A Novel Approach to Addressing an Unintended Consequence of Direct to Room: The Delay of Initial Vital Signs

Abstract Background Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input. Methods In our study, we analyzed patients’ conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets “near” to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers. Results We demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models. Conclusion Applying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.

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“Community vital signs” : incorporating geocoded social determinants into electronic records to promote patient and population health

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv088 ◽

2015 ◽

Vol 23 (2) ◽

pp. 407-412 ◽

Cited By ~ 52

Author(s):

Andrew W Bazemore ◽

Erika K Cottrell ◽

Rachel Gold ◽

Lauren S Hughes ◽

Robert L Phillips ◽

...

Keyword(s):

Social Determinants ◽

Vital Signs ◽

Community Services ◽

Electronic Records ◽

Health Improvement ◽

Geospatial Technologies ◽

Novel Approach ◽

The Social ◽

Patient Health ◽

Target Disease

Abstract Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, “community vital signs” (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.

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VITAL SIGNS MONITORING BASED ON WEBCAM FOR HOME TELEMEDICINE APPLICATIONS

10.12783/dtssehs/aeim2021/35982 ◽

2021 ◽

Author(s):

GUOMING CHEN GUOMING CHEN ◽

QI ZHANG ◽

XUANKE TONG ◽

GUOFU LIAO

Keyword(s):

Heart Rate ◽

Real Time ◽

Video Processing ◽

Wearable Sensors ◽

Vital Signs ◽

Low Frequency ◽

Experimental System ◽

The Real ◽

Vital Signs Monitoring ◽

Novel Approach

Abstract. In this paper, we proposed a novel approach to monitor the vital signs based webcam for home telemedicine applications. This approach can continuously monitor the vital signs without wearable sensors. It uses the real time video processing algorithm to obtain the instantaneous heart rate(HR) and respiration rate(RR). Furthermore, the heart rate variability (HRV) was analyzed by power spectral density (PSD) estimation using the Lomb periodogram. Experiments in different scenarios were performed to verify the efficacy of the proposed noncontact monitoring vital signs based on webcam. The real time experimental system can be used to measure the instantaneous HR and RR, at the same time the low frequency and high frequency components were extracted. All experimental results show that the proposed concept can be applied to the home telemedicine in the future.

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Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction

Journal of Healthcare Engineering ◽

10.1155/2019/5930379 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Eli Bloch ◽

Tammy Rotem ◽

Jonathan Cohen ◽

Pierre Singer ◽

Yehudit Aperstein

Keyword(s):

Machine Learning ◽

Predictive Accuracy ◽

Learning Algorithms ◽

Vital Signs ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Support Vector ◽

Icu Stay ◽

Novel Approach ◽

High Level

Objective. Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs. Methodology. We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient’s likelihood to develop sepsis during ICU stay, hours before it is diagnosed. Results. Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient’s probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%. Conclusions. The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient’s vital signs proves to be a good indicator of one’s chance to become septic during ICU stay.

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Copper-dependent ATP7B up-regulation drives the resistance of TMEM16A-overexpressing head-and-neck cancer models to platinum toxicity

Biochemical Journal ◽

10.1042/bcj20190591 ◽

2019 ◽

Vol 476 (24) ◽

pp. 3705-3719 ◽

Cited By ~ 2

Author(s):

Avani Vyas ◽

Umamaheswar Duvvuri ◽

Kirill Kiselyov

Keyword(s):

Oxidative Stress ◽

Head And Neck ◽

Transcriptional Activation ◽

Platinum Resistance ◽

Mrna Levels ◽

Strong Positive Correlation ◽

Platinum Compounds ◽

Copper Chelation ◽

Novel Approach ◽

Cancer Models

Platinum-containing drugs such as cisplatin and carboplatin are routinely used for the treatment of many solid tumors including squamous cell carcinoma of the head and neck (SCCHN). However, SCCHN resistance to platinum compounds is well documented. The resistance to platinum has been linked to the activity of divalent transporter ATP7B, which pumps platinum from the cytoplasm into lysosomes, decreasing its concentration in the cytoplasm. Several cancer models show increased expression of ATP7B; however, the reason for such an increase is not known. Here we show a strong positive correlation between mRNA levels of TMEM16A and ATP7B in human SCCHN tumors. TMEM16A overexpression and depletion in SCCHN cell lines caused parallel changes in the ATP7B mRNA levels. The ATP7B increase in TMEM16A-overexpressing cells was reversed by suppression of NADPH oxidase 2 (NOX2), by the antioxidant N-Acetyl-Cysteine (NAC) and by copper chelation using cuprizone and bathocuproine sulphonate (BCS). Pretreatment with either chelator significantly increased cisplatin's sensitivity, particularly in the context of TMEM16A overexpression. We propose that increased oxidative stress in TMEM16A-overexpressing cells liberates the chelated copper in the cytoplasm, leading to the transcriptional activation of ATP7B expression. This, in turn, decreases the efficacy of platinum compounds by promoting their vesicular sequestration. We think that such a new explanation of the mechanism of SCCHN tumors’ platinum resistance identifies novel approach to treating these tumors.

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Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

Language Speech and Hearing Services in Schools ◽

10.1044/2020_lshss-19-00050 ◽

2020 ◽

Vol 51 (3) ◽

pp. 544-560 ◽

Cited By ~ 3

Author(s):

Kimberly A. Murphy ◽

Emily A. Diehm

Keyword(s):

Written Language ◽

Morphological Knowledge ◽

English Orthography ◽

Word Level ◽

Novel Approach ◽

Language Pathology ◽

The One ◽

Critical Intervention ◽

Reading And Spelling ◽

Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

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