Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Hierarchical clustering reveals unique features in the diel dynamics of metabolites in the CAM orchid Phalaenopsis.

Crassulacean acid metabolism (CAM) is a major adaptation of photosynthesis that involves temporally separated phases of CO2 fixation and accumulation of organic acids at night, followed by decarboxylation and refixation of CO2 by the classical C3 pathway during the day. Transitory reserves such as soluble sugars or starch are degraded at night to provide the phosphoenolpyruvate (PEP) and energy needed for initial carboxylation by PEP carboxylase. The primary photosynthetic pathways in CAM species are well known, but their integration with other pathways of central C metabolism during different phases of the diel light-dark cycle is poorly understood. Gas exchange was measured in leaves of the CAM orchid Phalaenopsis 'Edessa' and leaves were sampled every 2 h during a complete 12-h light-12-h dark cycle for metabolite analysis. A hierarchical agglomerative clustering approach was employed to explore the diel dynamics and relationships of metabolites in this CAM species, and compare these with those in model C3 species. High levels of 3-phosphoglycerate (3PGA) in the light activated ADP-glucose pyrophosphorylase, thereby enhancing production of ADP-glucose, the substrate for starch synthesis. Trehalose 6-phosphate (T6P), a sugar signalling metabolite, was also correlated with ADP-glucose, 3PGA and PEP, but not sucrose, over the diel cycle. Whether or not this indicates a different function of T6P in CAM plants is discussed. T6P levels were low at night, suggesting that starch degradation is regulated primarily by circadian clock-dependent mechanisms. During the lag in starch degradation at dusk, carbon and energy could be supplied by rapid consumption of a large pool of aconitate that accumulates in the light. Our study showed similarities in the diel dynamics and relationships between many photosynthetic metabolites in CAM and C3 plants, but also revealed some major differences reflecting the specialized metabolic fluxes in CAM plants, especially during light-dark transitions and at night.

Development of a continuous reactor for emulsion-based microencapsulation of hexyl acetate with a polyuria shell.

Microencapsulation is almost exclusively performed in batch processes. With today's chemistry increasingly performed in flow reactors, this work aims to realise a continuous reactor setup for the encapsulation of an ester with a polyuria (PU) shell. The generation of an emulsion template is performed in a recirculation loop driven by a pump and equipped with static mixers, screen type and Kenics®. Calorimetric measurements are performed to characterise the energy dissipation rate inside the loop. The curing step is performed in a coiled tube reactor with two geometric configurations. Number based capsule size distributions are derived from micrograph analysis. Results indicate that the recycle pump is the main contributor to determine the capsule size distribution. A continuous setup is achieved for PU microcapsules containing hexyl acetate with a production rate of 198 g/h dry capsules, and a mean capsule diameter of 13.3 µm with a core content of 54 wt%.

Developing a system that can automatically detect health changes using transfer times of older adults.

BACKGROUND: As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. METHODS: This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. RESULTS: The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. CONCLUSIONS: The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.

Forecasting Time Series in Healthcare With Gaussian Processes and Dynamic Time Warping Based Subset Selection.

Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. Furthermore, the need for early prediction (as is often the case in healthcare applications) amplifies the problem of limited availability of data. This article proposes a novel personalised technique that can be learned in the absence of sufficient data for early prediction in time series. Our novelty lies in the development of a subset selection approach to select time series that share temporal similarities with the time series of interest, commonly known as the test time series. Then, a Gaussian processes-based model is learned using the existing test data and the chosen subset to produce personalised predictions for the test subject. We will conduct experiments with univariate and multivariate data from real-world healthcare applications to show that our strategy outperforms the state-of-the-art by around 20%.

Sampling errors and variability in video transects for assessment of reef fish assemblage structure and diversity.

Video monitoring is a rapidly evolving tool in aquatic ecological research because of its non-destructive ability to assess fish assemblages. Nevertheless, methodological considerations of video monitoring techniques are often overlooked, especially in more complex sampling designs, causing inefficient data collection, processing, and interpretation. In this study, we discuss how video transect sampling designs could be assessed and how the inter-observer variability, design errors and sampling variability should be quantified and accounted for. The study took place in the coastal areas of the Galapagos archipelago and consisted of a hierarchical repeated-observations sampling design with multiple observers. Although observer bias was negligible for the assessment of fish assemblage structure, diversity and counts of individual species, sampling variability caused by simple counting/detection errors, observer effects and instantaneous fish displacement was often important. Especially for the counts of individual species, sampling variability most often exceeded the variability of the transects and sites. An extensive part of the variability in the fish assemblage structure was explained by the different transects (13%), suggesting that a sufficiently high number of transects is required to account for the within-location variability. Longer transect lengths allowed a better representation of the fish assemblages as sampling variability decreased by 33% if transect length was increased from 10 to 50 meters. However, to increase precision, including more repeats was typically more efficient than using longer transect lengths. The results confirm the suitability of the technique to study reef fish assemblages, but also highlight the importance of a sound methodological assessment since different biological responses and sampling designs are associated with different levels of sampling variability, precision and ecological relevance. Therefore, besides the direct usefulness of the results, the procedures to establish them may be just as valuable for researchers aiming to optimize their own sampling technique and design.

