Jeffrey Rogers’s research while affiliated with IBM Research - Thomas J. Watson Research Center and other places

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Publications (37)


ID: 320139 Longitudinal Patient Monitoring with Patient States with Varied Data
  • Article

October 2024

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3 Reads

Neuromodulation

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ERIC LOUDERMILK

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Julio Paez

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[...]

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Matthew McDonald










Citations (12)


... Berger et al. [28] pointed to difficulties in standardizing metrics, such as those for placebo responses in clinical trials, which complicate cross-study comparisons. J. M. Reinen et al. [14] emphasized the need to assess the balance of positive versus negative inputs in digital health interventions, a largely neglected area. Furthermore, C. Agurto et al. [17] highlighted the potential of unstructured speech data in chronic pain research, yet its integration into clinical studies remains limited due to a lack of methodological tools. ...

Reference:

Decade of Natural Language Processing in Chronic Pain: A Systematic Review
Remotely-captured, free-text responses track with patient health states in chronic pain
  • Citing Conference Paper
  • July 2024

... Venerito and Iannone [13] used prompt-engineered sentiment analysis with Mistral-7B for fibromyalgia diagnosis, achieving high accuracy (0.87) but limited to a single application. Similarly, C. Agurto et al. [17] combined textual and audio data using RoBERTa and Whisper models, identifying correlations between mood, pain, and alertness but struggling to integrate these insights into clinical workflows. The number of studies utilizing LLMs in this review is limited. ...

Exploring Chronic Pain Experiences: Leveraging Text and Audio Analysis to Infer Well-Being Metrics
  • Citing Conference Paper
  • July 2024

... The increase in the use of continuous EEG (cEEG) in neurocritical care has exposed a gap in the availability of clinicians to read cEEG, even in wellresourced hospitals. Automated EEG seizure-detection algorithms are becoming more available [4], while their quality is expected to improve. AI-based seizure-detection software moves cEEG into the role of a continuous neuromonitor, where an alarm can alert a provider to assess the patient in real-time. ...

Artificial intelligence‐enhanced epileptic seizure detection by wearables
  • Citing Article
  • Full-text available
  • October 2023

... The evaluation aims to determine which placement strategy best balances resource efficiency and application performance under different network conditions. To conduct the experiments, we selected three distinct applications: CPuS-IoT, a cyber-physical IoT framework for manufacturing systems [12]; Health Guardian, a digital health monitoring application [13]; and VAS, a Video Analytics Serving application [14]. ...

Health Guardian: Using Multi-modal Data to Understand Individual Health
  • Citing Conference Paper
  • July 2023

... Less than one third of the studies (N = 6, 27%) used public datasets. In more detail, 3 studies [68,79,84] used the mPower dataset [86] and 3 studies [65,69,70] used data from the Physionet vertical ground reaction force (VGRF) dataset [87]. The Physionet database contains measures of gait from 93 PwPD and 73 HC. ...

An Automated Digital Biomarker of Mobility
  • Citing Conference Paper
  • July 2023

... The application's front-end handles sample measurement. Its back-end consists of the clinical task manager (CTM) that ensures secure data storage in the cloud and expands the application's capabilities [26], including connecting to physician dashboards for better data visualization, advancing complex data analytics and enabling data sharing with other applications, as well as fostering collaboration, real-time monitoring, and thorough data analysis. ...

Health Guardian Platform: A technology stack to accelerate discovery in Digital Health research
  • Citing Conference Paper
  • July 2022

... A methodology allows serious games in a medical setting to leverage multimodal data. To remotely monitor patients using mobile devices has grown substantially, but the results aren't always easy to understand [16]. It provides a method that uses clinical expertise to evaluate outcomes and a data-driven approach to generate meaningful patient status representations from complicated data streams. ...

Definition and clinical validation of Pain Patient States from high-dimensional mobile data: application to a chronic pain cohort
  • Citing Conference Paper
  • July 2022

... While it is known that various chronic pain symptoms can co-occur, it remains currently unknown whether symptom profiles may be successfully organized into distinct health states. Here, we propose a method by which we aim to identify clusters from high-dimensional, longitudinal data in chronic pain patients, and label them as Pain Patient States that may be operationalized for clinical application and decision making [15] [16]. To this end, we examined data from chronic pain patients in three subsets of data: 1) with questionnaires only; 2) with questionnaires plus voice data; and 3) with questionnaires plus actigraphy data. ...

ID:16310 Patient States: Artificial Intelligence-Driven Metric Providing Comprehensive Yet Straightforward Understanding of Chronic Pain Patients
  • Citing Article
  • July 2022

Neuromodulation

... For example, a model-free technique [29] was proposed for the detection of bradykinesia in PD from videos using optical flow. A model-based technique was also proposed [30] which relies on 2D and 3D poses and uses an ensemble of deep learning models to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores. ...

Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos
  • Citing Conference Paper
  • June 2021