asdasd

.github/workflows/jekyll.yml

@@ -36,7 +36,7 @@ jobs:
       - name: Setup Ruby
         uses: ruby/setup-ruby@8575951200e472d5f2d95c625da0c7bec8217c42 # v1.161.0
         with:
-          ruby-version: '3.3' # Not needed with a .ruby-version file
+          ruby-version: '2.6.5' # Not needed with a .ruby-version file
           bundler-cache: true # runs 'bundle install' and caches installed gems automatically
           cache-version: 0 # Increment this number if you need to re-download cached gems
       - name: Setup Pages

_bibliography/references.bib

@@ -78,3 +78,92 @@ @article{costaBiomedicalHealthInformatics2023
   pmcid = {PMC10034451},
   keywords = {Biomedical and health informatics,Education,Informatics,Medical informatics,Professional education,Teaching}
 }
+
+
+@article{coutinho-almeidaFastHealthcareInteroperability2024a,
+  title = {Fast {{Healthcare Interoperability Resources}}–{{Based Support System}} for {{Predicting Delivery Type}}: {{Model Development}} and {{Evaluation Study}}},
+  shorttitle = {Fast {{Healthcare Interoperability Resources}}–{{Based Support System}} for {{Predicting Delivery Type}}},
+  author = {Coutinho-Almeida, João and Cardoso, Alexandrina and Cruz-Correia, Ricardo and Pereira-Rodrigues, Pedro},
+  date = {2024-04-08},
+  journaltitle = {JMIR Formative Research},
+  shortjournal = {JMIR Form Res},
+  volume = {8},
+  pages = {e54109},
+  issn = {2561-326X},
+  doi = {10.2196/54109},
+  url = {https://formative.jmir.org/2024/1/e54109},
+  urldate = {2024-04-25},
+  abstract = {Background: The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3\% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences. Objective: This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians’ decisions. Methods: This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model’s performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals. Results: The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88\%. In the trial deployment phase at a single hospital, 3.8\% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30\% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries. Conclusions: This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.},
+  langid = {english}
+}
+
+
+@article{gazzarataHL7FastHealthcare2024,
+  title = {{{HL7 Fast Healthcare Interoperability Resources}} ({{HL7 FHIR}}) in Digital Healthcare Ecosystems for Chronic Disease Management: {{Scoping}} Review},
+  shorttitle = {{{HL7 Fast Healthcare Interoperability Resources}} ({{HL7 FHIR}}) in Digital Healthcare Ecosystems for Chronic Disease Management},
+  author = {Gazzarata, Roberta and Almeida, Joao and Lindsköld, Lars and Cangioli, Giorgio and Gaeta, Eugenio and Fico, Giuseppe and Chronaki, Catherine E.},
+  date = {2024-09-01},
+  journaltitle = {International Journal of Medical Informatics},
+  shortjournal = {International Journal of Medical Informatics},
+  volume = {189},
+  pages = {105507},
+  issn = {1386-5056},
+  doi = {10.1016/j.ijmedinf.2024.105507},
+  url = {https://www.sciencedirect.com/science/article/pii/S1386505624001709},
+  urldate = {2024-07-04},
+  abstract = {Background The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient’s home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows. Objectives This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management. Methods To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used. Results The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45~\%), cardiovascular disease (CVD) (more than 15~\%), and diabetes (almost 15~\%). The scoping review revealed that references to HL7 FHIR IGs are limited to~∼~20~\% of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40~\%), chronic disease management (25~\%), and diabetes (20~\%). HL7 FHIR IGs frequently complement the information in the article. Conclusions HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.},
+  keywords = {Chronic diseases,Digital healthcare ecosystems,Digital transformation,HL7 FHIR Implementation Guide,Interoperability standards,Living environments}
+}
+
+
+@article{coutinho-almeidaEvaluatingDistributedlearningRealworld2024,
+  title = {Evaluating Distributed-Learning on Real-World Obstetrics Data: Comparing Distributed, Centralized and Local Models},
+  shorttitle = {Evaluating Distributed-Learning on Real-World Obstetrics Data},
+  author = {Coutinho-Almeida, João and Cruz-Correia, Ricardo João and Rodrigues, Pedro Pereira},
+  date = {2024-05-15},
+  journaltitle = {Scientific Reports},
+  shortjournal = {Sci Rep},
+  volume = {14},
+  number = {1},
+  pages = {11128},
+  issn = {2045-2322},
+  doi = {10.1038/s41598-024-61371-1},
+  url = {https://www.nature.com/articles/s41598-024-61371-1},
+  urldate = {2024-07-04},
+  abstract = {Abstract             This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66\% being superior to the centralized and local counterpart and 77\% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.},
+  langid = {english},
+  keywords = {notion}
+}
+
+
+@article{coutinho-almeidaDevelopmentInitialValidation2024,
+  title = {Development and Initial Validation of a Data Quality Evaluation Tool in Obstetrics Real-World Data through {{HL7-FHIR}} Interoperable {{Bayesian}} Networks and Expert Rules},
+  author = {Coutinho-Almeida, João and Saez, Carlos and Correia, Ricardo and Rodrigues, Pedro Pereira},
+  date = {2024-10-01},
+  journaltitle = {JAMIA Open},
+  shortjournal = {JAMIA Open},
+  volume = {7},
+  number = {3},
+  pages = {ooae062},
+  issn = {2574-2531},
+  doi = {10.1093/jamiaopen/ooae062},
+  url = {https://doi.org/10.1093/jamiaopen/ooae062},
+  urldate = {2024-09-03},
