Discovering Latent Psychological Structures from Self-Report Assessments of Hospital Workers

Kao, Hsien-Te; Hosseinmardi, Homa; Shen, Yan; Hasan, Michelle; Narayanan, Shrikanth; Lerman, Kristina; Ferrara, Emilio

doi:10.1109/besc.2018.8697325

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…The steel industry also presents relevant research for systematic mapping (Krishna et al, 2015;Sarkar et al, 2017Sarkar et al, , 2018Sarkar et al, , 2019aShirali et al, 2018), as well as applications in the area of healthcare (Di Noia et al, 2019;Kao et al, 2018;Olsen et al, 2009;Saâdaoui et al, 2015;Ueno et al, 2008), mining (Luo et al, 2016;Qu, 2009;Sanmiquel et al, 2015Sanmiquel et al, , 2018 and petrochemical (Bevilacqua et al, 2008;Cheng et al, 2013;Sanchez-Pi et al, 2014;Waghmare & Pai, 2013).…”

Section: Insights Identified In the Systematic Mapping On Dm In Oshmentioning

confidence: 99%

Data mining in occupational safety and health: a systematic mapping and roadmap

Reis¹,

Rosa²,

Machado³

et al. 2021

Prod.

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Paper aims: This research presents a literature overview in relation to data mining and machine learning applications in the area of occupational health and safety.Originality: A summary of main insights obtained from the analysis of systematic mapping is presented at the end, as well as a roadmap with recommendations for directing future research on the topic.Research method: This article carries out a thorough descriptive research of the scientific literature on the topic through a systematic mapping covering the period between the years 2008 and 2019 and 12 scientific databases, which at the end presents 68 selected records.Main findings: Around 84% of the selected records were of total significance for the research, with the majority of them being classified in the areas of civil construction and steel industry.Implications for theory and practice: Through this study it is possible to understand the way research has been developed on this theme, as well as point to the guidelines for future studies. Other contribution is the indication of studies in OSH 4.0 concept, based on monitoring workers full-time.

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Section: Insights Identified In the Systematic Mapping On Dm In Oshmentioning

confidence: 99%

Data mining in occupational safety and health: a systematic mapping and roadmap

Reis¹,

Rosa²,

Machado³

et al. 2021

Prod.

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“…Finally, in [51] we took a more global approach and used several of the data streams collected through sensors to infer self-assessments of participants, i.e., scored surveys.…”

Section: Work Using the Data Setmentioning

confidence: 99%

TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers

Mundnich,

Booth,

L'Hommedieu

et al. 2020

Preprint

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We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to investigate the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs such as job performance, interpersonal interaction, and well-being of hospital workers over time in their natural day-to-day job settings. We collected behavioral and physiological data from n = 212 participants through Internet-of-Things Bluetooth data hubs, wearable sensors (including a wristband, a biometrics-tracking garment, a smartphone, and an audio-feature recorder), together with a battery of surveys to assess personality traits, behavioral states, job performance, and well-being over time. Besides the default use of the data set, we envision several novel research opportunities and potential applications, including multi-modal and multi-task behavioral modeling, authentication through biometrics, and privacy-aware and privacy-preserving machine learning.

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“…Users can be separated into cohorts by means of clustering using similarity measures, and personalized modeling can be applied on each user using cohort characteristics. [18,19,25,45,54,55]. Personalized healthcare is not possible without users providing sufficient historical data.…”

Section: Introductionmentioning

confidence: 99%

User-Based Collaborative Filtering Mobile Health System

Kao¹,

Shen²,

Hosseinmardi³

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Mobile health systems predict health conditions based on multimodal signals. Users are often reluctant to provide their health status over privacy concerns. It is challenging to make health predictions without sufficient historical data from the users. In this paper, we propose a user-based collaborative filtering mobile health system. The system requests users to provide a few health labels. These labels are used to determine cohort similarity and discarded afterward to ensure privacy protection. The cohorts are designed to maximize user similarity across health labels, variable relationships, and sensor data. Our system predicts users based on the health information from their cohort. We empirically evaluate the system by conducting a ten-week longitudinal study to assess the health conditions of 212 hospital workers using mobile devices, wearables, and sensors. The results show successful cohort assignments with five health labels. Health predictions achieve promising performance without historical data. Our system demonstrates strong interpretability, predictability, and usability.

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Discovering Latent Psychological Structures from Self-Report Assessments of Hospital Workers

Cited by 6 publications

References 30 publications

Data mining in occupational safety and health: a systematic mapping and roadmap

Data mining in occupational safety and health: a systematic mapping and roadmap

TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers

User-Based Collaborative Filtering Mobile Health System

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