A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs

2017 ◽  
Vol 139 ◽  
pp. 1-16 ◽  
Author(s):  
Abdur Rahim Mohammad Forkan ◽  
Ibrahim Khalil
Keyword(s):  
Decision Making ◽  
Remote Monitoring ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Patient Specific ◽  
Monitoring Systems ◽  
Context Aware ◽  
Remote Monitoring Systems

scholarly journals Task Shifting: The Use of Laypersons for Acquisition of Vital Signs Data for Clinical Decision Making in the Emergency Room Following Traumatic Injury

2017 ◽  
Vol 41 (12) ◽  
pp. 3066-3073 ◽  
Author(s):  
Bryce E. Haac ◽  
Jared R. Gallaher ◽  
Charles Mabedi ◽  
Andrew J. Geyer ◽  
Anthony G. Charles
Keyword(s):  
Decision Making ◽  
Emergency Room ◽  
Clinical Decision Making ◽  
Traumatic Injury ◽  
Vital Signs ◽  
Clinical Decision ◽  
Task Shifting

scholarly journals Quantifying the Impact of Patient-Specific Factors and Disease Severity on Clinical Decision Making in Cuff Tear Arthropathy: A Case-Based Survey

2019 ◽  
Vol 15 (3) ◽  
pp. 276-285
Author(s):  
Adam P. Schumaier ◽  
Yehia H. Bedeir ◽  
Joshua S. Dines ◽  
Keith Kenter ◽  
Lawrence V. Gulotta ◽  
...  
Keyword(s):  
Decision Making ◽  
Disease Severity ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Patient Specific ◽  
Cuff Tear Arthropathy ◽  
Cuff Tear ◽  
Specific Factors ◽  
The Impact ◽  
Case Based

Gaps Between Alarm Capabilities and Decision-making Needs: An Observational Study of Detecting Patient Decompensation

2018 ◽  
Vol 7 (1) ◽  
pp. 112-116 ◽  
Author(s):  
Chelsea R. Horwood ◽  
Michael F. Rayo ◽  
Morgan Fitzgerald ◽  
E. Asher Balkin ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision ◽  
Unintended Consequences ◽  
Warning Signs ◽  
Medical Setting ◽  
Mental Picture ◽  
Wide Range

Decompensation is a change in the overall ability to maintain physiological function in the presence of a stressor or disease. In the medical setting, clinicians utilize a wide range of technological tools to aid in their clinical decision making and to identify early warning signals for decompensation. However, many of these technologies have underperformed and are not aligned with the actual role of practitioners, resulting in unintended consequences and adverse events. The primary aim of this study is to explore how different nurses interpret early warning signs in order to anticipate decompensation. The secondary aim is to assess which technologies nurses rely on when anticipating decompensation, and if those technologies are adequately aiding them in their clinical decision making. Two researchers performed semi-structured ethnographic interviews that were recorded and transcribed during the summer of 2017. In total, 43 nurses were interviewed from different medical and surgical floors within the same hospital. Participants were asked questions focused on how they use and respond to alarms and how they anticipate patient decompensation. Constant Comparative Analysis was used to reveal patterns of responses between participants. Based on the qualitative analysis 6 major themes emerged:  1. Anticipating patient decompensation requires creating a complete mental “picture of the patient” by the nurses  2. Nurse-to-nurse communication and expertise is essential to understanding the patient’s history  3. Warning signs for decompensation were largely determined by a patient’s baseline  4. Change over time, or trends, is informative for anticipating decompensation. Numbers (regarding vital signs and labs) alone are not  5. Consistent care of patients improved nurse’s confidence in decision making  6. Anticipating decompensation requires “staying ahead of the machines Our research suggests that there is a gap between the information practitioners need to accurately anticipate patient decompensation, and the information current alarm technologies provide. Alarms are the primary tool provided to nurses to aid them in detecting hazardous events, however, current alarms are not well-suited in supporting signals that anticipate patient decompensation before it happens.

scholarly journals Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rawan AlSaad ◽  
Qutaibah Malluhi ◽  
Ibrahim Janahi ◽  
Sabri Boughorbel
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Short Term Memory ◽  
Clinical Decision ◽  
Patient Specific ◽  
Great Promise ◽  
Short Term ◽  
Children With Asthma ◽  
Correct Pattern ◽  
Long Short Term Memory

Abstract Background Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients’ EHR historical visits. Methods We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes. Results Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model’s prediction to a group of visits. Conclusion We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.

scholarly journals A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paul G. M. Knoops ◽  
Athanasios Papaioannou ◽  
Alessandro Borghi ◽  
Richard W. F. Breakey ◽  
Alexander T. Wilson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Reconstructive Surgery ◽  
Clinical Decision Making ◽  
Specific Treatment ◽  
Operation Time ◽  
Clinical Decision ◽  
Patient Specific ◽  
Computer Assisted ◽  
Plastic And Reconstructive Surgery

Abstract Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.

scholarly journals Clinical decision making regarding the use of vital signs in physical therapy

2016 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Virginia G Thistle ◽  
Allison L Basskin ◽  
Eric Shamus ◽  
Renee Jeffreys-Heil
Keyword(s):  
Decision Making ◽  
Physical Therapy ◽  
Clinical Decision Making ◽  
Vital Signs ◽  
Clinical Decision
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