Publications
Real-World Treatment Preferences Among People Living with ALS: A Discrete Choice Experiment
Katie Stenson, Lara Taylor, Nicholas Belviso, Teresa E. Fecteau, Paula Alvarez, Nandini Hadker, Matthew O'Hara, Amod Athavale, Olivia Green, Vijay Abhilash, Lucas Monserrat, Cali Orsulak, Raquel Norel, Meera Gandhi.
Biogen, 225 Binney Street, Cambridge, MA, USA
Trinity Life Sciences, Waltham, MA, USA
Northeast Amyotrophic Lateral Sclerosis Consortium, MA, USA
IBM Research, Yorktown Heights, NY, USA
EverythingALS, Seattle, WA, USA
Objective
Quantitatively assess which treatment attributes are most important to people living with amyotrophic lateral sclerosis (ALS; pALS) in the United States (US) when making treatment decisions
Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description
Carla Agurto(IBM), Guillermo Cecchi(IBM), Bo Wen(IBM), Ernest Fraenkel(MIT), James Berry(MGH), Indu Navar(EverythingALS) and Raquel Norel(IBM)
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal disease
that not only affects movement, speech, and breath but also
cognition. Recent studies have focused on the use of language analysis techniques to detect ALS and infer scales for monitoring functional progression. In this paper, we focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population. In an effort to reach the ALS population, which frequently exhibits mobility limitations, we implemented the digital version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) test for the first time.
Screening for amyotrophic lateral sclerosis through interactions with an internet search engine
Elad Yom-Tov, Indu Navar, Ernest Fraenkel, James D. Berry
Microsoft Research, Israel
EverythingALS, Seattle, Washington, USA
Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA
Sean M. Healey and AMG Center for ALS, Department of Neurology, Massachusetts General Hospital, USA
Abstract
Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with a diagnostic delay of over a year. Here we examine the possibility that interactions with an internet search engine could be used to help screen for ALS.
ALS Community Pressing Issues: Lessons from a Survey
A. Anvar, J. Berry, E. Fraenkel, I. Navar, G. A. Cecchi, R. Norel
Standford University, Palo Alto, CA
EverythingALS Peter Cohen Foundation, Los Altos, USA
MGH Institute of Health Professions, Boston, USA
Massachusetts Institute of Technology, Cambridge, USA
IBM Thomas J. Watson Research Center, Yorktown Heights, USA
Abstract
We gathered survey data to identify the unmet needs expressed by Amyotrophic Lateral Sclerosis (ALS) patients, caregivers, and advocates. Natural Language Processing was used to summarize free text data. Identified needs, named anchor topics were selected manually from the data. Text embedding was used to score participant answers to anchor topics. Despite a broad range of opinions among cohorts, we detected pain control, better access to information and ALSFRS-R alternatives as important ALS community issues. Our results could be used to better direct ALS community resources and research efforts.
Multimodal dialog based speech and facial biomarkers capture differential disease progression rates for ALS remote patient monitoring,
M. Neumann, O. Roesler, J. Liscombe, H. Kothare, D. Suendermann-Oeft, J. D. Berry, E. Fraenkel, R. Norel, A. Anvar, I. Navar, A. V. Sherman, J. R. Green and V. Ramanarayanan (2021).
In Proc. of: The 32nd International Symposium on Amyotrophic Lateral Sclerosis and Motor Neuron Disease, Virtual, December 2021.
Objective
Identify audiovisual speech markers that are responsive to clinical progression of Amyotrophic Lateral Sclerosis (ALS).
Lessons learned from a large-scale audio-visual remote data collection for Amyotrophic Lateral Sclerosis research.
Vikram Ramanarayanan, Michael Neumann , Aria Anvar, Oliver Roesler , Jackson Liscombe , Hardik Kothare , David Suendermann-Oeft , James D. Berry , Ernest Fraenkel , Raquel Norel , Alexander V. Sherman, Jordan R. Green and Indu Navar
Modality.AI, MGH Institute of Health Professions, Massachusetts Institute of Technology, IBM Thomas J. Watson Research Center, EverythingALS, Peter Cohen Foundation, Harvard University, University of California, San Francisco
Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale.
M. Neumann, O. Roesler, J. Liscombe, H. Kothare, D. Suendermann-Oeft, D. Pautler, I. Navar, A. Anvar, J. Kumm, R. Norel, E. Fraenkel, A. Sherman, J. Berry, G. Pattee, J. Wang, J. Green, V. Ramanarayanan: Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale . Accepted at Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czech Republic, August - September 2021
Accepted at Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czech Republic, August - September 2021.
Abstract
We investigate the utility of audiovisual dialog systems combined with speech and video analytics for real-time remote monitoring of depression at scale in uncontrolled environment settings. We collected audiovisual conversational data from participants who interacted with a cloud-based multimodal dialog system, and automatically extracted a large set of speech and vision metrics based on the rich existing literature of laboratory studies. We report on the efficacy of various audio and video metrics in differentiating people with mild, moderate and severe depression, and discuss the implications of these results for the deployment of such technologies in real-world neurological diagnosis and monitoring applications.
Towards A Large-Scale Audio-Visual Corpus for Research on Amyotrophic Lateral Sclerosis
A. Anvar, D. Suendermann-Oeft, D. Pautler, V. Ramanarayanan, J. Kumm, J. Berry, R. Norel, E. Fraenkel, and I. Navar: Towards A Large-Scale Audio-Visual Corpus for Research on Amyotrophic Lateral Sclerosis. In Proc. of AAN 2021, 73th Annual Meeting of the American Academy of Neurology, Virtual, April 2021.
In Proc. of AAN 2021, 73th Annual Meeting of the American Academy of Neurology, Virtual, April 2021
Objective
This presentation describes the creation of a large, open data platform, comprising speech and video recordings of people with ALS and healthy volunteers. Each participant is interviewed by Modality.AI’s virtual agent, emulating the role of a neurologist or speech pathologist walking them through speaking exercises [Fig 1] The collected data is made available to the academic and research community to foster acceleration of the development of biomarkers, diagnostics, therapies, and fundamental scientific understanding of ALS.
