Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making.

Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.

Unveiling the Future of Oncology and Precision Medicine through Data Science.

Information generated via next-generation sequencing (NGS) technologies is often termed multi-omics data [...].

A produção sobre Ciência Aberta na área de Ciência da Informação no Brasil: análise no mapa temático das publicações da Brapci ­ 2015 a 2019 / The production on Open Science in the Information Science area in Brazil: analysis on the thematic map of Brapci publications ­ 2015 to 2019 / La producción sobre Ciencia Abierta en el área de Ciencias de la Información en Brasil: análisis sobre el mapa temático de las publicaciones de Brapci ­ 2015 a 2019

O texto discorre sobre relações entre a Ciência da Informação e o movimento da Ciência Aberta, sob a ótica de artigos científicos identificados na Base de Dados Referenciais de Artigos de Periódicos em Ciência da Informação. Objetiva determinar dimensões, campos e movimentos que se relacionam, estabelecendo um panorama dessa relação com as pesquisas brasileiras no período entre 2015 e 2019 no domínio da comunicação científica. A metodologia é a revisão narrativa de literatura, por meio da aplicação da análise de títulos, resumos e palavras-chave dos artigos selecionados. O campo empírico é composto pelos resultados obtidos pela busca na base, totalizando 36 resultados. Conclui-se que a Ciência da Informação está se relacionando com a Ciência Aberta, observando-se a prevalência de estudos sobre temáticas de dados de pesquisa abertos e sobre repositórios, de acordo com o período observado, como maneiras de aperfeiçoar os fazeres científicos.

The text discusses the relationship between Information Science and the Open Science movement, from the perspective of scientific articles identified in the Referential Database of Journal Articles in Information Science. The objective is to determine the dimensions, fields, and movements related, establishing an overview of this relationship with Brazilian research between 2015 and 2019, in the domain of scientific communication. The methodology employed is the narrative literature review, through the analysis of titles, abstracts, and keywords of selected articles. The empirical field consists of the results obtained through the search in the database, totaling 36 results. It is concluded that Information Science is relating to Open Science, with a prevalence of studies on open research data and repositories, according to the observed period, as ways to enhance scientific practices.

El texto discute la relación entre la Ciencia de la Información y el movimiento de la Ciencia Abierta, desde la perspectiva de artículos científicos identificados en la Base de Datos Referencial de Artículos de Revistas en Ciencia de la Información. El objetivo es determinar dimensiones, campos y movimientos relacionados, estableciendo una visión general de esta relación con la investigación brasileña entre 2015 y 2019, en el ámbito de la comunicación científica. La metodología es la revisión narrativa de literatura, a través del análisis de títulos, resúmenes y palabras clave de artículos seleccionados. El campo empírico consiste en los resultados obtenidos mediante la búsqueda en la base de datos, con 36 resultados. Se concluye que la Ciencia de la Información se relaciona con la Ciencia Abierta, con una prevalencia de estudios sobre datos de investigación abiertos y repositorios, según el período observado, como formas de mejorar las prácticas científicas.

The influence of ambient environmental factors on breakthrough Cancer pain: insights from remote health home monitoring and a proposed data analytic approach.

BACKGROUND: Breakthrough cancer pain (BTCP) is primarily managed at home and can stem from physical exertion and emotional distress triggers. Beyond these triggers, the impact of ambient environment on pain occurrence and intensity has not been investigated. This study explores the impact of environmental factors on the frequency and severity of breakthrough cancer pain (BTCP) in the home context from the perspective of patients with advanced cancer and their primary family caregiver. METHODS: A health monitoring system was deployed in the homes of patient and family caregiver dyads to collect self-reported pain events and contextual environmental data (light, temperature, humidity, barometric pressure, ambient noise.) Correlation analysis examined the relationship between environmental factors with: 1) individually reported pain episodes and 2) overall pain trends in a 24-hour time window. Machine learning models were developed to explore how environmental factors may predict BTCP episodes. RESULTS: Variability in correlation strength between environmental variables and pain reports among dyads was found. Light and noise show moderate association (r = 0.50-0.70) in 66% of total deployments. The strongest correlation for individual pain events involved barometric pressure (r = 0.90); for pain trends over 24-hours the strongest correlations involved humidity (r = 0.84) and barometric pressure (r = 0.83). Machine learning achieved 70% BTCP prediction accuracy. CONCLUSION: Our study provides insights into the role of ambient environmental factors in BTCP and offers novel opportunities to inform personalized pain management strategies, remotely support patients and their caregivers in self-symptom management. This research provides preliminary evidence of the impact of ambient environmental factors on BTCP in the home setting. We utilized real-world data and correlation analysis to provide an understanding of the relationship between environmental factors and cancer pain which may be helpful to others engaged in similar work.

