Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.Facultad de Informátic

Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.Facultad de Informátic

Persisting non-albicans candidemia in low birth weight neonates in a tertiary care hospital, Jammu and Kashmir

Background: Neonatal candidemia is among the leading causes of mortality in neonatal intensive care units of the developing countries like India. This work aimed at determining the prevalence of candidemia, spectrum of disease, risk factors and the antifungal susceptibility in low birth weight neonates in neonatal intensive care unit (NICU)’s at a tertiary care level. Methods: This was a prospective cross-sectional study of blood culture positive candidemia cases in neonates admitted to the neonatal intensive care unit of tertiary care hospital, SMHS, Jammu and Kashmir, India, between July 2021 to December 2022. All neonates with a clinical suspicion of candidemia with a positive blood culture (BacT alert) were identified. Patient demographics, clinical details, neonatal risk factors, and laboratory data and antifungal susceptibilities (using VITEK 2 compact system) were recorded and analyzed. Results: A total of 680 neonatal blood culture samples were collected from NICU’s, out of which 88 (12.94%) developed candidemia. Low birth weight (33.33%), indwelling catheters (31.52%), prematurity (31.31%) and prolonged use of antibiotics were important risk factors. The commonest clinical manifestation was feed intolerance 66.1% and respiratory distress 62.2%. Non-albicans candida was seen in majority cases 86.36% with Candida krusei 77.27%. All the Candida spp. showed 100% sensitivity to voriconazole and caspofugin followed by amphotericin B, fluconazole and micafugin. Conclusions: In this study, we focussed on determining the prevalence of candidemia in low birth weight neonates. The persistently emerging non-albicans Candida particularly Candida krusei has emerged as a big concern and needs attention for its prevention and treatment to minimize the morbidity and mortality rate

A deep learning model for real-time mortality prediction in critically ill children

BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. METHODS: Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. RESULTS: Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89-0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. CONCLUSIONS: PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.ope

Feature selection using generalized linear model for Machine Learning-based sepsis prediction

Sepsis is a life-threatening condition of patients in an intensive care unit. Early sepsis detection can reduce the mortality rate and cost of treatment among the patients of the Intensive care unit (ICU). Machine Learning-based model can be used to predict sepsis early using Electronic Health Record (EHR) which consists of big data. Features selection plays a vital role for reducing overfitting and the accuracy of the MLbased prediction model. In this paper, Generalized Linear Model (GLM) was used to select the significant features related to sepsis using MIMIC-III dataset which is a rational database that contains ICU patient’s data at Beth Israel Deaconess Medical center. In addition, developed a sepsis prediction model using Artificial Neural Network (ANN) and Random Forest (RF) and validated those models using confusion matrix. After that, clinical severity scores were also calculated with the same dataset. Finally, compared the Area Under the Receiver Operating Characteristic (AUROC) between MLbased model and clinical severity score. The accuracy of MLbased prediction model with GLM is better than clinical severity scores like SOFA, qSOFA and SIR

Big data analytics in intensive care units: challenges and applicability in an Argentinian hospital

In a typical intensive care unit of a healthcare facilities, many sensors are connected to patients to measure high frequency physiological data. Currently, measurements are registered from time to time, possibly every hour. With this data lost, we are losing many opportunities to discover new patterns in vital signs that could lead to earlier detection of pathologies. The early detection of pathologies gives physicians the ability to plan and begin treatments sooner or potentially stop the progression of a condition, possibly reducing mortality and costs. The data generated by medical equipment are a Big Data problem with near real-time restrictions for processing medical algorithms designed to predict pathologies. This type of system is known as realtime big data analytics systems. This paper analyses if proposed system architectures can be applied in the Francisco Lopez Lima Hospital (FLLH), an Argentinian hospital with relatively high financial constraints. Taking into account this limitation, we describe a possible architectural approach for the FLLH, a mix of a local computing system at FLLH and a public cloud computing platform. We believe this work may be useful to promote the research and development of such systems in intensive care units of hospitals with similar characteristics to the FLLH.Facultad de Informátic

Origins of neonatal intensive care in the UK

Chaired by Professor Robert Boyd, this seminar reviewed the development and changes in care of the newborn in the UK over the past 50 years. Advances in techniques were described, such as mechanical ventilation, total parenteral nutrition and continuous monitoring of vital signs, to care for ill or vulnerable newborn infants. Diagnostic techniques that were developed and introduced in the 1970s and early 1980s were discussed, such as ultrasound imaging, magnetic resonance spectroscopy and imaging and near infrared spectroscopy, for the non-invasive investigation of the brain, as well as the setting up of neonatal intensive care units. Witnesses include: Professor Eva Alberman, Dr Herbert Barrie, Professor Richard Cooke, Dr Beryl Corner, Dr Pamela Davies, Professor John Davis, Professor David Delpy, Professor Victor and Dr Lilly Dubowitz, the late Professor Harold Gamsu, Professor David Harvey, Professor Colin Normand, Professor Tom Oppé, Professor Osmund Reynolds, Dr Jean Smellie, Professor Maureen Young and nurses, including Miss Anthea Blake, Miss Caroline Dux and Miss Mae Nugent. Introduction by Professor Peter Dunn, viii, 84pp, 1 chart, glossary, subject and name index