ONCO-FAIR Project: Improving Data Interoperability in Oncology Chemotherapy Treatments for Data Reuse.

The ONCO-FAIR project's initial experimentation aims to enhance data interoperability in oncology chemotherapy treatments, adhering to the FAIR principles. This study focuses on integrating the HL7 FHIR standard to address interoperability challenges within chemotherapy data exchange. Collaborating with healthcare institutions in Rennes, the research team assessed the limitations of current standards such as PN13, mCODE, and OSIRIS, leading to the customization of twelve FHIR resources complemented by two chemotherapy-specific extensions. The methodological approach follows the Integrating the Healthcare Enterprise (IHE) framework, organizing the process into four key stages to ensure the effectiveness and relevance of health data reuse for research. This framework facilitated the identification of chemotherapy-specific needs, the evaluation of existing standards, and data modeling through a FHIR implementation guide. The article underscores the importance of upstream interoperability for aligning chemotherapy software with clinical data warehouse infrastructure, showcasing the proposed solution's capability to overcome interoperability barriers and promote data reuse in line with FAIR principles. Furthermore, it discusses future directions, including extending this approach to other oncology data categories and enhancing downstream interoperability with health data sharing platforms.

Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment.

PURPOSE: In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. METHODS: With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. RESULTS: Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. CONCLUSION: Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.

OBJECTIVE: To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow. MATERIALS AND METHODS: The detection pipeline relied both on rule-based and machine learning algorithms, respectively, for named entity recognition and entity qualification, respectively. We used a large language model pre-trained on millions of clinical notes along with annotated clinical notes in the context of 3 cohort studies related to oncology, cardiology, and rheumatology. The overall workflow was conceived to foster collaboration between studies while respecting the privacy constraints of the data warehouse. We estimated the added values of the advanced technologies and of the collaborative setting. RESULTS: The pipeline reached macro-averaged F1-score positive predictive value, sensitivity, and specificity of 95.7 (95%CI 94.5-96.3), 95.4 (95%CI 94.0-96.3), 96.0 (95%CI 94.0-96.7), and 99.2 (95%CI 99.0-99.4), respectively. F1-scores were superior to those observed using alternative technologies or non-collaborative settings. The models were shared through a secured registry. CONCLUSIONS: We demonstrated that a community of investigators working on a common clinical data warehouse could efficiently and securely collaborate to develop, validate and use sensitive artificial intelligence models. In particular, we provided an efficient and robust NLP pipeline that detects conditions mentioned in clinical notes.

Pediatric Optic Pathway Gliomas Resource Utilization and Prevalence in the OptumLabs Data Warehouse.

BACKGROUND: Although significant progress has been made in improving the rate of survival for pediatric optic pathway gliomas (OPGs), data describing the methods of diagnosis and treatment for OPGs are limited in the modern era. This retrospective study aims to provide an epidemiological overview in the pediatric population and an update on eye care resource utilization in OPG patients using big data analysis. METHODS: Using the OptumLabs Data Warehouse, 9-11 million children from 2016 to 2021 assessed the presence of an OPG claim. This data set was analyzed for demographic distribution data and clinical data including average ages for computed tomography (CT), MRI, strabismus, and related treatment (surgery, chemotherapy, and radiation), as well as yearly rates for optical coherence tomography (OCT) and visual field (VF) examinations. RESULTS: Five hundred fifty-one unique patients ranging in age from 0 to 17 years had an OPG claim, with an estimated prevalence of 4.6-6.1 per 100k. Among the 476 OPG patients with at least 6 months of follow-up, 88.9% had at least one MRI and 15.3% had at least one CT. Annual rates for OCT and VF testing were similar (1.26 vs 1.35 per year), although OCT was ordered for younger patients (mean age = 9.2 vs 11.7 years, respectively). During the study period, 14.1% of OPG patients had chemotherapy, 6.1% had either surgery or radiation, and 81.7% had no treatment. CONCLUSIONS: This study updates OPG demographics for the modern era and characterizes the burden of the treatment course for pediatric OPG patients using big data analysis of a commercial claims database. OPGs had a prevalence of about 0.005% occurring equally in boys and girls. Most did not receive treatment, and the average child had at least one claim for OCT or VF per year for clinical monitoring. This study is limited to only commercially insured children, who represent approximately half of the general child population.

