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Details

  • Name

    Mário Amorim Lopes
  • Role

    Senior Researcher
  • Since

    01st December 2013
009
Publications

2023

Predicting the future: introducing business analytics to endoscopy units

Authors
Pinho, R; Veloso, R; Estevinho, MM; Rodrigues, T; Almada Lobo, B; Amorim Lopes, M; Freitas, T;

Publication
REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS

Abstract
Background and aims: currently, most endoscopy software only provides limited statistics of past procedures, while none allows patterns to be extrapolated. To overcome this need, the authors applied business analytic models to pre-dict future demand and the need for endoscopists in a ter-tiary hospital Endoscopy Unit. Methods: a query to the endoscopy database was per-formed to retrieve demand from 2015 to 2021. The graphi-cal inspection allowed inferring of trends and seasonality, perceiving the impact of the COVID-19 pandemic, and se-lecting the best forecasting models. Considering COVID-19's impact in the second quarter of 2020, data for esoph-agogastroduodenoscopy (EGD) and colonoscopy was estimated using linear regression of historical data. The actual demand in the first two quarters of 2022 was used to validate the models. Results: during the study period, 53,886 procedures were requested. The best forecasting models were: a) simple sea-sonal exponential smoothing for EGD, colonoscopy and percutaneous endoscopic gastrostomy (PEG); b) double ex-ponential smoothing for capsule endoscopy and deep en-teroscopy; and c) simple exponential smoothing for endo-scopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS). The mean average percent-age error ranged from 6.1 % (EGD) to 33.5 % (deep en - teroscopy). Overall, 8,788 procedures were predicted for 2022. The actual demand in the first two quarters of 2022 was within the predicted range. Considering the usual time allocation for each technique, 3.2 full-time equivalent en-doscopists (40 hours-dedication to endoscopy) will be re-quired to perform all procedures in 2022. Conclusions: the incorporation of business analytics into the endoscopy software and clinical practice may enhance resource allocation, improving patient-focused deci-sion-making and healthcare quality.

2023

A Biomedical Entity Extraction Pipeline for Oncology Health Records in Portuguese

Authors
Sousa, H; Pasquali, A; Jorge, A; Santos, CS; Lopes, MA;

Publication
38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023

Abstract
Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to suboptimal and/or inefficient treatment procedures, healthcare providers would greatly benefit from a system that effectively summarizes the information of those records. With the advent of deep neural models, this objective has been partially attained for English clinical texts, however, the research community still lacks an effective solution for languages with limited resources. In this paper, we present the approach we developed to extract procedures, drugs, and diseases from oncology health records written in European Portuguese. This project was conducted in collaboration with the Portuguese Institute for Oncology which, besides holding over 10 years of duly protected medical records, also provided oncologist expertise throughout the development of the project. Since there is no annotated corpus for biomedical entity extraction in Portuguese, we also present the strategy we followed in annotating the corpus for the development of the models. The final models, which combined a neural architecture with entity linking, achieved..1 scores of 88.6, 95.0, and 55.8 per cent in the mention extraction of procedures, drugs, and diseases, respectively.

2022

Fostering Customer Bargaining and E-Procurement Through a Decentralised Marketplace on the Blockchain

Authors
Martins, J; Parente, M; Amorim Lopes, M; Amaral, L; Figueira, G; Rocha, P; Amorim, P;

Publication
IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT

Abstract
Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.

2021

Improving picking performance at a large retailer warehouse by combining probabilistic simulation, optimization, and discrete-event simulation

Authors
Amorim Lopes, M; Guimaraes, L; Alves, J; Almada Lobo, B;

Publication
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH

Abstract
Distribution warehouses are a critical part of supply chains, representing a nonnegligible share of the operating costs. This is especially true for unautomated, labor-intensive warehouses, partially due to time-consuming activities such as picking up items or traveling. Inventory categorization techniques, as well as zone storage assignment policies, may help in improving operations, but may also be short-sighted. This work presents a three-step methodology that uses probabilistic simulation, optimization, and event-based simulation (SOS) to analyze and experiment with layout and storage assignment policies to improve the picking performance. In the first stage, picking performance is estimated under different storage assignment policies and zone configurations using a probabilistic model. In the second stage, a mixed integer optimization model defines the overall warehouse layout by selecting the configuration and storage assignment policy for each zone. Finally, the optimized layout solution is tested under demand uncertainty in the third, final simulation phase, through a discrete-event simulation model. The SOS methodology was validated with three months of operational data from a large retailer's warehouse, successfully illustrating how it may be successfully used for improving the performance of a distribution warehouse.

2021

Enhancing optimization planning models for health human resources management with foresight

Authors
Amorim Lopes, M; Oliveira, M; Raposo, M; Cardoso Grilo, T; Alvarenga, A; Barbas, M; Alves, M; Vieira, A; Barbosa Povoa, A;

Publication
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE

Abstract
Achieving a balanced healthcare workforce requires health planners to adjust the supply of health human resources (HHR). Mathematical programming models have been widely used to assist such planning, but the way uncertainty is usually considered in these models entails methodological and practical issues and often disregards radical yet plausible changes to the future. This study proposes a new socio-technical methodology to factor in uncertainty over the future within mathematical programming modelling. The methodological approach makes use of foresight and scenario planning concepts to build tailor-made scenarios and scenario fit input parameters, which are then used within mathematical programming models. Health stakeholders and experts are engaged in the scenario building process. Causal map modelling and morphological analysis are adopted to digest stakeholders and experts' information about the future and give origin to contrasting and meaningful scenarios describing plausible future. These scenarios are then adjusted and validated by stakeholders and experts, who then elicit their best quantitative estimates for coherent combinations of input parameters for the mathematical programming model under each scenario. These sets of parameters for each scenario are then fed to the mathematical programming model to obtain optimal solutions that can be interpreted in light of the meaning of the scenario. The proposed methodology has been applied to a case study involving HHR planning in Portugal, but its scope far extends HHR planning, being especially suited for addressing strategic and policy planning problems that are sensitive to input parameters.

Supervised
thesis

2022

Timing Constraints in Quantum Programming Languages

Author
Vítor Emanuel Gonçalves Fernandes

Institution
UM

2022

Improvement and Standardization of a Mizusumashi in a Cork Industry

Author
Sérgio Tiago Lopes de Oliveira

Institution
UP-FEUP

2022

Towards Tunable Distributed Data Management for IoT

Author
Luís Manuel Meruje Ferreira

Institution
UM

2022

Improving Innovation Management Practices in a Luxury Fashion E-tail Business

Author
Alice Carvalho de Sousa

Institution
UP-FEUP

2022

Development and Analysis of A Local Energy Market Using Blockchain

Author
Tiago Manuel Massano Tavares

Institution
UP-FEUP