diabetes improvement (977/6674, 14.6%), skin health (784/6674, 11.8%), and psychological well-being (731/6674, 11%). Over half (3355/6674, 50.3%) spanned multiple research objectives. Significant channel-level variation was observed (χ²10=927.5; P<.001), with positive outcome rates ranging from 1.32% to 10.40% (odds ratio 8.68, 95% CI 7.10-10.61). Transformer baselines achieved higher recall but lower precision, confirming their advantage for high-confidence corpus generation. A supplementary aspect-based sentiment analysis indicated a positive-to-negative ratio of approximately 4.6:1 (n=1003), with negative experiences (59/495, 11.9%) predominantly involving gastrointestinal and cardiovascular concerns. Conclusions This study presents, to our knowledge, the first validated, rule-based framework for extracting self-reported metabolic health outcomes from healthcasting YouTube comments at corpus scale. Unlike existing recall-oriented social media health classifiers, the precision-optimized design achieves the confidence threshold required for outcomes research without manual review. These findings demonstrate that expert-led health content comment sections constitute a scalable, complementary data source for monitoring real-world engagement with dietary interventions, with implications for public health surveillance, platform design, and health communication research.
The value proposition of blockchain technologies and its impact on Digital Platforms Aneesh Zutshi, Antonio Grilo, Tahereh Nodehi Computers and Industrial Engineering, 2021 Since the last few years there have been a massive spurt of Research on Blockchain Technologies and several attempts at incorporating them for a myriad of Business Applications. Blockchain Technologies promised to bring about decentralized Trust having the potential to disrupt digital ecosystems by providing alternatives to centralized storage and management of data. This has the potential to radically transform industries and services through new models of data storage, transparency, tracking, payment systems amongst other advantages. However, Blockchain Technology is designed to provide other Value Propositions beyond decentralized storage, such as innovative crypto economic and investment models and radical new forms of decentralized participative governance models that could lead to the evolution of new generation of Digital Platforms and multi stakeholder business interactions. In this paper, through a systematic literature review, we present a set of key Blockchain Value Propositions and a discussion on how it can complement to the evolution of Digital Platforms and the Collaborative Economy. We also argue that while some of the value propositions are easier to integrate with existing Digital Platforms, more disruptive value propositions such as Business Automation, Economic and Governance Models present greater challenges but could lead to not just innovation at the technological level, but also metamorphosis of the existing social, economic and governance models.
A blockchain based architecture for fulfilling the needs of an e-procurement platform Proceedings of the International Conference on Industrial Engineering and Operations Management, 2020
The Emergence of Digital Platforms: A Conceptual Platform Architecture and impact on Industrial Engineering Aneesh Zutshi, Antonio Grilo Computers and Industrial Engineering, 2019 The Digital Platform Business Model has caused massive disruption across multiple industries and services and led to optimising efficiencies, speeding up information sharing and dynamising of business processes. This new phenomenon has pushed businesses into redesigning their strategy, and enabled economies of scale for several smaller companies, through the evolution of Business Ecosystems based on Digital Platforms. In this paper, through a systematic analysis of numerous case scenarios, we present a conceptual understanding of the Digital Platform and explore its impact in the field of Industrial Engineering as well as its implications for society. We elaborate on the key actors and characteristics of a Digital Platform Ecosystem and identify its main Value Proposition. We also present a Digital Platform Architecture, that enables us to conceptually classify its various layers based on the functionality and domain knowledge. Finally, we elaborate on the impact of Digital Platforms on Industrial Engineering, and identify scenarios where different areas of Industrial Engineering can be applied to the different layers of our Digital Platform Architecture.
Caller-Agent Pairing in Call Centers Using Machine Learning Techniques with Imbalanced Data Negin Mehrbod, Antonio Grilo, Aneesh Zutshi 2018 IEEE International Conference on Engineering Technology and Innovation ICE Itmc 2018 Proceedings, 2018 Call centers as the frontline of companies have high interaction with customers. Therefore, the call center performance is very important in the issue of customer satisfaction. Successful communications between agents and customers, satisfy customers and increase the performance of contact center. Call centers managers try to use historical data to improve the service to their clients. Pairing caller with the best suited agent using historical data, helps companies to reduce their costs and improve customer satisfaction. In this work, we proposed a model which optimize call centers outcome with using machine learning techniques to route the caller to the based-suited agent. The result shows using historical data of call center to find an intelligent pairing of callers and agents can improve the performance.
Simulation and forecasting of digital pricing models for an e-procurement platform using an agent-based simulation model Aneesh Zutshi, Antonio Grilo, Tahereh Nodehi, Ahmad Mehrbod, Ricardo Jardim-Goncalves Journal of Simulation, 2018 Online businesses can be represented as a complex interaction of interconnected online users responding to the value proposition of an online company. We propose a Dynamic Agent-Based Modeling framework (DYNAMOD) that aims to explain these complex dynamics. This framework aids in the creation of simulation models that mimic the actual market behavior and perform business forecasting and decision support functions. Through a case study of the largest e-procurement provider in Portugal – Vortal.biz, we simulate their pricing model and analyze revenue impact by optimizing pricing using genetic algorithms. The objective of this research is to propose agent-based model as an effective method to forecast the impact of pricing decisions.
A conceptual framework of risk identification for scale up companies in transition period Proceedings of the International Conference on Industrial Engineering and Operations Management, 2018
Relationship between investors and European startup ecosystems builders Antonio Grilo, Andre Agueda, Aneesh Zutshi, Tahereh Nodehi 2017 International Conference on Engineering Technology and Innovation Engineering Technology and Innovation Management Beyond 2020 New Challenges New Approaches ICE Itmc 2017 Proceedings, 2017
A comparison between nordic and mediterranean start up ecosystems: Economic sectors, business and pricing models Proceedings of the International Conference on Industrial Engineering and Operations Management, 2017
How business startup accelerators envision their future Proceedings of the International Conference on Industrial Engineering and Operations Management, 2017
Digital marketing practices of start-up accelerators Proceedings of International Conference on Computers and Industrial Engineering CIE, 2017
Simulating digital businesses using an agent based modeling approach Aneesh Zutshi, Antonio Grilo, Ricardo Jardim-Gonçalves ICE B 2014 Proceedings of the 11th International Conference on E Business Part of Icete 2014 11th International Joint Conference on E Business and Telecommunications, 2014
