An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Rehm, Gregory B.; Kühn, Bärbel; Lieng, Monica K.; Cortés‐Puch, Irene; Nguyen, James; Guo, Edward C.; Delplanque, Jean‐Pierre; Anderson, Nicholas; Adams, Jason Y.

doi:10.1101/568386

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…AI for health care has already afforded new perspectives [ 4 ] on automated assessments leading to novel and timely interventions [ 5 ]. Machine learning (ML) models are used in medical imaging [ 6 , 7 ], neurology [ 8 ], cardiology [ 9 , 10 ], pulmonology [ 11 ], nephrology [ 12 , 13 ], gastroenterology [ 14 ], pathology [ 15 , 16 ], health care informatics [ 17 , 18 ], and clinical decision support [ 5 , 19 ]. ML models capable of automated in-home assessments and alerts are also in the early stages of supporting individualized home-based interventions [ 20 - 24 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

Fritz¹,

Wilson²,

Dermody³

et al. 2020

J Med Internet Res

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Background Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients’ natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. Objective This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. Methods A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. Results We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. Conclusions Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians’ real-world knowledge when developing pain-assessing machine learning models improves the model’s performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

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Section: Introductionmentioning

confidence: 99%

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

Fritz¹,

Wilson²,

Dermody³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…AI for health care has already afforded new perspectives [4] on automated assessments leading to novel and timely interventions [5]. Machine learning (ML) models are used in medical imaging [6,7], neurology [8], cardiology [9,10], pulmonology [11], nephrology [12,13], gastroenterology [14], pathology [15,16], health care informatics [17,18], and clinical decision support [5,19]. ML models capable of automated in-home assessments and alerts are also in the early stages of supporting individualized home-based interventions [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis (Preprint)

Fritz¹,

Wilson²,

Dermody³

et al. 2020

Preprint

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BACKGROUND Poorly managed pain can lead to addiction, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings, far removed from patients’ natural environment. Advances in smart home technology can, potentially, allow observation of pain in the home setting. Smart homes can be trained to recognize human behaviors and may be useful for quantifying functional pain interference thereby creating new ways of assessing pain and supporting people with pain who are living at home. OBJECTIVE To determine if a health-assistive smart home can detect pain-related behaviors to perform automated assessment and support intervention. METHODS A preliminary retrospective study was nested in a larger longitudinal smart home study. Archived ambient sensor and weekly nursing health assessment data from 11 independent older adults reporting pain across 1-2 years were co-analyzed. Two nurses using qualitative methods interpreted 27 unique pain events (acute, flare, chronic) represented in sensor-based data and illuminated 13 clinically-relevant associated behaviors. Data were used to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS Qualitative analysis revealed six pain-related behavioral themes. Quantitative results were classified using a clinician-guided random forest technique which yielded a classification accuracy of 0.70, a sensitivity of 0.72, a specificity of 0.69, an area under the ROC curve of 0.756, and an area under the PRC curve of 0.777. The regression formulation achieved moderate correlation with r=0.42. Without clinician guidance, using standard anomaly detection techniques, an accuracy of only 0.16 was achieved. CONCLUSIONS Findings reveal that a pain-assessing smart home may viably support pain management. Utilizing clinicians’ real-world knowledge when developing machine learning models for healthcare delivery could improve the model’s performance. A larger study is warranted to improve model performance. Automated assessment may improve health outcomes for persons with chronic pain by facilitating timely contact with the healthcare team.

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An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Cited by 2 publications

References 40 publications

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis (Preprint)

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