Maternal, Fetal and Neonatal Outcomes Related to Recreational Cannabis Use during Pregnancy: Analysis of a Real-World Clinical Data Warehouse between 2010 and 2019.

BACKGROUND: Cannabis is the main illicit psychoactive substance used in French childbearing women and very few data are available about adverse events (AEs) related to its use during pregnancy. The aim of this study was to evaluate the association between recreational cannabis use during pregnancy and adverse outcomes from a real-world clinical data warehouse. METHODS: Data from the Poitiers University Hospital warehouse were analyzed between 1 January 2010 and 31 December 2019. Logistic regression models were used to evaluate associations between outcomes in three prenatal user groups: cannabis alone ± tobacco (C ± T) (n = 123), tobacco alone (T) (n = 191) and controls (CTRL) (n = 355). RESULTS: Pregnant women in the C ± T group were younger (mean age: 25.5 ± 5.7 years), had lower pre-pregnancy body mass index (22.8 ± 5.5 kg/m2), more psychiatric history (17.5%) and were more likely to benefit from universal free health-care coverage (18.2%) than those in the T and CTRL groups. Cannabis use increases the occurrence of voluntary interruption of pregnancy, at least one AE during pregnancy, at least one neonatal AE, the composite adverse pregnancy outcome over 28, prematurity and small for gestational age. CONCLUSION: Given the trivialization of recreational cannabis use during pregnancy, there is an urgent need to communicate on AEs of cannabis use during pregnancy.

A Clinical Data Warehouse Analysis of Risk Factors for Inpatient Falls in a Tertiary Hospital: A Case-Control Study.

OBJECTIVES: The aims of the study are to identify fall risk factors and to establish automatic risk assessments based on clinical data from electronic medical records of hospitalized patients. METHODS: In this retrospective case-control study, we reviewed the electronic medical records of 1454 patients (292 and 1162 patients in the fall and nonfall groups, respectively) who were hospitalized at a 1800-bed tertiary hospital in South Korea between January 1, 2017, and December 31, 2017. Patients' age, sex, and clinical department were matched, and all laboratory reports, clinical flow sheets, and nursing initial assessment records of case from the Clinical Data Warehouse system were analyzed. The collated patient records data were analyzed using SAS (version 9.4) and logistic regression. RESULTS: Overall, 65 risk factors, including low body mass index, low blood pressure, low albumin levels, high fasting blood sugar level, low red blood cell counts, and high potassium levels, that significantly increased the incidence of falls were identified. Falls were also associated with 21 items from the clinical flow sheet and nursing initial assessment, including frequent bowel movements, 24-hour urine tests, imaging tests, biopsy, pain, intravenous tubes, unclear consciousness, and taking medication. CONCLUSIONS: Fall risk factors identified via the Clinical Data Warehouse can be used to build an automated detection system to detect fall risk in electronic medical records, enabling nurses to assess the fall risk in addition to using the fall scale.

Integrated Real-World Study Databases in 3 Diverse Asian Health Care Systems in Taiwan, India, and Thailand: Scoping Review.

