Comparison of Machine Learning Methods in the Study of Cancer Survivors’ Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort

Badreau, Marie; Fadel, Marc; Bertin, Mélanie; Rapicault, Clémence; Gilbert, Fabien; Porro, Bertrand; Descatha, Alexis

doi:10.1007/s10926-023-10112-8

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Furthermore, machine learning offers the advantage of being relatively unconstrained by the limitations imposed by various assumptions in traditional research methodologies 15 . By simultaneously including several variables, machine learning can determine the relationship among key variables and assess the importance of predictive factors 16 . This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Factors influencing psychological distress among breast cancer survivors using machine learning techniques

Park,

Chun,

Bae

et al. 2024

Sci Rep

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Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of life of breast cancer survivors. This study aimed to assess the level of distress in breast cancer survivors and analyze the variables that significantly affect distress using machine learning techniques. A survey was conducted with 641 adult breast cancer patients using the National Comprehensive Cancer Network Distress Thermometer tool. Participants identified various factors that caused distress. Five machine learning models were used to predict the classification of patients into mild and severe distress groups. The survey results indicated that 57.7% of the participants experienced severe distress. The top-three best-performing models indicated that depression, dealing with a partner, housing, work/school, and fatigue are the primary indicators. Among the emotional problems, depression, fear, worry, loss of interest in regular activities, and nervousness were determined as significant predictive factors. Therefore, machine learning models can be effectively applied to determine various factors influencing distress in breast cancer patients who have completed primary treatment, thereby identifying breast cancer patients who are vulnerable to distress in clinical settings.

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Section: Introductionmentioning

confidence: 99%

Factors influencing psychological distress among breast cancer survivors using machine learning techniques

Park,

Chun,

Bae

et al. 2024

Sci Rep

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“…In occupational health research, the development of new modeling approaches like machine learning is bringing new insights in complex phenomenon, like occupational injuries or return-to-work [ 6 – 8 ]. For example, linking data from insurance companies, occupational health units, health care organizations or compensation claims and applying machine learning methods could bring promising results in the field of occupational health research and rehabilitation [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain

Fadel,

Petot,

Gourraud

et al. 2024

PLoS ONE

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Objectives Though the rise of big data in the field of occupational health offers new opportunities especially for cross-cutting research, they raise the issue of privacy and security of data, especially when linking sensitive data from the field of insurance, occupational health or compensation claims. We aimed to validate a large, blinded synthesized database developed from the CONSTANCES cohort by comparing associations between three independently selected outcomes, and various exposures. Methods From the CONSTANCES cohort, a large synthetic dataset was constructed using the avatar method (Octopize) that is agnostic to the data primary or secondary data uses. Three main analyses of interest were chosen to compare associations between the raw and avatar dataset: risk of stroke (any stroke, and subtypes of stroke), risk of knee pain and limitations associated with knee pain. Logistic models were computed, and a qualitative comparison of paired odds ratio (OR) was made. Results Both raw and avatar datasets included 162,434 observations and 19 relevant variables. On the 172 paired raw/avatar OR that were computed, including stratified analyses on sex, more than 77% of the comparisons had a OR difference ≤0.5 and less than 7% had a discrepancy in the statistical significance of the associations, with a Cohen’s Kappa coefficient of 0.80. Conclusions This study shows the flexibility and the multiple usage of a synthetic database created with the avatar method in the particular field of occupational health, which can be shared in open access without risking re-identification and privacy issues and help bring new insights for complex phenomenon like return to work.

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Le médecin confronté à l’IA (Intelligence artificielle) : Éthique et responsabilité

Manaouil,

Chamot,

Petit

2024

Médecine & Droit

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Comparison of Machine Learning Methods in the Study of Cancer Survivors’ Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort

Cited by 3 publications

References 30 publications

Factors influencing psychological distress among breast cancer survivors using machine learning techniques

Factors influencing psychological distress among breast cancer survivors using machine learning techniques

Flexibility of a large blindly synthetized avatar database for occupational research: Example from the CONSTANCES cohort for stroke and knee pain

Le médecin confronté à l’IA (Intelligence artificielle) : Éthique et responsabilité

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