TY - GEN
T1 - Cough audio sentiment analytics for software as a medical device applications
AU - Damani, Shivam
AU - Sethi, Arshia K.
AU - Baraskar, Bhavana
AU - Gopalakrishnan, Keerthy
AU - Agarwal, Joshika
AU - Albitar, Hasan A.
AU - Ahluwalia, Vaibhav
AU - Donlinger, Sue Ann P.
AU - Iyer, Vivek
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Chronic cough is not only one of the leading causes of seeking healthcare all over the world but also a huge emotional drain on the affected patient population. In this study, we used 24-hour cough recordings to analyze the intervening conversations for sentiment analyses to better diagnose, guide, and manage treatment in such patients. We surveyed a cough clinic and selected four subjects with active cough complaints using relevant ICD-10 codes. Subjects were given and instructed to wear a device to record cough for 24 hours and the recordings were collected at weeks 0, 4, 8, and 12 of the treatment. The collected data was preprocessed to eliminate sections with no data (sleep, silence) and the number of coughs was counted. Google search API calls were used to transcribe the audio files and NLTK s VADER analyzer was used to classify sentiments on a scale of 0 to 1. Finally, average scores were calculated and plotted over a graph to interpret any trends. 12 weeks of cough treatment had varied results on the four subjects. We categorized the exhibited sentiments into negative, neutral, positive, and compound and noted that they also showed no general trends. Among these, the compound sentiment displayed the most erratic patterns, and the obtained results could not generate a steady trend. Further studies are required with a large cohort to collect data over a longer duration to accurately analyze the sentiments associated with chronic cough.
AB - Chronic cough is not only one of the leading causes of seeking healthcare all over the world but also a huge emotional drain on the affected patient population. In this study, we used 24-hour cough recordings to analyze the intervening conversations for sentiment analyses to better diagnose, guide, and manage treatment in such patients. We surveyed a cough clinic and selected four subjects with active cough complaints using relevant ICD-10 codes. Subjects were given and instructed to wear a device to record cough for 24 hours and the recordings were collected at weeks 0, 4, 8, and 12 of the treatment. The collected data was preprocessed to eliminate sections with no data (sleep, silence) and the number of coughs was counted. Google search API calls were used to transcribe the audio files and NLTK s VADER analyzer was used to classify sentiments on a scale of 0 to 1. Finally, average scores were calculated and plotted over a graph to interpret any trends. 12 weeks of cough treatment had varied results on the four subjects. We categorized the exhibited sentiments into negative, neutral, positive, and compound and noted that they also showed no general trends. Among these, the compound sentiment displayed the most erratic patterns, and the obtained results could not generate a steady trend. Further studies are required with a large cohort to collect data over a longer duration to accurately analyze the sentiments associated with chronic cough.
KW - Chronic cough
KW - Natural Language Toolkit (NLTK)
KW - Sentiment analysis
KW - Valence Aware Dictionary and Sentiment Reasoner (VADER)
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U2 - 10.1115/DMD2023-1316
DO - 10.1115/DMD2023-1316
M3 - Conference contribution
AN - SCOPUS:85164982147
T3 - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
BT - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
PB - American Society of Mechanical Engineers
T2 - 2023 Design of Medical Devices Conference, DMD 2023
Y2 - 17 April 2023 through 21 April 2023
ER -