BACKGROUND: The use of real-world data (RWD) warehouses for research in Asia is on the rise, but current trends remain largely unexplored. Given the varied economic and health care landscapes in different Asian countries, understanding these trends can offer valuable insights. OBJECTIVE: We sought to discern the contemporary landscape of linked RWD warehouses and explore their trends and patterns in 3 Asian countries with contrasting economies and health care systems: Taiwan, India, and Thailand. METHODS: Using a systematic scoping review methodology, we conducted an exhaustive literature search on PubMed with filters for the English language and the past 5 years. The search combined Medical Subject Heading terms and specific keywords. Studies were screened against strict eligibility criteria to identify eligible studies using RWD databases from more than one health care facility in at least 1 of the 3 target countries. RESULTS: Our search yielded 2277 studies, of which 833 (36.6%) met our criteria. Overall, single-country studies (SCS) dominated at 89.4% (n=745), with cross-country collaboration studies (CCCS) being at 10.6% (n=88). However, the country-wise breakdown showed that of all the SCS, 623 (83.6%) were from Taiwan, 81 (10.9%) from India, and 41 (5.5%) from Thailand. Among the total studies conducted in each country, India at 39.1% (n=133) and Thailand at 43.1% (n=72) had a significantly higher percentage of CCCS compared to Taiwan at 7.6% (n=51). Over a 5-year span from 2017 to 2022, India and Thailand experienced an annual increase in RWD studies by approximately 18.2% and 13.8%, respectively, while Taiwan's contributions remained consistent. Comparative effectiveness research (CER) was predominant in Taiwan (n=410, or 65.8% of SCS) but less common in India (n=12, or 14.8% of SCS) and Thailand (n=11, or 26.8% of SCS). CER percentages in CCCS were similar across the 3 countries, ranging from 19.2% (n=10) to 29% (n=9). The type of RWD source also varied significantly across countries, with India demonstrating a high reliance on electronic medical records or electronic health records at 55.6% (n=45) of SCS and Taiwan showing an increasing trend in their use over the period. Registries were used in 26 (83.9%) CCCS and 31 (75.6%) SCS from Thailand but in <50% of SCS from Taiwan and India. Health insurance/administrative claims data were used in most of the SCS from Taiwan (n=458, 73.5%). There was a consistent predominant focus on cardiology/metabolic disorders in all studies, with a noticeable increase in oncology and infectious disease research from 2017 to 2022. CONCLUSIONS: This review provides a comprehensive understanding of the evolving landscape of RWD research in Taiwan, India, and Thailand. The observed differences and trends emphasize the unique economic, clinical, and research settings in each country, advocating for tailored strategies for leveraging RWD for future health care research and decision-making. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/43741.

Early Ascertainment of Breast Cancer Diagnoses Comparing Self-Reported Questionnaires and Electronic Health Record Data Warehouse: The WISDOM Study.

PURPOSE: The goal of this study was to use real-world data sources that may be faster and more complete than self-reported data alone, and timelier than cancer registries, to ascertain breast cancer cases in the ongoing screening trial, the WISDOM Study. METHODS: We developed a data warehouse procedural process (DWPP) to identify breast cancer cases from a subgroup of WISDOM participants (n = 11,314) who received breast-related care from a University of California Health Center in the period 2012-2021 by searching electronic health records (EHRs) in the University of California Data Warehouse (UCDW). Incident breast cancer diagnoses identified by the DWPP were compared with those identified by self-report via annual follow-up online questionnaires. RESULTS: Our study identified 172 participants with confirmed breast cancer diagnoses in the period 2016-2021 by the following sources: 129 (75%) by both self-report and DWPP, 23 (13%) by DWPP alone, and 20 (12%) by self-report only. Among those with International Classification of Diseases 10th revision cancer diagnostic codes, no diagnosis was confirmed in 18% of participants. CONCLUSION: For diagnoses that occurred ≥20 months before the January 1, 2022, UCDW data pull, WISDOM self-reported data via annual questionnaire achieved high accuracy (96%), as confirmed by the cancer registry. More rapid cancer ascertainment can be achieved by combining self-reported data with EHR data from a health system data warehouse registry, particularly to address self-reported questionnaire issues such as timing delays (ie, time lag between participant diagnoses and the submission of their self-reported questionnaire typically ranges from a month to a year) and lack of response. Although cancer registry reporting often is not as timely, it does not require verification as does the DWPP or self-report from annual questionnaires.

Why Are Data Missing in Clinical Data Warehouses? A Simulation Study of How Data Are Processed (and Can Be Lost).

