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Found 12 papers, 4 papers with code
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model.
Disease Insight through Digital Biomarkers Developed by Remotely Collected Wearables and Smartphone Data
Digital Biomarkers and remote patient monitoring can provide valuable and timely insights into how a patient is coping with their condition (disease progression, treatment response, etc.
Predicting Clinical Intent from Free Text Electronic Health Records
In this paper we train a machine learning model to detect a clinician's intent to follow up with a patient from the patient's clinical notes.
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets.
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration.
Estimating Redundancy in Clinical Text
This work is a quantitative examination of information redundancy in EHR notes.
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care.
Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit
Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis.
Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset
Clinical coding is currently a labour-intensive, error-prone, but critical administrative process whereby hospital patient episodes are manually assigned codes by qualified staff from large, standardised taxonomic hierarchies of codes.
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results.
