Publications

Conference and Journal Papers:

ML Tlachac, Walter Gerych, Kratika Agrawal, Benjamin Litterer, Nicholas Jurovich, Saitheeraj Thatigotla, Jidapa Thadajarassiri, Elke Rundensteiner, “Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks”, IEEE International Conference on Big Data (BigData) Workshop on Big Data Analytic in Healthcare, 2022

  • Data Analyzed: Moodable text message content, and DAIC-WOZ clinical interview voice transcripts

Avantika Shrestha, ML Tlachac, Ricardo Flores, Elke Rundensteiner, “BERT Variants for Depression Screening with Typed and Transcribed Responses”, In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC ’22 Adjunct), 2022.

  • Data Analyzed: StudentSADD text prompts and unscripted voice transcripts

Ricardo Flores, ML Tlachac, Avantika Shrestha, and Elke A. Rundensteiner, “Temporal Facial Features for Depression Screening”, In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC ’22 Adjunct), 2022.

ML Tlachac and Samuel S. Ogden “Left on Read: Reply Latency for Anxiety & Depression Screening”, In Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp/ISWC ’22 Adjunct), 2022.

ML Tlachac, Avantika Shrestha, Mahum Shah, Benjamin Litterer, and Elke Rundensteiner, “Automated Construction of Lexicons to Improve Depression Screening with Text Messages”, IEEE Journal of Biomedical and Health Informatics (J-BHI) Special Issue on Advancing Biomedical Discovery & Healthcare Delivery Through Digital Technologies, pp 1-8, 2022.

ML Tlachac, Miranda Reisch, Brittany Lewis, Ricardo Flores, Lane Harrison, and Elke Rundensteiner, “Impact Assessment of Stereotype Threat on Mobile Depression Screening using Bayesian Estimation”, Healthcare Analytics, Elsevier, 2022.

Ricardo Flores, ML Tlachac, Ermal Toto, Elke Rundensteiner, “AudiFace: Multimodal Deep Learning for Depression Screening”, Machine Learning for Healthcare, 2022.

Ricardo Flores, ML Tlachac, Ermal Toto, Elke Rundensteiner, “Transfer Learning for Depression Screening from Follow-up Clinical Interview Questions”, Deep Learning Applications (DLAV), vol 4, Springer, 2022.

  • Data Analyzed: DAIC-WOZ clinical interview voice recordings

ML Tlachac, Ricardo Flores, Ermal Toto, Elke Rundensteiner, “Early Mental Health Uncovering with Short Scripted and Unscripted Voice Recordings”, Deep Learning Applications (DLAV), vol 4, Springer, 2022.

  • Data Analyzed: EMU and StudentSADD unscripted voice recordings, unscripted transcripts, and scripted voice recordings

ML Tlachac, Ricardo Flores, Miranda Reisch, Katie Housekeeper, Elke Rundensteiner, “DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected During the COVID-19 Pandemic to Screen for Mental Illnesses”, ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 6, no. 2, 2022.

ML Tlachac, Ricardo Flores, Miranda Reisch, Rimsha Kayastha, Nina Taurich, Veronica Melican, Connor Bruneau, Hunter Caouette, Joshua Lovering, Ermal Toto, Elke Rundensteiner, “StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic”, ACM Proceedings on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 6, no. 2, 2022.

Saskia Senn, ML Tlachac, Ricardo Flores, Elke Rundensteiner, “Ensembles of BERT for Depression Classification”, 44th International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), Accepted.

ML Tlachac, Ermal Toto, Joshua Lovering, Rimsha Kayastha, Nina Taurich, Elke Rundensteiner, “EMU: Early Mental Health Uncovering Framework and Dataset”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA) Special Session Machine Learning in Health, 2021.

Ricardo Flores, ML Tlachac, Ermal Toto, Elke Rundensteiner, “Depression Screening Using Deep Learning on Follow-up Questions in Clinical Interviews”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021.

Ermal Toto, ML Tlachac, Elke Rundensteiner, “AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening”, 30th ACM International Conference on Information and Knowledge Management (CIKM) Applied Research Track, 2021 (best applied paper).

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, 2021.

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), 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), 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, volume 24, no. 11, 2020, pp. 3326-3332.

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.

Theses:

  1. Miranda Reisch, “Utilizing Unimodal and Multimodal Deep Transfer Learning to Classify Mobile Speech Recordings with Mental Health Labels”, MS thesis, 2022.
  2. ML Tlachac, “Improving Mental Health Screening with Predictive and Generative Modeling of Text Messages”, Doctoral Dissertation, 2022.
  3. Saskia Senn, “Ensemble of BERT Variants for Depression Detection”, MS thesis, 2022.
  4. Ermal Toto, “Towards Instantaneous Mental Health Screening From Voice Using Machine and Deep Learning“, Doctoral Dissertation, 2021.

Major Qualifying Projects:

  1. Lillian Garfinkel, Madeline E. Halley, Nicholas S. Jurovich, Mairéad O’Neill, Brian R. Phillips, Jyalu Wu, “Deep Learning for Mental Health Screening Using Smartphone Data“, 2022.
  2. Rimsha Kayastha, Hunter Caouette, Miranda Reisch, Veronica R. Melican, Connor Bruneau, “Machine Learning for Mental Health Screening“, 2021.
  3. 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.
  4. Adonay Resom, Jerry Assan, Maurice Flannery, Yufei Gao, Yuxin Wu, “Machine Learning For Mental Health Detection“, 2019.
  5. Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, “Sensing Depression“, 2018.

Posters:

  1. Saskia Senn, ML Tlachac, Ricardo Flores, Elke Rundensteiner, “Ensembles of BERT for Depression Classification”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2022.
  2. Katie Houskeeper, Matthew Dzwil, Dante Amicarella, ML Tlachac, “Extraction of Named Entities from Text Messages”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2022.
  3. ML Tlachac, Miranda Reisch, Ricardo Flores, Elke Rundensteiner, “StudentSADD versus DepreST: Collecting Data During COVID-19 for Rapid Mental Illness Screening”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2022.
  4. Avantika Shrestha, ML Tlachac, Mahum Shah, Benjamin Litterer, Elke Rundensteiner, “Constructing Lexicons to Improve Depression Screening with Texts”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2022.
  5. Mahum Shah, ML Tlachac, Benjamin Litterer, Sai Thatigotla, Nicholas Jurovich, E Rundensteiner, “Improving Lexical Category Features for Depression Screening with Text Messages”, IEEE Conference on Biomedical and Health Informatics (BHI), 2021.
  6. Kratika Agrawal, ML Tlachac, Elke Rundensteiner, “Generating Conditional Text Messages based on Depression”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2021.
  7. Miranda Reisch, ML Tlachac, “Stereotype Threat Study on Mobile Application”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2021.
  8. Rimsha Kayastha, Veronica Melican, Connor Bruneau, Hunter Caouette, Miranda Reisch, Nina Taurich, Joshua Lovering, ML Tlachac, Ermal Toto, Elke Rundensteiner, “Student Depression Dataset Collection”, Women in Data Science (WiDS) Central MA Conference Poster Session, 2021.
  9. ML Tlachac, Elke Rundensteiner, “The 10 Most Important Features in Predicting Depression from Content of Retrospectively Harvested Text Messages”, IEEE Conference on Biomedical and Health Informatics (BHI), 2019.