In recent years, the development of clinical data warehouses (CDW) has put Electronic Health Records (EHR) data in the spotlight. More and more innovative technologies for healthcare are based on these EHR data. However, quality assessments on EHR data are fundamental to gain confidence in the performances of new technologies. The infrastructure developed to access EHR data - CDW - can affect EHR data quality but its impact is difficult to measure. We conducted a simulation on the Assistance Publique - Hôpitaux de Paris (AP-HP) infrastructure to assess how a study on breast cancer care pathways could be affected by the complexity of the data flows between the AP-HP Hospital Information System, the CDW, and the analysis platform. A model of the data flows was developed. We retraced the flows of specific data elements for a simulated cohort of 1,000 patients. We estimated that 756 [743;770] and 423 [367;483] patients had all the data elements necessary to reconstruct the care pathway in the analysis platform in the "best case" scenarios (losses affect the same patients) and in a random distribution scenario (losses affect patients at random), respectively.

How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of the PENELOPE Initiative in Urologic Cancers.

PURPOSE: To compare the computability of Observational Medical Outcomes Partnership (OMOP)-based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool. MATERIALS AND METHODS: We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs. RESULTS: We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively. CONCLUSION: We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

Integrating a new knowledge organisation system for monoclonal antibodies for therapeutic use authorised in Europe into HeTOP terminology-ontology server.

Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs. The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes. Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data). The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody).

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.

PURPOSE: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system. METHODS: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine. RESULTS: The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95. CONCLUSION: This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

[Performing pharmacoepidemiological studies using the French health insurance data warehouse (SNDS): How to translate guidelines into practice]. / Réalisation d'études pharmacoépidémiologiques à partir du système national des données de santé (SNDS) : application des recommandations dans la pratique.

The French health insurance data warehouse named SNDS is one of the largest medico-administrative in the world allowing for powerful pharmacoepidemiological studies, based on real-life data collected prospectively. In addition to the absolute necessity of a strong pharmacological rationale, recommendations have been thought to improve the quality of pharmacoepidemiological studies. These guidelines emphasize the importance of an accurate definition of the study population, outcome and exposure, especially for studies performed on medico-administrative databases. Compliance with certain guidelines, particularly those concerning the identification of a specific population or an outcome and the definition of risk periods or exposure periods, may be difficult when performing studies on the SNDS because of its structure and the nature of the data recorded. The objective of this article is to provide advice for the conduct of pharmacoepidemiological studies according to the recommendationswhen using the SNDS, given its specificities. The performing of reliable studies from this rich but complex data warehouse requires the expertise of researchers with deep knowledge both in the SNDS and in pharmacological reasoning.

Deep learning approach to detection of colonoscopic information from unstructured reports.

BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS: This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS: The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS: This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers.

Big Data and Artificial Intelligence can profoundly transform medical practices, particularly in oncology. Comprehensive Cancer Centers have a major role to play in this revolution. With the purpose of advancing our knowledge and accelerating cancer research, it is urgent to make this pool of data usable through the development of robust and effective data warehouses. Through the recent experience of Comprehensive Cancer Centers in France, this article shows that, while the use of hospital data warehouses can be a source of progress by taking into account multisource, multidomain and multiscale data for the benefit of knowledge and patients, it nevertheless raises technical, organizational and legal issues that still need to be addressed. The objectives of this article are threefold: 1. to provide insight on public health stakes of development in Comprehensive Cancer Centers to manage cancer patients comprehensively; 2. to set out a challenge of structuring the data from within them; 3. to outline the legal issues of implementation to carry out real-world evidence studies. To meet objective 1, this article firstly proposed a discussion on the relevance of an integrated approach to manage cancer and the formidable tool that data warehouses represent to achieve this. To address objective 2, we carried out a literature review to screen the articles published in PubMed and Google Scholar through the end of 2022 on the use of data warehouses in French Comprehensive Cancer Centers. Seven publications dealing specifically with the issue of data structuring were selected. To achieve objective 3, we presented and commented on the main aspects of French and European legislation and regulations in the field of health data, hospital data warehouses and real-world evidence.

Dental developmental complications in pediatric hematopoietic stem cell transplantation patients: A study using CMC clinical data warehouse.

