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Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning

Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning

event classification, this study investigates the ability of a scream-detection model to make useful predictions for audio outside of the training data, which to our knowledge has not yet been addressed in previous work.

Rebecca O'Donovan, Emre Sezgin, Sven Bambach, Eric Butter, Simon Lin

JMIR Form Res 2020;4(6):e18279


Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

Improved methods for ADE detection and analysis may identify novel drug safety signals and lead to improved methods for avoiding ADEs, with their attendant burden of morbidity, mortality, and cost.

Balaji Polepalli Ramesh, Steven M Belknap, Zuofeng Li, Nadya Frid, Dennis P West, Hong Yu

JMIR Med Inform 2014;2(1):e10


Developing a Disease Outbreak Event Corpus

Developing a Disease Outbreak Event Corpus

This paper reports on such a data set─an annotation scheme and corpus [9]─developed for disease outbreak event detection in the context of the BioCaster biosurvellance online news information extraction (IE) system [10,5].We believe that a focus on event extraction

Mike Conway, Ai Kawazoe, Hutchatai Chanlekha, Nigel Collier

J Med Internet Res 2010;12(3):e43


Trigger Tool–Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review

Trigger Tool–Based Automated Adverse Event Detection in Electronic Health Records: Systematic Review

Information concerning the data source, the triggers, and the reviewer(s) are detailed in Table 2.Concerning the methodology, 5 studies came from the “Automated Adverse Event Detection Collaborative,” which is a consortium to facilitate the use of automated

Sarah N Musy, Dietmar Ausserhofer, René Schwendimann, Hans Ulrich Rothen, Marie-Madlen Jeitziner, Anne WS Rutjes, Michael Simon

J Med Internet Res 2018;20(5):e198


Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

The list generated is by no means exhaustive but does represent some of the most common challenges (and solutions) we have encountered in our experience of designing, implementing, and evaluating numerous real-time event detection systems [14-25].

Eric Steven Kirkendall, Yizhao Ni, Todd Lingren, Matthew Leonard, Eric S Hall, Kristin Melton

J Med Internet Res 2019;21(5):e13047


A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

A New Approach for Detecting Sleep Apnea Using a Contactless Bed Sensor: Comparison Study

The data analysis consisted of 2 stages: (1) vital sign detection and (2) apneic event detection. Ensuing, we describe each stage separately.Vital Sign DetectionThe force applied to the sensor mat is the summation of the 3 sources.

Ibrahim Sadek, Terry Tan Soon Heng, Edwin Seet, Bessam Abdulrazak

J Med Internet Res 2020;22(9):e18297


Co-Design of a Consultation Audio-Recording Mobile App for People With Cancer: The SecondEars App

Co-Design of a Consultation Audio-Recording Mobile App for People With Cancer: The SecondEars App

Clinicians and health care providers must now work with patients to implement official systems of consultation audio-recording to facilitate an environment where audio-recording is openly encouraged.

Ruby Lipson-Smith, Fiona White, Alan White, Lesley Serong, Guy Cooper, Georgia Price-Bell, Amelia Hyatt

JMIR Form Res 2019;3(1):e11111


Expedited Safety Reporting Through an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study

Expedited Safety Reporting Through an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study

AE management consists of detection, processing, and reporting. AEs in clinical trials are usually detected during scheduled visits or when participants inform investigators of unscheduled visits to the emergency department or clinic.

Yu Rang Park, HaYeong Koo, Young-Kwang Yoon, Sumi Park, Young-Suk Lim, Seunghee Baek, Hae Reong Kim, Tae Won Kim

JMIR Med Inform 2020;8(2):e14379


Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

However, it is now also recognized that traditional SRS-based ADE detection methods only focus on detecting statistically significant drug-event pairs from the SRS database, and these methods often face challenges in identifying those new pharmacovigilance

Yue Yu, Kathryn Ruddy, Aaron Mansfield, Nansu Zong, Andrew Wen, Shintaro Tsuji, Ming Huang, Hongfang Liu, Nilay Shah, Guoqian Jiang

JMIR Med Inform 2020;8(6):e17353