Bibliotecários e os desafios da Ciência Aberta: habilidades, recursos e serviços / Librarians and the challenges of Open Science: skills, resources and services / Bibliotecarios y los desafíos de la Ciencia Abierta: habilidades, recursos y servicios

Este estudo tem como objetivo identificar, na literatura científica, produtos e serviços desenvolvidos por bibliotecários vislumbrando as práticas de Ciência Aberta. A questão principal é identificar: qual o papel dos bibliotecários frente aos desafios da Ciência Aberta? Predominantemente qualitativa, esta pesquisa pode ser caracterizada como bibliográfica, exploratória e descritiva. Para atingir seu objetivo, utilizou-se a técnica de revisão rápida de literatura. Foi realizado um levantamento de publicações indexadas na Brapci, na Scopus e na Web of Science, sendo recuperadas três publicações em cada. Ao excluir um título que se repetiu, o corpus da pesquisa configurou-se com seis artigos e dois resumos apresentados em evento. Conclui-se que debates sobre o novo modus operandi de fazer ciência vêm aumentando e os bibliotecários parecem intimamente relacionados às ações de Ciência Aberta nas diversas etapas da pesquisa científica. Devido às suas habilidades e aos seus serviços, entende-se que exercem um dos papéis centrais na concretização da abertura da ciência.

This study aims to identify, in the scientific literature, products and services developed by librarians with a view to Open Science practices. The main question is to identify: what role is played by librarians facing the challenges of Open Science? Predominantly qualitative, this research can be characterized as bibliographic, exploratory, and descriptive. To achieve its objective, a rapid literature review technique was used. A survey of publications indexed in Brapci, Scopus and Web of Science was carried out, and three publications from each were retrieved. After excluding one title that was repeated, the research corpus consisted of six articles and two abstracts presented at an event. We conclude that debates about the new modus operandi of doing science have been increasing and librarians seem closely related to Open Science actions in the various stages of scientific research. Because of their skills and services, they play one of the central roles to achieve the opening of science.

Este studio tiene como objetivo identificaren la literature científica los productos y servicios desarrollados por los bibliotecarios com vistas a las prácticas de la Ciencia Abierta. La cuestión principal es identificar: ¿ cuál es el papel de los bibliotecarios ante los desafíos de la Ciencia Abierta? Predominantemente cualita-tiva, esta investigación puede caracterizar se como bibliográfica, exploratoria y descriptiva. Para lograr su objetivo, se utilizó la técnica de revision rápida de la literatura. Se realizó un estudio de las publicaciones indexadas en Brapci, Scopus y Web of Science, recuperándo se tres publicaciones en cada una de ellas. Al excluir un título repetido, el corpus de la investigación quedó configurado con seis artículos y dos resúmenes presentados en un evento. Concluimos que los debates sobre el nuevo modus operandi de hacer ciencia han aumentado y los bibliotecarios parecen estar estrechamente relacionados con las acciones de la Ciencia Abierta en las distintas etapas de la investigación científica. Por sus habilidades y servicios, se entiende que ejercen uno de los papeles centrales en la realización de la Ciencia Abierta.

Big data analytics for MerTK genomics reveals its double-edged sword functions in human diseases.

RATIONALE: MER proto-oncogene tyrosine kinase (MerTK) is a key receptor for the clearance of apoptotic cells (efferocytosis) and plays important roles in redox-related human diseases. We will explore MerTK biology in human cells, tissues, and diseases based on big data analytics. METHODS: The human RNA-seq and scRNA-seq data about 42,700 samples were from NCBI Gene Expression Omnibus and analyzed by QIAGEN Ingenuity Pathway Analysis (IPA) with about 170,000 crossover analysis. MerTK expression was quantified as Log2 (FPKM + 0.1). RESULTS: We found that, in human cells, MerTK is highly expressed in macrophages, monocytes, progenitor cells, alpha-beta T cells, plasma B cells, myeloid cells, and endothelial cells (ECs). In human tissues, MerTK has higher expression in plaque, blood vessels, heart, liver, sensory system, artificial tissue, bone, adrenal gland, central nervous system (CNS), and connective tissue. Compared to normal conditions, MerTK expression in related tissues is altered in many human diseases, including cardiovascular diseases, cancer, and brain disorders. Interestingly, MerTK expression also shows sex differences in many tissues, indicating that MerTK may have different impact on male and female. Finally, based on our proteomics from primary human aortic ECs, we validated the functions of MerTK in several human diseases, such as cancer, aging, kidney failure and heart failure. CONCLUSIONS: Our big data analytics suggest that MerTK may be a promising therapeutic target, but how it should be modulated depends on the disease types and sex differences. For example, MerTK inhibition emerges as a new strategy for cancer therapy due to it counteracts effect on anti-tumor immunity, while MerTK restoration represents a promising treatment for atherosclerosis and myocardial infarction as MerTK is cleaved in these disease conditions.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network.

