Publications

Conference and Journal Papers:

Edwin Boudreaux, Elke Rundensteiner, Feifan Liu, Bo Wang, Celine Larkin, Emmanuel Agu, Samiran Ghosh, Joshua Semeter, Gregory Simon, Rachel Davis-Martin, “Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions”,
Frontiers in Psychiatry Journal, section Mood and Anxiety Disorders, Accepted

ML Tlachac, Veronica Melican, Miranda Reisch, Elke Rundensteiner, “Mobile Depression Screening with Time Series of Text Logs and Call Logs”, 17th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), July 2021

ML Tlachac, Katherine Dixon-Gordon, Elke Rundensteiner, “Screening for Suicidal Ideation with Text Messages”, 17th IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), July 2021

ML Tlachac, Adam Sargent, Ermal Toto, Randy Paffenroth, Elke Rundensteiner, “Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020

Ermal Toto, ML Tlachac, Francis Lee Stevens, Elke Rundensteiner, “Audio-based Depression Screening using Sliding Window Sub-clip Pooling”, 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020

Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, Ermal Toto, Elke A. Rundensteiner, Emmanuel Agu, Rachel Davis-Martin, Edwin Boudreaux, “Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples and retrospectively harvested smartphone and social media data,” Smart Health, 2020.

ML Tlachac and Elke Rundensteiner, “Depression screening from text message reply latency,” in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2020, pp. 1–4.

ML Tlachac and Elke Rundensteiner, “Screening for depression with retrospectively harvested private versus public text,” IEEE Journal of Biomedical and Health Informatics, 2020.

ML Tlachac, Ermal Toto, Elke Rundensteiner, “You’re Making Me Depressed: Leveraging Texts from Contact Subsets to Predict Depression”, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019, pp. 1-4.

Walter Gerych, Emmanuel Agu and Elke Rundensteiner, “Classifying Depression in Imbalanced Datasets Using an Autoencoder- Based Anomaly Detection Approach,” 2019 IEEE 13th International Conference on Semantic Computing (ICSC), 2019.

Ermal Toto, Brandon J. Foley, Elke A. Rundensteiner, “Improving Emotion Detection with Sub-clip Boosting”, ECML/PKDD 2018.

Major Qualifying Projects:

  1. Rimsha Kayastha, Hunter Caouette, Miranda Reisch, Veronica R. Melican, Connor Bruneau, “Machine Learning for Mental Health Screening“, 2021.
  2. Adam Leigh Sargent, Joseph P Caltabiano, Myo Min Thant, Nicolas F Pingal, Yosias Ghion Seifu, Yared M Taye, “Mental Health Sensing Using Machine Learning“, 2020.
  3. Adonay Resom, Jerry Assan, Maurice Flannery, Yufei Gao, Yuxin Wu, “Machine Learning For Mental Health Detection“, 2019.
  4. Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, “Sensing Depression“, 2018.