Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and F-1-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs

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

Towards a global Fishing Vessel Ocean Observing Network (FVON): state of the art and future directions

Ocean observations are the foundation of our understanding of ocean processes. Improving these observations has critical implications for our ability to sustainably derive food from the ocean, predict extreme weather events that take a toll on human life, and produce the goods and services that are needed to meet the needs of a vast and growing population. While there have been great leaps forward in sustained operational monitoring of our oceans there are still key data gaps which result in sub-optimal ocean management and policy decisions. The global fishing industry represents a vast opportunity to create a paradigm shift in how ocean data are collected: the spatio-temporal extent of ocean data gaps overlaps significantly with fishers’ activities; fishing vessels are suitable platforms of opportunity to host communications and sensor equipment; and many fishing vessels effectively conduct a depth-profile through the water column in the course of normal fishing activities, representing a powerful subsurface data collection opportunity. Fishing vessel-collected ocean data can complement existing ocean observing networks by enabling the cost-effective collection of vast amounts of subsurface ocean information in data-sparse regions. There is an emerging global network of fishing vessels participating in collaborative efforts to collect oceanographic data accelerated by innovations in enabling technologies. While there are clear opportunities that arise from partnering with fishing vessels, there are also challenges ranging from geographic and cultural differences in fleets, fishing methods and practices, data processing and management for heterogeneous data, as well as long term engagement of the fishers. To advance fishing vessel-based ocean observation on a global scale, the Fishing Vessel Ocean Observing Network (FVON) aims to maximize data value, establish best practices around data collection and management, and facilitate observation uptake. FVON’s ultimate goals are to foster collaborative fishing vessel-based observations, democratize ocean observation, improve ocean predictions and forecasts, promote sustainable fishing, and power a data-driven blue economy

What Does Ecological Farming Mean for Farm Labour?

Summary: Ecological farming, such as organic and low‐input farming, is gaining popularity in the public discourse. One question is how this type of farming may impact farm labour from a socio‐economic point of view. The article first discusses how low‐input farming practices (i.e. with lower reliance on inputs derived from fossil fuels) may affect the economic returns to labour, measured as the farm’s revenue per hour of labour input, on data from the Farm Accountancy Data Network (FADN) in 2004‐‐2015 for four European countries. Returns to labour appear to be highest at the two extremes – very low‐input farms and highly intensive farms. Farms in the low‐input end of the spectrum are in the minority, while the overwhelming majority of farms are intensive and have internal economic incentives to intensify further. The article also analyses how working conditions differ between organic and conventional dairy farms in two European countries based on interviews with farmers in 2019. Results show that all dimensions of working conditions are affected by being an organic farm or not, but this is not the only factor. There are many influences on working conditions, such as the production context and workforce composition

What Does Ecological Farming Mean for Farm Labour?

Summary: Ecological farming, such as organic and low‐input farming, is gaining popularity in the public discourse. One question is how this type of farming may impact farm labour from a socio‐economic point of view. The article first discusses how low‐input farming practices (i.e. with lower reliance on inputs derived from fossil fuels) may affect the economic returns to labour, measured as the farm’s revenue per hour of labour input, on data from the Farm Accountancy Data Network (FADN) in 2004‐‐2015 for four European countries. Returns to labour appear to be highest at the two extremes – very low‐input farms and highly intensive farms. Farms in the low‐input end of the spectrum are in the minority, while the overwhelming majority of farms are intensive and have internal economic incentives to intensify further. The article also analyses how working conditions differ between organic and conventional dairy farms in two European countries based on interviews with farmers in 2019. Results show that all dimensions of working conditions are affected by being an organic farm or not, but this is not the only factor. There are many influences on working conditions, such as the production context and workforce composition

Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements

status: publishe

A Machine Learning Approach to Investigate the Predictive Value of Pulse Pressure in ICU Mortality-Risk

status: publishe

Usability of a digital health platform to support home hospitalisation in heart failure patients:a multicentre feasibility study among healthcare professionals

AIMS: Heart failure (HF) is a common cause of mortality and (re)hospitalisations. The NWE-Chance project explored the feasibility of providing hospitalisations at home (HH) supported by a newly developed digital health platform. The aim of this study was to explore the perceived usability by healthcare professionals (HCPs) of a digital platform in addition to HH for HF patients. METHODS AND RESULTS: A prospective, international, multicentre, single-arm interventional study was conducted. Sixty-three patients and 22 HCPs participated. HH consisted of daily home visits by the nurse and use of the platform, consisting of a portable blood pressure device, weight scale, pulse oximeter, a wearable chest patch to measure vital signs (heart rate, respiratory rate, activity level and posture), and an eCoach for the patient. Primary outcome was usability of the platform measured by the System Usability Scale (SUS) halfway and at the end of the study. Overall usability was rated as sufficient (Mean score 72.1±8.9) and did not differ between the measurements moments (p=.690). HCPs reported positive experiences (n=7), negative experiences (n=13) and recommendations (n=6) for the future. Actual use of the platform was 79% of the HH days. CONCLUSION: A digital health platform to support HH was considered usable by HCPs, although actual use of the platform was limited. Therefore, several improvements in the integration of the digital platform into clinical workflows and in defining the precise role of the digital platform and its use are needed to add value before full implementation. REGISTRATION: ClinicalTrials.gov NCT04084964

Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject’s functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program