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Feature importance analysis for compensatory reserve to predict hemorrhagic shock
July 15, 2022
Conference Paper
Published in:
44th Annual Int. Conf. of IEEE Engineering in Medicine & Biology Society (EMBC), DOI: 10.1109/EMBC48229.2022.9871661.
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Summary
Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.
Summary
Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate...
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Toward improving EN adoption: Bridging the gap between stated intention and actual use
June 3, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-7
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact Tracing (PACT) protocol and related projects to assist the public health objective of slowing the spread of SARS-CoV-2 through digital contact tracing. The joint Google and Apple deployed protocol (Google-Apple Exposure Notifications, also known as GAEN or EN), which became the de facto standard in the U.S., employs the same features as detailed by PACT. The protocol leverages smartphone Bluetooth communications to alert users of potential contact with those carrying the COVID-19 virus in a way that preserves the privacy of both the known-infected individual, and the users receiving the alert. Contact tracing and subsequent personal precautions are more effective at reducing disease spread when more of the population participates, but there are known difficulties with the adoption of novel technology. In order to help the U.S. Centers for Disease Control and Prevention (CDC) and U.S. state-level public health teams address these difficulties, a team of staff from MIT's Lincoln Laboratory (MIT LL) and Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) focused on studying user perception and information needs.
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact...
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Modeling probability of alert of Bluetooth low energy-based automatic exposure notifications
April 27, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-4
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy (BLE) signal attenuation domains. However, many public health definitions of "exposure" to a disease are based upon the distance between an index case and another person. To bridge the conceptual gap for public health authorities (PHAs) from the familiar distance-and-duration space to the signal attenuation-and-duration space, BLEMUR uses BLE signal attenuation as a proxy for distance between people, albeit an imprecise one. This paper will discuss the EN settings that can be manipulated, the BLE data collected, how data support a model of the relationship between measured attenuation and distance between phones, and how BLEMUR calculates the probability of alert for a distance and duration based on the settings and data.
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy...
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The Simulation of Automated Exposure Notification (SimAEN) Model
April 15, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-5
Summary
Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take the appropriate steps recommended by their health care authority. This paper describes an agent-based model that estimates the effects of AEN deployment on COVID-19 caseloads and public health workloads in the context of other critical public health measures available during the COVID-19 pandemic. We selected simulation variables pertinent to AEN deployment options, varied them in accord with the system dynamics available in 2020-2021, and calculated the outcomes of key metrics across repeated runs of the stochastic multi-week simulation. SimAEN's parameters were set to ranges of observed values in consultation with public health professionals and the rapidly accumulating literature on COVID-19 transmission; the model was validated against available population-level disease metrics. Estimates from SimAEN can help public health officials determine what AEN deployment decisions (e.g., configuration, workflow integration, and targeted adoption levels) can be most effective in their jurisdiction, in combination with other COVID-19 interventions (e.g., mask use, vaccination, quarantine and isolation periods).
Summary
Automated Exposure Notication (AEN) was implemented in 2020 to supplement traditional contact tracing for COVID-19 by estimating "too close for too long" proximities of people using the service. AEN uses Bluetooth messages to privately label and recall proximity events, so that persons who were likely exposed to SARS-CoV-2 can take...
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Wearable technology in extreme environments
March 14, 2022
Book Chapter
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Chapter 2 in: Cibis, T., McGregor AM, C. (eds) Engineering and Medicine in Extreme Environments. Springer, Cham. https://doi-org.ezproxyberklee.flo.org/10.1007/978-3-030-96921-9_2
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Summary
Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or cold temperatures. Wearable technology is ubiquitous in the consumer market but is still needed for extreme environments. For these applications, it is particularly challenging to meet requirements to be actionable, accurate, acceptable, integratable, and affordable. To provide insight into these needs and possible solutions and the technology trade-offs involved, several examples are provided. A physiological monitoring example is described for predicting and avoiding heat injury. A cognitive monitoring example is described for estimating cognitive workload, with broader applicability to a variety of conditions, such as cognitive fatigue and depression. Finally, eye tracking is considered as a promising wearable sensing modality with applications for both physiological and cognitive monitoring. Concluding thoughts are offered on the compelling need for wearable technology in the face of pandemics, wildfires, and climate change, but also for global projects that can uplift mankind, such as long-duration spaceflight and missions to Mars.
Summary
Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or...
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Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
March 2, 2022
Journal Article
Published in:
Sci. Rep., Vol. 12, No. 1, 2 March 2022.
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Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.
Summary
Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily...
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Artificial intelligence for detecting COVID-19 with the aid of human cough, breathing and speech signals: scoping review
February 14, 2022
Journal Article
Published in:
IEEE Open J. Eng. Med. Biol., 14 February 2022, pp. 235-41.
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Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data forpreliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in theliterature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15st August 2021. Terms were selected based on thetarget intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of thedisease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Halfof the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 for cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. However, the proposed methods with some time and appropriate clinical testing would prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in human body.
Summary
Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data forpreliminary screening may help alleviate these issues. Objective: This scoping review...
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Gait instability and estimated core temperature predict exertional heat stroke
January 12, 2022
Journal Article
Published in:
Br J Sports Med., online ahead of print, 12 January 2022.
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Summary
Objective Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from heart rate and gait instability from a trunk-worn sensor system can forward predict EHS onset. Methods Heart rate and three-axis accelerometry data were collected from chest-worn sensors from 1806 US military personnel participating in timed 4/5-mile runs, and loaded marches of 7 and 12 miles; in total, 3422 high EHS-risk training datasets were available for analysis. Six soldiers were diagnosed with heat stroke and all had rectal temperatures of >41°C when first measured and were exhibiting CNS dysfunction. Estimated core temperature (ECTemp) was computed from sequential measures of heart rate. Gait instability was computed from three-axis accelerometry using features of pattern dispersion and autocorrelation. Results The six soldiers who experienced heat stroke were among the hottest compared with the other soldiers in the respective training events with ECTemps ranging from 39.2°C to 40.8°C. Combining ECTemp and gait instability measures successfully identified all six EHS casualties at least 3.5 min in advance of collapse while falsely identifying 6.1% (209 total false positives) examples where exertional heat illness symptoms were neither observed nor reported. No false-negative cases were noted. Conclusion The combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.
Summary
Objective Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from...
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Using oculomotor features to predict changes in optic nerve sheath diameter and ImPACT scores from contact-sport athletes
March 4, 2021
Journal Article
Published in:
Front. Neurol., 4 March 2021, Art. No. 584684.
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Summary
There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports.
Summary
There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential...
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Ultrasound diagnosis of COVID-19: robustness and explainability
November 30, 2020
Journal Article
Published in:
arXiv:2012.01145v1 [eess.IV]
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Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the high stakes application we propose the use of robust and explainable techniques. We demonstrate experimentally that robust models have more stable predictions and offer improved interpretability. A framework of contrastive explanations based on adversarial perturbations is used to explain model predictions that aligns with human visual perception.
Summary
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an...
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