PROTECT: Predictive approaches in managing long-term conditions: from remote monitoring data to digital biomarkers
Lead Research Organisation:
Imperial College London
Department Name: Brain Sciences
Abstract
The cost of care for long-term conditions is up to 70% of total health and social care expenditure and account for 50% of GP appointments in the UK. Developing data-driven preventative and predictive measures will enhance the quality of care services and reduce the cost of managing long-term conditions.
Emerging technologies such as the Internet of Things and wearable devices provide new opportunities to collect continuous in-home monitoring data such as movement, daily activity, vital signs and sleep. This data provides new possibilities to monitor the progression of conditions, do risk assessments and predict adverse healthcare events.
This research will develop new information engineering and machine learning methods to integrate and analyse continuous in-home monitoring data and create new digital biomarkers for personalised and preventative care. It will create scientific advances in in-home monitoring technologies and machine learning applied to healthcare and, in particular, managing long-term conditions. It will develop clinically applicable and care informed solutions to utilise remote monitoring data to extract health insights and provide predictive analysis and healthcare risk assessments. The research will contribute to the ambitions outlined in the call to transform care and health at home and enable independence by producing affordable solutions that be applied across a range of long-term health conditions.
The research programme will create a digital platform to integrate environmental and physiological information from in-home observation and measurement technologies safely and securely. It will apply machine learning models and solutions to derive digital biomarkers that can be used to improve healthcare. The overarching aim is to use the system to extract relevant information from complex datasets to allows effective and timely health interventions. To achieve this, the team will build the software infrastructure that allows sensor information to be collected in a safe and secure, and privacy-aware way and create the fundamental building blocks for the analysis of digital biomarkers through the development of person-centred and clinically informed predictive approaches.
Emerging technologies such as the Internet of Things and wearable devices provide new opportunities to collect continuous in-home monitoring data such as movement, daily activity, vital signs and sleep. This data provides new possibilities to monitor the progression of conditions, do risk assessments and predict adverse healthcare events.
This research will develop new information engineering and machine learning methods to integrate and analyse continuous in-home monitoring data and create new digital biomarkers for personalised and preventative care. It will create scientific advances in in-home monitoring technologies and machine learning applied to healthcare and, in particular, managing long-term conditions. It will develop clinically applicable and care informed solutions to utilise remote monitoring data to extract health insights and provide predictive analysis and healthcare risk assessments. The research will contribute to the ambitions outlined in the call to transform care and health at home and enable independence by producing affordable solutions that be applied across a range of long-term health conditions.
The research programme will create a digital platform to integrate environmental and physiological information from in-home observation and measurement technologies safely and securely. It will apply machine learning models and solutions to derive digital biomarkers that can be used to improve healthcare. The overarching aim is to use the system to extract relevant information from complex datasets to allows effective and timely health interventions. To achieve this, the team will build the software infrastructure that allows sensor information to be collected in a safe and secure, and privacy-aware way and create the fundamental building blocks for the analysis of digital biomarkers through the development of person-centred and clinically informed predictive approaches.
Organisations
Publications
Capstick A
(2024)
Digital remote monitoring for screening and early detection of urinary tract infections
in npj Digital Medicine
Fletcher-Lloyd N
(2023)
A quantitative analysis of the impact of COVID-19 quarantining on in-home eating and drinking habits in a cohort of people living with dementia
in Alzheimer's & Dementia
Fletcher-Lloyd N
(2024)
A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living With Dementia
in IEEE Internet of Things Journal
Hine C
(2023)
Negotiating the capacities and limitations of sensor-mediated care in the home
in Journal of Computer-Mediated Communication
Huang Y
(2024)
Analyzing entropy features in time-series data for pattern recognition in neurological conditions
in Artificial Intelligence in Medicine
Lima M
(2023)
Discovering Behavioral Patterns Using Conversational Technology for In-Home Health and Well-Being Monitoring
in IEEE Internet of Things Journal
Palermo F
(2023)
TIHM: An open dataset for remote healthcare monitoring in dementia.
in Scientific data
Title | TIHM/UKDRI Dataset |
Description | The dataset contains in-home sensory observation and measurement data from homes of people with dementia (n=170 homes). This includes high-resolution data from the participants' in-home movement, use of home appliances, daily activities, sleep, vital signs and daily wellbeing questionnaire. The cohort was part of the previous TIHM study. The study has continued as part of the UK DRI CR7T study since September 2019. The TIHM and UK DRI CR&T studies have collected over 40,0000 days of in-home observation and monitoring data and over 20,000 nights of sleep data collected using smart connected sensors. The dataset is currently used in our research to develop analytical models and digital biomarkers to predict adverse health conditions in dementia care. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | No |
Impact | We are using this dataset to develop digital biomarkers. The research is currently focused on identifying digital biomarkers that can be used for early detection of behavioural changes, infections, sleep disorders, risk of falls and social isolation. The current dataset and the previously collected dataset from the TIHM study are partially labelled and annotated for events such as infections, hospital admissions, hypertension, agitation and changes in the wellbeing of people living with dementia. The participants have given their consent for their data to be used for research and the study has obtained approval from an NHS ethics review panel. |
Title | TIHM: An open dataset for remote healthcare monitoring in dementia |
Description | Dementia is a progressive condition that affects cognitive and functional abilities. There is a need for reliable and continuous health monitoring of People Living with Dementia (PLWD) to improve their quality of life and support their independent living. Healthcare services often focus on addressing and treating already established health conditions that affect PLWD. Managing these conditions continuously can inform better decision-making earlier for higher-quality care management for PLWD. The Technology Integrated Health Management (TIHM) project developed a new digital platform to routinely collect longitudinal observational and measurement data within the home and apply machine learning and analytical models for the detection and prediction of adverse health events affecting the well-being of PLWD. This work describes the TIHM dataset collected during the second phase (i.e., feasibility study) of the TIHM project. The data was collected from homes of 56 PLWD and associated with events and clinical observations (daily activity, physiological monitoring, and labels for health-related conditions). The study recorded an average of 50 days of data per participant, totalling 2803 days. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | This dataset has been used to develop several models and analysis in dementia care, including: [1] Palermo et al., https://arxiv.org/abs/2110.09868 [2] Huang et al., https://arxiv.org/abs/2210.01736 [3] Rezvani et al., https://doi.org/10.1002/alz.052181 [4] Enshaeifar et al., https://doi.org/10.1145/3366424.3383541 [5] Rezvani et al., https://doi.org/10.1109/TKDE.2019.2961097 [6] Fletcher-Lloyd et al., https://doi.org/10.1101/2022.10.25.22281467 [7] Serban et al., https://doi.org/10.1038/s41746-022-00697-4 |
URL | https://zenodo.org/record/7622128 |
Description | Jawdrop Life Sciences Summit: Addressing Healthy Ageing, Pandemics, and AMR |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | A panel discussion on how the White City life science cluster is making old age better |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.whitecityinnovationdistrict.org.uk/jawdrop-life-sciences-24-putting-resilience-at-the-he... |
Description | Keynote, the 7th Singapore International Neuro-Cognitive Symposium |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | A keynote at the 7th Singapore International Neuro-Cognitive Symposium. The talk focused on the applications of AI in community care for dementia. |
Year(s) Of Engagement Activity | 2024 |
Description | Teaching and educational material on Machine Learning for Neuroscience |
Form Of Engagement Activity | Engagement focused website, blog or social media channel |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | This is an online set of material on machine learning methods, with a discussion of their use in neuroscience. We will cover several aspects of machine learning, from classical methods to deep learning. |
Year(s) Of Engagement Activity | 2024 |
URL | https://ml4ns.github.io |