scholarly journals Welfare Health and Productivity in Commercial Pig Herds

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1176
Author(s):  
Przemysław Racewicz ◽  
Agnieszka Ludwiczak ◽  
Ewa Skrzypczak ◽  
Joanna Składanowska-Baryza ◽  
Hanna Biesiada ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Vital Signs ◽  
Visual Assessment ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Herd Management ◽  
Pork Production ◽  
Antimicrobial Substances ◽  
Non Invasive ◽  
Frequency Identification

In recent years, there have been very dynamic changes in both pork production and pig breeding technology around the world. The general trend of increasing the efficiency of pig production, with reduced employment, requires optimisation and a comprehensive approach to herd management. One of the most important elements on the way to achieving this goal is to maintain animal welfare and health. The health of the pigs on the farm is also a key aspect in production economics. The need to maintain a high health status of pig herds by eliminating the frequency of different disease units and reducing the need for antimicrobial substances is part of a broadly understood high potential herd management strategy. Thanks to the use of sensors (cameras, microphones, accelerometers, or radio-frequency identification transponders), the images, sounds, movements, and vital signs of animals are combined through algorithms and analysed for non-invasive monitoring of animals, which allows for early detection of diseases, improves their welfare, and increases the productivity of breeding. Automated, innovative early warning systems based on continuous monitoring of specific physiological (e.g., body temperature) and behavioural parameters can provide an alternative to direct diagnosis and visual assessment by the veterinarian or the herd keeper.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

Application of Radio Frequency Identification (RFID) Technology in Dairy Herd Management

2014 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Akhilesh Singh ◽  
Sudipta Ghosh ◽  
Biswajit Roy ◽  
Deepak Tiwari ◽  
RPS Baghel
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Dairy Herd ◽  
Rfid Technology ◽  
Herd Management ◽  
Frequency Identification

An assessment of the effects of widespread use of mobile applications in the health sector: An exploratory study of its success and failures

2016 ◽  
pp. 145-149
Author(s):  
Nadire Cavus
Keyword(s):  
Health Care ◽  
Mobile Applications ◽  
Health Sector ◽  
Vital Signs ◽  
Early Warning Systems ◽  
Mobile Technologies ◽  
Warning Systems ◽  
Medical Practitioners ◽  
Mobile Health Applications ◽  
Health Applications

Abstract Prior to the introduction of mobile technologies, the manual system of checking patients’ vital signs after approximately seven hours increased the health risk of the patients. Some of the patients’ health was jeopardised, worsening their situation, others re-admitted and others even passing on. The introduction and extensive use of mobile technologies has transformed the delivery of health care. Mobile applications with early warning systems are now dominating the health sector in an attempt to alert medical practitioners to act promptly to the patients’ needs. This paper reviews effects of mobile applications in the health sector as well as the success and failures of Mobile health applications. The assimilation of mobile applications in health care is marking an incredible venture in the health care industry. Keywords: mHealth, mobile applications, success, failures, health sector, mobile technologies, adoption, patients, hospitals.

scholarly journals Developing a Sociocultural Framework of Compliance: An Exploration of Factors Related to the Use of Early Warning Systems Among Acute Care Clinicians

2020 ◽  
Author(s):  
Tracy Flenady ◽  
Trudy Dwyer ◽  
Agnieszka Sobolewska ◽  
Danielle Le Lagadec ◽  
Justine Connor ◽  
...  
Keyword(s):  
Acute Care ◽  
Early Warning ◽  
Vital Signs ◽  
Warning System ◽  
Early Warning Systems ◽  
Public Hospitals ◽  
Sociocultural Factors ◽  
Warning Systems ◽  
Structured Interviews ◽  
Good Communication

Abstract Background: Early warning systems (EWS) are most effective when clinicians monitor patients’ vital signs and comply with the recommended escalation of care protocols once deterioration is recognised. Objectives: To explore sociocultural factors influencing acute care clinicians’ compliance with an early warning system commonly used in Queensland public hospitals in Australia. Methods: This interpretative qualitative study utilised inductive thematic analysis to analyse data collected from semi-structured interviews conducted with 30 acute care clinicians from Queensland, Australia.Results: This study identified that individuals and teams approached compliance with EWS in the context of 1) the use of EWS for patient monitoring; and 2) the use of EWS for the escalation of patient care. Individual and team compliance with monitoring and escalation processes is facilitated by intra and inter-professional factors such as acceptance and support, clear instruction, inter-disciplinary collaboration and good communication. Noncompliance with EWS can be attributed to intra and inter-professional hierarchy and poor communication. Conclusions: The overarching organisational context including the hospital’s embedded quality improvement and administrative protocols (training, resources and staffing) impact hospital-wide culture and influence clinicians’ and teams’ compliance or non-compliance with early warning system’s monitoring and escalation processes. Successful adoption of EWS relies on effective and meaningful interactions among multidisciplinary staff.