+  abstract = {The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement.This article addresses the critical need for data quality evaluation in obstetrics by developing a novel tool. The tool utilizes Health Level 7 (HL7) Fast Healthcare Interoperable Resources (FHIR) standards in conjunction with Bayesian Networks and expert rules, offering a novel approach to assessing data quality in real-world obstetrics data.A harmonized framework focusing on completeness, plausibility, and conformance underpins our methodology. We employed Bayesian networks for advanced probabilistic modeling, integrated outlier detection methods, and a rule-based system grounded in domain-specific knowledge. The development and validation of the tool were based on obstetrics data from 9 Portuguese hospitals, spanning the years 2019-2020.The developed tool demonstrated strong potential for identifying data quality issues in obstetrics EHRs. Bayesian networks used in the tool showed high performance for various features with area under the receiver operating characteristic curve (AUROC) between 75\% and 97\%. The tool’s infrastructure and interoperable format as a FHIR Application Programming Interface (API) enables a possible deployment of a real-time data quality assessment in obstetrics settings. Our initial assessments show promised, even when compared with physicians’ assessment of real records, the tool can reach AUROC of 88\%, depending on the threshold defined.Our results also show that obstetrics clinical records are difficult to assess in terms of quality and assessments like ours could benefit from more categorical approaches of ranking between bad and good quality.This study contributes significantly to the field of EHR data quality assessment, with a specific focus on obstetrics. The combination of HL7-FHIR interoperability, machine learning techniques, and expert knowledge presents a robust, adaptable solution to the challenges of healthcare data quality. Future research should explore tailored data quality evaluations for different healthcare contexts, as well as further validation of the tool capabilities, enhancing the tool’s utility across diverse medical domains.With the widespread use of healthcare information systems, a vast amount of health data are generated, stored in electronic health records (EHRs). These data have the potential to advance medical knowledge and improve patient care, but only if it is of high quality. Data quality varies depending on its use, such as daily patient care, research, or management purposes. Poor data quality in EHRs can lead to incorrect healthcare decisions. Errors can occur at various stages, from data entry to processing and interpretation. Different approaches are needed to assess data quality based on its intended use. This article focuses on developing a tool to improve data quality in obstetrics using 3 main categories: completeness, plausibility, and conformance. Tested with data from 9 Portuguese hospitals, the tool uses methods like Bayesian networks and rule-based systems. Initial real-world testing showed promising results. However, assessing data quality remains complex and context dependent. Future research will refine the tool and expand its application. This work is a significant step towards ensuring high-quality EHR data for clinical and research purposes.}
+}
+@article{coutinho-almeidaCDK4InhibitorsEndocrine2024,
+  title = {{{CDK4}}/6 Inhibitors and Endocrine Therapy in the Treatment of Metastatic Breast Cancer: {{A}} Real-World and Propensity Score-Adjusted Comparison},
+  shorttitle = {{{CDK4}}/6 Inhibitors and Endocrine Therapy in the Treatment of Metastatic Breast Cancer},
+  author = {Coutinho-Almeida, João and Silva, Ana Sofia and Redondo, Patrícia and Rodrigues, Pedro Pereira and Ferreira, Ana},
+  date = {2024-01-01},
+  journaltitle = {Cancer Treatment and Research Communications},
+  shortjournal = {Cancer Treatment and Research Communications},
+  volume = {40},
+  pages = {100818},
+  issn = {2468-2942},
+  doi = {10.1016/j.ctarc.2024.100818},
+  url = {https://www.sciencedirect.com/science/article/pii/S2468294224000303},
+  urldate = {2024-09-04},
+  abstract = {Introduction/Background Hormone Receptor-positive (HR+) and Human Epidermal Growth Factor Receptor 2-negative (HER2-) breast cancer is the most common subtype, predominantly treated with endocrine therapy. The efficacy of CDK4/6 inhibitors combined with endocrine therapy in this context remains to be fully evaluated. Materials (or Patients) and Methods This study compared the effectiveness of CDK4/6 inhibitors (palbociclib and ribociclib) in combination with an aromatase inhibitor or fulvestrant against endocrine therapy alone in patients with HR+/HER2- advanced breast cancer. The main focus was on progression-free survival (PFS) and overall survival (OS). The study involved a population treated exclusively with endocrine therapy for bone involvement, examining median OS and PFS, and adjusting for variables like stage, visceral metastasis, age, and treatment line. Results The study found no significant OS difference between treatments with palbociclib, ribociclib, and endocrine therapy alone. However, ribociclib combined with letrozole significantly improved PFS over letrozole alone. Propensity score weighting indicated a potential 50 \% reduction in death risk with ribociclib compared to palbociclib, though this was not confirmed by cox regression. Conclusion CDK4/6 inhibitors, particularly ribociclib in combination with letrozole, show promise in improving outcomes for HR+/HER2- breast cancer patients. While palbociclib may not be superior to traditional endocrine therapy, the results underscore the need for further research. These findings could influence future treatment protocols, emphasizing the importance of personalized therapy in this patient group.},
+  keywords = {Breast cancer,CDK4/6,Palbociclib,Propensity score,Real-world data,Ribociclib}
+}

_config.yml

@@ -19,9 +19,7 @@ scholar:
   style: modern-language-association
   order: descending
   sort_by: year,month
-  bibliography_template: bib.html
-  source: _bibliography/
-  bibliography_class: bibliography
+  bibliography_template: bib
 
 repository: joofio/online-cv
 author: João Almeida