BACKGROUND: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. METHODS: Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. RESULTS: Model development included 41 929 patients, 14.6% with POU. The external validation included 31 932 (UK), 23 100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). CONCLUSIONS: Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE).

In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.

Hyperthyroidism and cardiovascular disease: an association study using big data analytics.

BACKGROUND: The cardiovascular (CV) system is profoundly affected by thyroid hormones. Both hypo- and hyperthyroidism can increase the risk of severe CV complications. OBJECTIVE: To assess the association of hyperthyroidism with major CV risk factors (CVRFs) and CV diseases (CVDs) using a big data methodology with the Savana Manager platform. MATERIAL AND METHODS: This was an observational and retrospective study. The data were obtained from the electronic medical records of the University Hospital Puerta de Hierro Majadahonda (Spain). Artificial intelligence techniques were used to extract the information from the electronic health records and Savana Manager 3.0 software was used for analysis. RESULTS: Of a total of 540,939 patients studied (53.62% females; mean age 42.2 ± 8.7 years), 5504 patients (1.02%; 69.9% women) had a diagnosis of hyperthyroidism. Patients with this diagnosis had a significantly (p < 0.0001) higher frequency of CVRFs than that found in non-hyperthyroid subjects. The higher frequency of CVRFs in patients with hyperthyroidism was observed in both women and men and in patients younger and older than 65 years of age. The total frequency of CVDs was also significantly (p < 0.0001) higher in patients diagnosed with hyperthyroidism than that found in patients without this diagnosis. The highest odds ratio values obtained were 6.40 (4.27-9.61) for embolic stroke followed by 5.99 (5.62-6.38) for atrial fibrillation. The frequency of all CVDs evaluated in patients with a diagnosis of hyperthyroidism was significantly higher in both women and men, as well as in those younger and older than 65 years, compared to subjects without this diagnosis. A multivariate regression analysis showed that hyperthyroidism was significantly and independently associated with all the CVDs evaluated except for embolic stroke. CONCLUSION: The data from this hospital cohort suggest that there is a significant association between the diagnosis of hyperthyroidism and the main CVRFs and CVDs in our population, regardless of the age and gender of the patients. Our study, in addition to confirming this association, provides useful information for understanding the applicability of artificial intelligence techniques to "real-world data and information".

Artificial Intelligence in Surgical Research: Accomplishments and Future Directions.

MINI-ABSTRACT: The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data. OBJECTIVE: Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice. SUMMARY BACKGROUND DATA: The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies. METHODS: The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm. RESULTS AND CONCLUSIONS: The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.

Data analytics improves the diagnostic accuracy of serum free light chain results for detecting monoclonal gammopathy.

OBJECTIVES: To evaluate the real-world performance and reference intervals of the Binding Site Freelite serum free light chain (SFLC) assay (Thermo Fisher Scientific), a global standard for diagnosis, prognostication, and response assessment for monoclonal gammopathies. METHODS: An informatics-based approach was used to retrospectively evaluate concordance between SFLC and the orthogonal Sebia HYDRASYS immunofixation assay results in a large clinical data set consecutively reported between 2010 and 2020. RESULTS: Among patients with monoclonal-negative results by both SFLC and Sebia HYDRASYS immunofixation assays, 25% (1226/5057) had κ/λ ratios (KLRs) outside the manufacturer-defined and International Myeloma Working Group-cited normal reference interval of 0.26 to 1.65. These results were consistent over the study period and were not affected by sex, age, impaired kidney function, or assay antisera lot variation. Assay drift, in addition to other potential factors, affected the KLR distribution. Using International Statistical Classification of Diseases (ICD) codes, kidney function data, and the central 95% of KLR values generated on the Optilite platform (Thermo Fisher Scientific), we derived a new reference interval of 0.67 to 2.13, reducing the KLR false-positive rate to 8%. However, normal KLR persisted among 16% (14/85) of samples with free λ chains by immunofixation, warranting caution during interpretation. CONCLUSIONS: Our analysis indicated that revision of Freelite SFLC reference intervals improves assay interpretation and should prompt reconsideration of Freelite reference intervals worldwide.