OBJECTIVE: This study aimed to investigate the prevalence and extent of dental developmental complications in patients who have undergone pediatric hematopoietic stem cell transplantation (SCT) and identify the risk factors. MATERIALS AND METHODS: We retrospectively investigated the clinical data warehouse of the Catholic Medical Center information system for identifying patients who: 1) visited the Department of Pediatrics between 2009 and 2019, 2) underwent SCT under the age of 10, and 3) had panoramic radiographs. Thus 153 patients were included in this study. The prevalence and extent of tooth agenesis, microdontia, and root malformation were assessed using panoramic radiographs obtained after SCT, and the risk factors were analyzed using regression analysis. RESULTS: All 153 patients had at least one dental anomaly. When grouped according to the age at initial chemotherapy (≤ 2.5; 2.6-5.0; 5.1-7.5; > 7.5 years), the prevalence of agenesis showed statistically significant differences among the different age groups (P < 0.001). The prevalence of agenesis was highest in the youngest age group. As the initial age at chemotherapy increased, the number of affected teeth per patient decreased for all three anomalies. The location of the affected tooth was also influenced by the age at initial chemotherapy. Regression analysis demonstrated that young age at initial chemotherapy was a risk-increasing factor for tooth agenesis and microdontia. CONCLUSIONS: The age at initial chemotherapy may be a critical factor in determining the type, extent, and location of dental complications after SCT. These results suggest that careful dental follow-up and timely treatment are recommended for pediatric patients undergoing SCT.

Sistema inteligente para la medición del comportamiento humano en relación al cumplimiento del protocolo de bioseguridad Covid-19 implementando tecnología de IoB / Smart system for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing IoB technology

Este proyecto de investigación 2021 desarrollado por la Escuela de Ingeniería en Computación de ITCA-FEPADE, tuvo como objetivo usar las tecnologías para ayudar a mejorar el comportamiento de la comunidad educativa en pandemia Covid-19. Es un sistema inteligente para la medición del comportamiento humano con relación al cumplimiento del protocolo de bioseguridad Covid-19, implementando tecnologías de Internet del Comportamiento IoB, Internet de las Cosas IoT, Business Intelligence, Big Data y reconocimiento facial. La primera fase consistió en la toma de requerimientos y el estudio de investigaciones previas. Posteriormente se diseñó la interfaz del aplicativo que interpreta los datos colectados y la estructura de un dispensador inteligente de alcohol gel para ser impreso en 3D. Finalmente se realizó la programación del sistema y del circuito que conforman el dispositivo. Como resultado se construyó un dispositivo inteligente que mide y alerta la temperatura, dispensa alcohol gel y toma de fotografía para reconocimiento facial en la portación correcta de mascarilla. Incorpora un sistema informático que procesa los datos colectados que son utilizados por la aplicación de Inteligencia de Negocios para analizar el comportamiento de las personas ante el cumplimiento del protocolo de bioseguridad para Covid-19. El resultado del proyecto es un dispositivo inteligente y automatizado, que dotará a la institución de una herramienta innovadora de bajo costo para medir el comportamiento de la población que hace uso de las instalaciones de ITCA-FEPADE Sede Central y contribuirá a prevenir contagios por Covid-19, dando mayor seguridad a un retorno presencial al campus.

This research project was carried out in 2021 by the Escuela de Ingeniería en Computación of ITCA-FEPADE and aimed to use technologies to improve the behavior of the educational community in the context of Covid-19 pandemic. A smart system was development for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing Internet of Behavior (IoB), Internet of Things (IoT), Business Intelligence, Big Data and facial recognition technologies. The first phase consisted on the identification of requirements and previous investigations. Subsequently, the application interface that interprets the collected data and the structure of a smart hand sanitizer dispenser to be printed in 3D was designed. Finally, the programming of the system and the circuit that make up the device was carried out. As a result, a smart device that measures and alerts the body temperature, dispenses hand sanitizer and applies facial recognition for the detection of proper face mask wearing was built. The device also incorporates a computer system that processes the collected data that to analyze the behavior of people in compliance with the biosafety protocol for Covid-19 through the Business Intelligence application. The result of the project was a smart and automated device that will provide the institution an innovative, low-cost tool to measure the behavior of the population that makes use of the ITCA-FEPADE Sede Central facilities and will contribute to preventing Covid-19 infections by giving greater safety to a face-to-face return to the facilities.