Stink or swim: techniques to meet the challenges for the study and conservation of small critters that hide, swim, or climb, and may otherwise make themselves unpleasant

2018 ◽  
Author(s):  
Roger A. Powell ◽  
Stephen Ellwood ◽  
Roland Kays ◽  
Tiit Maran
Keyword(s):  
Radio Frequency Identification ◽  
Environmental Dna ◽  
Knowledge Gain ◽  
Non Invasive ◽  
Magnetic Inductance ◽  
Creative Research ◽  
Frequency Identification ◽  
Identification Tags ◽  
Noxious Chemicals ◽  
Invasive Techniques

The study of musteloids requires different perspectives and techniques than those needed for most mammals. Musteloids are generally small yet travel long distances and many live or forage underground or under water, limiting the use of telemetry and direct observation. Some are arboreal and nocturnal, facilitating telemetry but limiting observation, trapping, and many non-invasive techniques. Large sexual size dimorphism arguably doubles sample sizes for many research questions. Many musteloids defend themselves by expelling noxious chemicals. This obscure group does not attract funding, even when endangered, further reducing rate of knowledge gain. Nonetheless, passive and active radio frequency identification tags, magnetic-inductance tracking, accelerometers, mini-biologgers and some GPS tags are tiny enough for use with small musteloids. Environmental DNA can document presence of animals rarely seen. These technologies, coupled with creative research design that is well-grounded on the scientific method, form a multi-dimensional approach for advancing our understanding of these charismatic minifauna.

scholarly journals Machine Learning-based Early Warning Systems for Clinical Deterioration: A Systematic Scoping Review (Preprint)

2020 ◽  
Author(s):  
Sankavi Muralitharan ◽  
Walter Nelson ◽  
Shuang Di ◽  
Michael McGillion ◽  
PJ Devereaux ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ambulatory Care ◽  
Early Warning ◽  
Vital Signs ◽  
Early Warning Systems ◽  
Clinical Deterioration ◽  
Cochrane Library ◽  
Warning Systems ◽  
Learning Models ◽  
Machine Learning Models

BACKGROUND Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively and preventing adverse outcomes. Vital signs-based aggregate-weighted Early Warning Systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE To identify, summarize, and evaluate the available research, current state of utility and challenges with machine learning based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs”, “clinical deterioration”, and “machine learning”. Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS 24 peer-reviewed studies were identified for inclusion from 417 articles. 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, ICUs, emergency departments, step-down units, medical assessment units, post-anesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS In studies that compared performance, reported results suggest that machine learning based early warning systems can achieve greater accuracy than aggregate weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings. CLINICALTRIAL

Dinitrogen oxide detection for nitrification failure early warning systems

2005 ◽  
Vol 52 (8) ◽  
pp. 249-256 ◽  
Author(s):  
M.D. Butler ◽  
T. Stephenson ◽  
L. Stokes ◽  
R.M. Stuetz
Keyword(s):  
Early Warning ◽  
Strong Correlation ◽  
Pilot Plant ◽  
Time Lag ◽  
Early Warning Systems ◽  
Pilot Scale ◽  
Aeration Tank ◽  
Warning Systems ◽  
Non Invasive ◽  
Nitrification Process

Experiments were conducted in order to establish whether N2O could be used to predict nitrification failure (through non-invasive means). Previous research had shown a strong correlation between N2O gas and NH3 in the effluent, giving rise to the possibility N2O can be used as an indicator for failure in the nitrification process. Two pilot-scale activated sludge plants were used, each with two lanes. The smaller consisted of a 60l aeration tank and a 20l clarifier; the larger pilot plant had an aeration tank of 315l and a clarifier of 100l. The small pilot plant experiments showed that N2O gas was given off almost immediately from O2 deprivation/NH3 shock loads, but did not follow the expected trend of the time lag of NH3 in the effluent. This led to further investigation in the hydrodynamics and mixing characteristics of aeration basins, where a second larger pilot plant was used. Further experiments were conducted of high NH3 loadings and O2 deprivation, showed that work of was reproducible. However, it was also shown that with partial nitrification failure, a different N2O response of a continual rise was observed.