Keratoconus in India: Clinical presentation and demographic distribution based on big data analytics.

PURPOSE: This paper aims to describe the clinical presentation and demographic distribution of keratoconus (KCN) in India by analyzing the electronic medical records (EMR) of patients presenting at a multitier ophthalmology hospital network. METHODS: This cross-sectional hospital-based study included the data of 2,384,523 patients presenting between January 2012 and March 2020. Data were collected from an EMR system. Patients with a clinical diagnosis of KCN in at least one eye were included in this study. Univariate analysis was performed to identify the prevalence of KCN. A multiple logistic regression analysis was performed using R software (version 3.5.1), and the odds ratios are reported. RESULTS: Data were obtained for 14,749 (0.62%) patients with 27,703 eyes diagnosed with KCN and used for the analysis. The median age of the patients was 22 (inter-quartile range (IQR): 17-27). In total, 76.64% of adults (odds ratio = 8.77; P = <0.001) were affected the most. The majority of patients were male (61.25%), and bilateral (87.83%) affliction was the most common presentation. A significant proportion of the patients were students (63.98%). Most eyes had mild or no visual impairment (<20/70; 61.42%). Corneal signs included ectasia (41.35%), Fleischer ring (44.52%), prominent corneal nerves (45.75%), corneal scarring (13.60%), Vogts striae (18.97%), and hydrops (0.71%). Only 7.85% showed an association with allergic conjunctivitis. A contact lens clinic assessment was administered to 47.87% of patients. Overall, 10.23% of the eyes affected with KCN underwent a surgical procedure. the most common surgery was collagen cross-linking (8.05%), followed by deep anterior lamellar keratoplasty (1.13%) and penetrating keratoplasty (0.88%). CONCLUSION: KCN is usually bilateral and predominantly affects males. It commonly presents in the second and third decade of life, and only a tenth of the affected eyes require surgical treatment.

Clinical profile, demographic distribution, and management of Posner-Schlossman syndrome: An electronic medical record-driven data analytics from an eye care network in India.

PURPOSE: To describe the clinical profile, demographics, and management of Posner-Schlossman syndrome (PSS) in patients presenting to a multitier ophthalmology hospital network in India. METHODS: This cross-sectional hospital-based study included 3,082,727 new patients presenting between August 2010 and December 2021. Patients with a clinical diagnosis of PSS in at least one eye were included as cases. The data were collected using an electronic medical record system. RESULTS: Overall, 130 eyes of 126 (0.004%) patients were diagnosed with PSS. The majority of the patients were male (81.75%) and had unilateral (96.83%) affliction. The most common age group at presentation was during the fourth decade of life, with 46 (36.5%) patients. The overall prevalence was higher in patients from a higher socioeconomic status (0.005%) presenting from the metropolitan geography (0.008%) and in professionals (0.014%). A significant number of patients (108; 83.08%) had a raised intraocular pressure of >30 mm of Hg. The majority of the eyes had mild or no visual impairment (better than 20/70) in 99 (76.15%) eyes. Keratic precipitates were found in 59 (45.38%) eyes, anterior chamber cells in 43 (33.08%) eyes, and iris atrophy in seven (5.38%) eyes. The majority of eyes (127; 97.69%) had open angles on gonioscopy. The average duration of use of topical steroids was 1.70 ± 0.76 months, and the average duration of use of topical antiglaucoma medications (AGMs) was 1.66 ± 0.81 months, with 35 eyes (26.92%) requiring continued AGMs. Among the surgical interventions, trabeculectomy was performed in nine (6.92%) eyes and cataract surgery in five (3.85%) eyes. CONCLUSION: PSS more commonly affects males presenting during the fourth decade of life from higher socioeconomic status and is predominantly unilateral. The majority of the eyes have mild or no visual impairment, open angles, and require surgical intervention in a tenth of the eyes.

Big Data-Planetary Health approach for evaluating the Brazilian Dengue Control Program / Abordagem Big Data-Saúde Planetária para avaliação do Programa de Combate à Dengue

ABSTRACT OBJECTIVE This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.