Prediction of Visual Outcomes After Diabetic Vitrectomy Using Clinical Factors From Common Data Warehouse.

Purpose: We sought to analyze the visual outcome and systemic prognostic factors for diabetic vitrectomy and predicted outcomes using these factors. Methods: This was a multicenter electronic medical records (EMRs) review study of 1504 eyes with type 2 diabetes that underwent vitrectomy for proliferative diabetic retinopathy at 6 university hospitals. Demographics, laboratory results, intra-operative findings, and visual acuity (VA) values were analyzed and correlated with visual outcomes at 1 year after the vitrectomy. Prediction models for visual outcomes were obtained using machine learning. Results: At 1 year, VA was 1.0 logarithm of minimal angle resolution (logMAR) or greater (poor visual outcome group) in 456 eyes (30%). Baseline visual acuity, duration of diabetes treatment, tractional membrane, silicone oil tamponade, smoking, and vitreous hemorrhage correlated with logMAR VA at 1 year (r = 0.450, -0.159, 0.221, 0.280, 0.067, and -0.105; all P ≤ 0.036). An ensemble decision tree model trained using all variables generated accuracy, specificity, F1 score (the harmonic means of which precision and sensitivity), and receiver-operating characteristic curve area under curve values of 0.77, 0.66, 0.85, and 0.84 for the prediction of poor visual outcomes at 1 year after vitrectomy. Conclusions: Visual outcome after diabetic vitrectomy is associated with pre- and intra-operative findings and systemic factors. Poor visual outcome after diabetic vitrectomy was predictable using clinical factors. Intensive care in patients who are predicted to result in poor vision may limit vision loss resulting from type 2 diabetes. Translational Relevance: This study demonstrates that a real world EMR big data could predict outcome after diabetic vitrectomy using clinical factors.

Determining the Set of Items to Include in Breast Operative Reports, Using Clustering Algorithms on Retrospective Data Extracted from Clinical DataWarehouse.

Medical reports are key elements to guarantee the quality, and continuity of care but their quality remains an issue. Standardization and structuration of reports can increase their quality, but are usually based on expert opinions. Here, we hypothesize that a structured model of medical reports could be learnt using machine learning on retrospective medical reports extracted from clinical data warehouses (CDW). To investigate our hypothesis, we extracted breast cancer operative reports from our CDW. Each document was preprocessed and split into sentences. Clustering was performed using TFIDF, Paraphrase or Universal Sentence Encoder along with K-Means, DBSCAN, or Hierarchical clustering. The best couple was TFIDF/K-Means, providing a sentence coverage of 89 % on our dataset; and allowing to identify 7 main categories of items to include in breast cancer operative reports. These results are encouraging for a document preset creation task and should then be validated and implemented in real life.

How to Optimize Connection Between PACS and Clinical Data Warehouse: A Web Service Approach Based on Full Metadata Integration.

Clinical image data analysis is an active area of research. Integrating such data in a Clinical Data Warehouse (CDW) implies to unlock the PACS and RIS and to address interoperability and semantics issues. Based on specific functional and technical requirements, our goal was to propose a web service (I4DW) that allows users to query and access pixel data from a CDW by fully integrating and indexing imaging metadata. Here, we present the technical implementation of this workflow as well as the evaluation we carried out using a prostate cancer cohort use case. The query mechanism relies on a Dicom metadata hierarchy dynamically generated during the ETL Process. We evaluated the Dicom data transfer performance of I4DW, and found mean retrieval times of 5.94 seconds and 0.9 seconds to retrieve a complete DICOM series from the PACS and all metadata of a series. We could retrieve all patients and imaging tests of the prostate cancer cohort with a precision of 0.95 and a recall of 1. By leveraging the CMOVE method, our approach based on the Dicom protocol is scalable and domain-neutral. Future improvement will focus on performance optimization and de identification.