Feature selection for unbiased imputation of missing values: A case study in healthcare.

Datasets in healthcare are plagued with incomplete information. Imputation is a common method to deal with missing data where the basic idea is to substitute some reasonable guess for each missing value and then continue with the analysis as if there were no missing data. However unbiased predictions based on imputed datasets can only be guaranteed when the missing mechanism is completely independent of the observed or missing data. Often, this promise is broken in healthcare dataset acquisition due to unintentional errors or response bias of the interviewees. We highlight this issue by studying extensively on an annual health survey dataset on infant mortality prediction and provide a systematic testing for such assumption. We identify such biased features using an empirical approach and show the impact of wrongful inclusion of these features on the predictive performance.Clinical relevance- We show that blind analysis along with plug and play imputation of healthcare data is a potential pitfall that clinicians and researchers want to avoid in finding important markers of disease.

Gestational weight gain prediction using privacy preserving federated learning.

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140.Clinical relevance- Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance.

From Mangrove to Fork: Metal Presence in the Guayas Estuary (Ecuador) and Commercial Mangrove Crabs.

Mangrove wetlands provide essential ecosystem services such as coastal protection and fisheries. Metal pollution due to industrial and agricultural activities represents an issue of growing concern for the Guayas River Basin and related mangroves in Ecuador. Fisheries and the related human consumption of mangrove crabs are in need of scientific support. In order to protect human health and aid river management, we analyzed several elements in the Guayas Estuary. Zn, Cu, Ni, Cr, As, Pb, Cd, and Hg accumulation were assessed in different compartments of the commercial red mangrove crab Ucides occidentalis (hepatopancreas, carapax, and white meat) and the environment (sediment, leaves, and water), sampled at fifteen sites over five stations. Consistent spatial distribution of metals in the Guayas estuary was found. Nickel levels in the sediment warn for ecological caution. The presence of As in the crabs generated potential concerns on the consumers' health, and a maximum intake of eight crabs per month for adults is advised. The research outcomes are of global importance for at least nine Sustainable Development Goals (SDGs). The results presented can support raising awareness about the ongoing contamination of food and their related ecosystems and the corresponding consequences for environmental and human health worldwide.

PREgDICT : Early prediction of gestational weight gain for pregnancy care.

Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.

Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

Accelerometry-based home monitoring for detection of nocturnal hypermotor seizures based on novelty detection.

Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure monitoring with the standard method of video/EEG-monitoring. We propose a method for hypermotor seizure detection based on accelerometers attached to the extremities. From the acceleration signals, multiple temporal, frequency, and wavelet-based features are extracted. After determining the features with the highest discriminative power, we classify movement events in epileptic and nonepileptic movements. This classification is only based on a nonparametric estimate of the probability density function of normal movements. Such approach allows us to build patient-specific models to classify movement data without the need for seizure data that are rarely available. If, in the test phase, the probability of a data point (event) is lower than a threshold, this event is considered to be an epileptic seizure; otherwise, it is considered as a normal nocturnal movement event. The mean performance over seven patients gives a sensitivity of 95.24% and a positive predictive value of 60.04%. However, there is a noticeable interpatient difference.

Detecting rare events using extreme value statistics applied to epileptic convulsions in children.

OBJECTIVE: Nocturnal home monitoring of epileptic children is often not feasible due to the cumbersome manner of seizure detection with the standard method of video electroencephalography monitoring. The goal of this paper is to propose a method for hypermotor seizure detection based on accelerometers that are attached to the extremities. METHODS: Supervised methods that are commonly used in literature need annotation of data and hence require expert (neurologist) interaction resulting in a substantial cost. In this paper an unsupervised method is proposed that uses extreme value statistics and seizure detection based on a model of normal behavior that is estimated using all recorded and unlabeled data. In this way the expensive interaction can be avoided. RESULTS: When applying this method to a labeled dataset, acquired from 7 patients, all hypermotor seizures are detected in 5 of the 7 patients with an average positive predictive value (PPV) of 53%. For evaluating the performance on an unlabeled dataset, seizure events are presented to the system as normal movement events. Since hypermotor seizures are rare compared to normal movements, the very few abnormal events have a negligible effect on the quality of the model. In this way, it was possible to evaluate the system for 3 of the 7 patients when 3% of the training set was composed of seizure events. This resulted in sensitivity scores of 80%, 22% and 90% and a PPV of 89%, 21% and 44% respectively. These scores are comparable with a state-of-the-art supervised machine learning based approach which requires a labeled dataset. CONCLUSIONS: A person-dependent epileptic seizure detection method has been designed that requires little human interaction. In contrast to traditional machine learning approaches, the imbalance of the dataset does not cause substantial difficulties.