Core body temperature monitoring with passive transponder boluses in beef heifers

2008 ◽  
Vol 88 (2) ◽  
pp. 225-235 ◽  
Author(s):  
J. A. Small ◽  
A. D. Kennedy ◽  
S. H. Kahane
Keyword(s):  
Body Temperature ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Core Body Temperature ◽  
The Body ◽  
Full Duplex ◽  
Inductively Coupled ◽  
Non Invasive ◽  
Frequency Identification ◽  
Core Body

Experiments were conducted over 82 d (Nov. 04 to Jan. 24, 49°N, mean ambient temperature -3.6 to -12.6 ± 1.6°C) to determine the effects of fever, photoperiod and pen setup on the rate (MR) and frequency (MF) with which heifers were monitored and the body (rumen) temperatures (BTr) obtained with a cattle temperature moniotoring system (MaGiiX). Magnetic, inductively coupled full duplex radio-frequency identification (RFID) transponder boluses containing thermistors were administered per os to 72 heifers (7.9 ± 0.5 mo of age and 283 ± 23 kg body weight) housed in one of four pens, in outdoor shed-lot facilities, each with a panel reader co-located with the waterbowl. A mixed ration (59% dry matter) was provided at 1500 daily. Fencing was arranged within pens for water motivated (WM) acquisitions during exps. 1 (Initial), 2 (Fever) and 3 (Photoperiod), or for either WM or activity motivated (AM) acquisitions during exp. 4 (Pen setup). Overall, most heifers were monitored daily (Mode MR 100%), several times perday (MF 7.8 ± 0.5), mostly during the afternoon and evening rather than night and morning 6-h periods, and BTr (37.8 ± 0.2°C, range 22 to 42°C) were usually lower (P < 0.05) for the afternoon than night. A 2°C increase in mean BTr caused by fever was detected (P < 0.05) when monitoring was scheduled rather than unscheduled. Extended (16h) in contrast to natural (8h) photoperiod increased (P < 0.05) evening MR (96.8 vs.83.5 ± 1.9%) and MF (3.8 vs. 2.5 ± 0.2), and morning-BTr (38.0 vs. 37.4 ± 0.11°C). Pen setup for AM in contrast to WM acquisitions increased (P < 0.05) MR and MF and BTr (by 1°C) in all periods of the day. The technology has excellent potential for non-invasive monitoring of BTr in heifers. Key words: Radio-frequency identification, cattle, transponder bolus, body temperature, rumen

scholarly journals Developing a sociocultural framework of compliance: an exploration of factors related to the use of early warning systems among acute care clinicians

2020 ◽  
Author(s):  
Tracy Flenady ◽  
Trudy Dwyer ◽  
Agnieszka Sobolewska ◽  
Danielle Le Lagadec ◽  
Justine Connor ◽  
...  
Keyword(s):  
Acute Care ◽  
Early Warning ◽  
Vital Signs ◽  
Warning System ◽  
Early Warning Systems ◽  
Public Hospitals ◽  
Sociocultural Factors ◽  
Warning Systems ◽  
Structured Interviews ◽  
Good Communication

Abstract Background: Early warning systems (EWS) are most effective when clinicians monitor patients’ vital signs and comply with the recommended escalation of care protocols once deterioration is recognised.Objectives: To explore sociocultural factors influencing acute care clinicians’ compliance with an early warning system commonly used in Queensland public hospitals in Australia.Methods: This interpretative qualitative study utilised inductive thematic analysis to analyse data collected from semi-structured interviews conducted with 30 acute care clinicians from Queensland, Australia.Results: This study identified that individuals and teams approached compliance with EWS in the context of 1) the use of EWS for patient monitoring; and 2) the use of EWS for the escalation of patient care. Individual and team compliance with monitoring and escalation processes is facilitated by intra and inter-professional factors such as acceptance and support, clear instruction, inter-disciplinary collaboration and good communication. Noncompliance with EWS can be attributed to intra and inter-professional hierarchy and poor communication.Conclusions: The overarching organisational context including the hospital’s embedded quality improvement and administrative protocols (training, resources and staffing) impact hospital-wide culture and influence clinicians’ and teams’ compliance or non-compliance with early warning system’s monitoring and escalation processes. Successful adoption of EWS relies on effective and meaningful interactions among multidisciplinary staff.

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