Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19133, first published .
Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Social Reminiscence in Older Adults’ Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning

Journals

  1. Luo M, Debelak R, Schneider G, Martin M, Demiray B. With a little help from familiar interlocutors: real-world language use in young and older adults. Aging & Mental Health 2021;25(12):2310 View
  2. Ferrario A, Luo M, Polsinelli A, Moseley S, Mehl M, Yordanova K, Martin M, Demiray B. Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach. JMIR Aging 2022;5(1):e28333 View
  3. Chiavi D, Haag C, Chan A, Kamm C, Sieber C, Stanikić M, Rodgers S, Pot C, Kesselring J, Salmen A, Rapold I, Calabrese P, Manjaly Z, Gobbi C, Zecca C, Walther S, Stegmayer K, Hoepner R, Puhan M, von Wyl V. The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing. JMIR Medical Informatics 2022;10(11):e37945 View
  4. Hsu J, Christensen P, Ge Y, Long S. Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing. Journal of Pathology Informatics 2022;13:100123 View
  5. Li Y, Li C, Zhang T, Wu L, Lin X, Li Y, Wang L, Yang H, Lu D, Miao D, Fang P. Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data. Frontiers in Neuroscience 2023;17 View
  6. Ferrario A, Loi M. The Robustness of Counterfactual Explanations Over Time. IEEE Access 2022;10:82736 View
  7. Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR mHealth and uHealth 2022;10(8):e33850 View
  8. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
  9. Masukawa K, Aoyama M, Yokota S, Nakamura J, Ishida R, Nakayama M, Miyashita M. Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records. Palliative Medicine 2022;36(8):1207 View
  10. Röcke C, Luo M, Bereuter P, Katana M, Fillekes M, Gehriger V, Sofios A, Martin M, Weibel R. Charting everyday activities in later life: Study protocol of the mobility, activity, and social interactions study (MOASIS). Frontiers in Psychology 2023;13 View
  11. Stoev T, Ferrario A, Demiray B, Luo M, Martin M, Yordanova K. Coping With Imbalanced Data in the Automated Detection of Reminiscence From Everyday Life Conversations of Older Adults. IEEE Access 2021;9:116540 View
  12. Wang H, Zheng J, Xiang J. Online bearing fault diagnosis using numerical simulation models and machine learning classifications. Reliability Engineering & System Safety 2023;234:109142 View
  13. Zhang Y, Cui J, Wan W, Liu J, Uddin Z. Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57. Computational Intelligence and Neuroscience 2022;2022:1 View
  14. Zhao H, Su Y, Lyu Z, Tian L, Xu P, Lin L, Han W, Fu P. Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning. Academic Radiology 2024;31(1):35 View
  15. Ferrario A, Demiray B. Understanding reminiscence and its negative functions in the everyday conversations of young adults: A machine learning approach. Heliyon 2024;10(1):e23825 View

Books/Policy Documents

  1. Gao L, Hong L, Mashhadi A. Social Informatics. View