RESUMO OBJETIVO Integrar os conceitos de Saúde Planetária e Big Data ao modelo de Donabedian, para avaliar o Programa de Combate à Dengue no estado de São Paulo. MÉTODOS Foram adotados métodos de Ciência de Dados para integração e análise de dados relacionados à dengue, agregando o contexto aos componentes de estrutura e de resultado do modelo de Donabedian. Esses dados, considerando o período de 2010 a 2019, foram coletados de fontes como Datasus, Instituto Brasileiro de Geografia e Estatística (IBGE), WorldClim e MapBiomas, e integrados em um Data Warehouse. Para a identificação de grupos com contextos similares, foi utilizado o algoritmo K-means. Em seguida, foram realizadas análises estatísticas e visualizações espaciais dos grupos, considerando variáveis socioeconômicas, demográficas, solo, estrutura de saúde e casos de dengue. RESULTADOS Com o uso das variáveis climáticas, o algoritmo K-means identificou quatro grupos de municípios com características similares. A comparação dos seus indicadores revelou certos padrões nos municípios com pior desempenho quanto aos resultados de casos de dengue. Embora tivessem melhores condições econômicas, eles tinham menor número médio de agentes comunitários e de unidades básicas de saúde por habitante. Dessa forma, as condições econômicas não refletiram em melhor estrutura de saúde nos três indicadores avaliados. Outra característica desses municípios é a urbanização. Os municípios de pior desempenho tinham maior taxa de população urbana e de modificações antrópicas relacionadas à urbanização. CONCLUSÕES Por meio desta metodologia, foi possível identificar importantes deficiências nas condições para a execução do programa de combate à dengue no estado de São Paulo. A integração de diversas bases de dados e a utilização de métodos de Ciência de Dados permitiram a avaliação do programa em larga escala, considerando o contexto em que as ações são executadas. Dessa forma, a gestão pública pode utilizar as informações coletadas para planejar ações e investir de acordo com as deficiências de cada local.

CytoCopasi: a chemical systems biology target and drug discovery visual data analytics platform.

MOTIVATION: Target discovery and drug evaluation for diseases with complex mechanisms call for a streamlined chemical systems analysis platform. Currently available tools lack the emphasis on reaction kinetics, access to relevant databases, and algorithms to visualize perturbations on a chemical scale providing quantitative details as well streamlined visual data analytics functionality. RESULTS: CytoCopasi, a Maven-based application for Cytoscape that combines the chemical systems analysis features of COPASI with the visualization and database access tools of Cytoscape and its plugin applications has been developed. The diverse functionality of CytoCopasi through ab initio model construction, model construction via pathway and parameter databases KEGG and BRENDA is presented. The comparative systems biology visualization analysis toolset is illustrated through a drug competence study on the cancerous RAF/MEK/ERK pathway. AVAILABILITY AND IMPLEMENTATION: The COPASI files, simulation data, native libraries, and the manual are available on https://github.com/scientificomputing/CytoCopasi.

A comparison study on creating simulated patient data for individuals suffering from chronic coronary disorders.

An emerging area in data science that has lately gained attention is the virtual population (VP) and synthetic data generation. This field has the potential to significantly affect the healthcare industry by providing a means to augment clinical research databases that have a shortage of subjects. The current study provides a comparative analysis of five distinct approaches for creating virtual data populations from real patient data. The data set utilized for the current analyses involved clinical data collected among patients scheduled for elective coronary artery bypass graft surgery (CABG). To that end, the five computational techniques employed to augment the given dataset were: (i) Tabular Preset, (ii) Gaussian Copula Model (iii) Generative Adversarial Network based (GAN) Deep Learning data synthesizer (CTGAN), (iv) a variation of the CTGAN Model (Copula GAN), and (v) VAE-based Deep Learning data synthesizer (TVAE). The performance of these techniques was assessed against their effectiveness in producing high-quality virtual data. For this purpose, dataset correlation matrices, cosine similarity distance, density histograms, and kernel density estimation are employed to perform a comparative analysis of each attribute and the respective synthetic equivalent. Our findings demonstrate that Gaussian Copula Model prevails in creating virtual data with consistent distributions (Kolmogorov-Smirnov (KS) and Chi-Squared (CS) tests equal to 0.9 and 0.98, respectively) and correlation patterns (average cosine similarity equals to 0.95).Clinical Relevance- It has been shown that the use of a VP can increase the predictive performance of a ML model, i.e., above using a smaller non-augmented population.