Real science driving real change.
From our roots in academia to today, groundbreaking and peer-reviewed research provides the foundation for all that we do.
Committed to research.
From day one we’ve believed that technology can help extend the reach of evidence-based therapeutic practices, augment the work of clinicians, coaches, and social service providers, and help hire and train more people better and faster. And we’ve set out to prove it.
With deep expertise in speech signal processing, machine learning, user-centered design, and software engineering — as well as our decades of clinical expertise — we conduct scientific research on a continuing basis and develop innovative health technology solutions that are practical, scalable, and cost efficient.
More than 60 scientific papers and counting.
Collectively the leadership behind Lyssn have been conducting research on the use of AI in the assessment of wellness and behavioral health care delivery for decades – resulting in more than 50 peer-reviewed published studies. Below are just a few selected papers for our work:
- Bringing digital mental health to where it is needed most Nature Human Behavior, 2017. Ben-Zeev D, Atkins DC.
- Technology-enhanced human interaction in psychotherapy J Couns Psychol. 2017. Imel ZE, Caperton DD, Tanana M, Atkins DC.
- Computational psychotherapy research: scaling up the evaluation of patient-provider interactions Psychotherapy. 2015. Imel ZE, Steyvers M, Atkins DC.
- Design feasibility of an automated, machine-learning based feedback system for motivational interviewing Psychotherapy. 2019. Imel ZE, Pace BT, Soma CS, et al.
- “Rate My Therapist”: Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing PLoS ONE. 2015. Xiao B, Imel ZE, Georgiou PG, Atkins DC, Narayanan SS.
- Detecting conversation topics in primary care office visits from transcripts of patient-provider interactions Journal of the American Medical Informatics Association, 2019. Park J. et al.
- Content Coding of Psychotherapy Transcripts Using Labeled Topic Models IEEE J Biomed Health Inform. 2017. Gaut G, Steyvers M, Imel ZE, Atkins DC, Smyth P.
- “It’s hard to argue with a computer:” Investigating Psychotherapists’ Attitudes towards Automated Evaluation DIS (Des Interact Syst Conf). 2018. Hirsch T, Soma C, Merced K, Kuo P, Dembe A, Caperton DD, Atkins DC, Imel ZE.
- A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing J Subst Abuse Treat. 2016. Tanana M, Hallgren KA, Imel ZE, Atkins DC, Srikumar V.
- Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification Implement Sci. 2014. Atkins DC, Steyvers M, Imel ZE, Smyth P.
- Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. Singla K, Chen Z, Atkins D, Narayanan S.
- How do you feel? Using natural language processing to automatically rate emotion in psychotherapy Research Support, N.I.H., Extramural 2021. Michael J Tanana, Brian T Pace, David C Atkins, Zac E Imel et al
- Automated rating of patient and physician emotion in primary care visits Patient Education Counsel, 2021. Park J., Jindal A., Kuo P., Tanana M., Atkins D., Imel Z., et al.
- Human-AI Collaboration to Encourage Empathic Conversations in Text-based Mental Health Support University of Washington 2021. Sharma A., Lin I., Miner A., Atkins D., Althoff T.
- Development and Evaluation of ClientBot: Patient-Like Conversational Agent to Train Basic Counseling Skills Journel of Medical Internet Research 2019. Tanana M., Soma C., Srikumar V., Atkins D., Imel Z.