DATA ANALYTICS FOR HOSPITAL OPERATIONS

Modern hospitals and clinics produce tons of electronic data every day regarding patients, medications, treatments, and diseases. These amounts of data contain the potential to help humanity understand and analyze the biomedical fields from a statistical and predictive point of view. Throughout the years, research has been concluded to develop methods of interpreting and analyzing this data. Biomedical statistical researchers have experimented algorithms to achieve findings and associations within the aspects of the data given. Medical decision-making is becoming more and more dependent on data analysis, rather than conventional experience and intuition. Hence, this project will look into the feasibility of developing software for hospital data analysis, specifically, the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) data that support a diverse range of analytic studies which extend across epidemiology, clinical decision-rule improvement, and electronic tool development. This statistical software is programmed to run multiple algorithms on the massive datasets in the mean of revealing similarities of items related to sets by focusing on the algorithm based of Market-Basket method. With the aim to assist in showing patterns and associations in hospital big data, the software reveals associations and apprehending patterns inside this data demonstrated as predictive analytics that can assist in handling comparable cases and present clinical and hospital-decision

DATA ANALYTICS FOR HOSPITAL OPERATIONS

Modern hospitals and clinics produce tons of electronic data every day regarding patients, medications, treatments, and diseases. These amounts of data contain the potential to help humanity understand and analyze the biomedical fields from a statistical and predictive point of view. Throughout the years, research has been concluded to develop methods of interpreting and analyzing this data. Biomedical statistical researchers have experimented algorithms to achieve findings and associations within the aspects of the data given. Medical decision-making is becoming more and more dependent on data analysis, rather than conventional experience and intuition. Hence, this project will look into the feasibility of developing software for hospital data analysis, specifically, the MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care) data that support a diverse range of analytic studies which extend across epidemiology, clinical decision-rule improvement, and electronic tool development. This statistical software is programmed to run multiple algorithms on the massive datasets in the mean of revealing similarities of items related to sets by focusing on the algorithm based of Market-Basket method. With the aim to assist in showing patterns and associations in hospital big data, the software reveals associations and apprehending patterns inside this data demonstrated as predictive analytics that can assist in handling comparable cases and present clinical and hospital-decision

The complexity of intracranial pressure as an indicator of cerebral autoregulation

Intracranial Pressure (ICP) is one of the main neuromonitories used today to guide the treatment of acute neurological patients in the Intensive Care Unit (ICU). Within this article the complexity of periods of intracranial hypertension is evaluated and compared with periods of stable intracranial tension. Using the multiparameter intelligent monitoring in intensive care III (MIMIC-III) database from the Beth Israel Deaconess Medical Center the complexity of periods of stable intracranial tension and high intracranial hypertension are evaluated using two quantifiers: the Permutation Entropy and their respective number of missing patterns. Both indicate a loss of complexity in hypertension signals. A physiological explanation of this loss of complexity is given using a dynamical model of the Cerebral Autoregulation and Cerebral Hemodynamics.Fil: Ciarrocchi, Nicolás. Hospital Italiano; ArgentinaFil: Quiroz, Nicolas. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Traversaro Varela, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Universidad Escuela de Medicina del Hospital Italiano; Argentina. Universidad Nacional de Lanús; ArgentinaFil: San Roman, Juan Eduardo. Instituto Universidad Escuela de Medicina del Hospital Italiano; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; ArgentinaFil: Goldemberg, Fernando. University of Chicago; Estados UnidosFil: Redelico, Francisco Oscar. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Hospital Italiano. Instituto de Medicina Traslacional E Ingenieria Biomedica. - Instituto Universitario Hospital Italiano de Buenos Aires. Instituto de Medicina Traslacional E Ingenieria Biomedica.; Argentin

Predicting Intensive Care Unit Length of Stay via Supervised Learning

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Laura Igual Muñoz[en] Healthcare is a traditional sector that is demanding, nowadays, a profound change regarding tasks and ways of work. The explotation of data-based analytical techniques together with computational capabilities are potential candidates to lead part of that demanding change. This can cause an innovation to the sector with considerable social impact. In any case, it is necessary to take into account the specific characteristics of the clinical data: quality, volume, access and multimodality. In this Master Thesis, an analysis of the data from critical patients was carried out in order to study the influence of several observables to determine their Length of Stay in the Intensive Care Unit. Try to solve that problem can help a lot not only the physicians from the mere investigation purposes point of view but also the healthcare sector because Intensive Care Unit logistics counts and it can become very important

FHIR-DHP: A standardized clinical data harmonisation pipeline for scalable AI application deployment

Background Increasing digitalisation in the medical domain gives rise to large amounts of healthcare data which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to non-standardised data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the healthcare system. Despite the existence of standardised data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remains limited.Objective We developed a data harmonisation pipeline (DHP) for clinical data sets relying on the common FHIR data standard.Methods We validated the performance and usability of our FHIR-DHP with data from the MIMIC IV database including > 40,000 patients admitted to an intensive care unit.Results We present the FHIR-DHP workflow in respect of transformation of “raw” hospital records into a harmonised, AI-friendly data representation. The pipeline consists of five key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonised data into the patient-model database and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.Conclusions Our approach enables scalable and needs-driven data modelling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step towards increasing cooperation, interoperability and quality of patient care in the clinical routine and for medical research

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions

The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models