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

Baby incubator monitoring system using global system for mobile technology

Giving birth to a child is one of the precious moments in life. Every second a life is brought into the world and not many children are lucky enough to be healthy. Monitoring the health conditions of a baby in the incubator is a critical medical issue. Many researchers are working in this area to improve the safety of newborn babies. As far as the study that we proceeded with, there exists a fundamental issue in ensuring whether the doctor has attended the emergency or not. In this paper, a system is proposed to monitor the baby inside the incubator using a global system for mobile technology (GSM). The proposed system detects the baby’s temperature, heartbeat, weight, and baby’s sound inside the incubator. If there are any changes in the above-said parameters beyond the threshold level, an intimation will be sent to the concerned doctor through the GSM. The system will keep sending the alert message to the doctor every minute until the doctor acknowledges the baby’s condition. This system will enhance the safety of newborn babies by addressing the above-said issue, thereby reducing the risk involved in monitoring the babies inside the incubator. A prototype is developed and it was tested for functional verification

Internet of medical things – integrated, ultrasound-based respiration monitoring system for incubators

The study's aim was to develop a non-contact, ultrasound (US) based respiration rate and respiratory signal monitor suitable for babies in incubators. Respiration rate indicates average number of breaths per minute and is higher in young children than adults. It is an important indicator of health deterioration in critically ill patients. The current incubators do not have an integrated respiration monitor due to complexities in its adaptation. Monitoring respiratory signal assists in diagnosing respiration rated problems such as central Apnoea that can affect infants. US sensors are suitable for integration into incubators as US is a harmless and cost-effective technology. US beam is focused on the chest or abdomen. Chest or abdomen movements, caused by respiration process, result in variations in their distance to the US transceiver located at a distance of about 0.5 m. These variations are recorded by measuring the time of flight from transmitting the signal and its reflection from the monitored surface. Measurement of this delay over a time interval enables a respiration signal to be produced from which respiration rate and pauses in breathing are determined. To assess the accuracy of the developed device, a platform with a moving surface was devised. The magnitude and frequency of its surface movement were accurately controlled by its signal generator. The US sensor was mounted above this surface at a distance of 0.5 m. This US signal was wirelessly transmitted to a microprocessor board to digitise. The recorded signal that simulated a respiratory signal was subsequently stored and displayed on a computer or an LCD screen. The results showed that US could be used to measure respiration rate accurately. To cater for possible movement of the infant in the incubator, four US sensors were adapted. These monitored the movements from different angles. An algorithm to interpret the output from the four US sensors was devised and evaluated. The algorithm interpreted which US sensor best detected the chest movements. An IoMT system was devised that incorporated NodeMcu to capture signals from the US sensor. The detected data were transmitted to the ThingSpeak channel and processed in real-time by ThingSpeak’s add-on Matlab© feature. The data were processed on the cloud and then the results were displayed in real-time on a computer screen. The respiration rate and respiration signal could be observed remotely on portable devices e.g. mobile phones and tablets. These features allow caretakers to have access to the data at any time and be alerted to respiratory complications. A method to interpret the recorded US signals to determine respiration patterns, e.g. intermittent pauses, were implemented by utilising Matlab© and ThingSpeak Server. The method successfully detected respiratory pauses by identifying lack of chest movements. The approach can be useful in diagnosing central apnoea. In central apnoea, respiratory pauses are accompanied by cessation of chest or abdominal movements. The devised system will require clinical trials and integration into an incubator by conforming to the medical devices directives. The study demonstrated the integration of IoMT-US for measuring respiration rate and respiratory signal. The US produced respiration rate readings compared well with the actual signal generator's settings of the platform that simulated chest movements

Neonatal Health Care

This issue of Children concerns healthcare delivery and research in neonatology. Several articles concern the work of the California Perinatal Quality Care Collaborative, including a history by founder Dr. Jeffrey Gould, and recent quality improvement work. Other articles concern methodological issues in neonatal research and findings of recent clinical studies

Music in Medicine : the value of music interventions for hospitalised children

This thesis addresses the question: ‘music in medicine: does it work and should we use it in treating hospitalized children’? The overall aim was to find if live music therapy and recorded music interventions could reduce pain and distress from medical procedures