LIV Well when Life Is Limited: Technology to Support Independence at Home.

There has been significant growth in technologies and services creating 'care at home' ecosystems for people with life-limiting conditions such as dementia. Dementia is one of the leading causes of disability and loss of independence that causes a heavy burden for families and caregivers. There is a clear need to support independent living of people living with dementia and their caregivers.

The Significance and Limitations of Sensor-based Agitation Detection in People Living with Dementia.

Agitation, a commonly observed behaviour in people living with dementia (PLwD), is frequently interpreted as a response to physiological, environmental, or emotional stress. Agitation has the potential to pose health risks to both individuals and their caregivers, and can contribute to increased caregiver burden and stress. Early detection of agitation can facilitate with timely intervention, which has the potential to prevent escalation to other challenging behaviors.

Detecting Depression-related Movement Changes in Older Adults using Smart Home Motion Sensors - A Feasibility Study.

Smart home sensor data is being increasingly used to identify health risks through passive tracking of specific behaviours and activity patterns. This study explored the feasibility of using motion sensor data to track changes in daytime movement patterns within the home, and their potential association with depression in older adults. This study analysed the motion sensor data collected during a one-year smart home trial, and explored their association with Geriatric Depression Scale (GDS) scores collected at three different time points during the trial (i.

Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey.

Social isolation (SI) and loneliness are 'invisible enemies'. They affect older people's health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care.

The Significance and Limitations of Monitoring Sleep during Pregnancy.

Sleep patterns often change during pregnancy and postpartum. However, if severe and persistent, these changes can depict a risk factor for significant health complications. It is thus essential to identify and understand changes in women's sleeping pattern over the course of pregnancy and postpartum, to offer an appropriate and timely intervention if necessary.

Association of Longitudinal Sleep and Next-day Indoor Mobility Measured via Passive Sensors among Community-dwelling Older Adults.

Previous studies have shown there is a relationship between sleep and mobility in older adults by collecting and analysing self-reported data from surveys and questionnaires, or by using objective measures from polysomnography or actigraphy. However, these methods have limitations for long-term monitoring, especially for community-dwelling adults. In this paper, we investigate the association between sleep and indoor mobility using longitudinal data collected over a period of about 12 months for older adults (65 years or older) living at home in Australia.

A validation study of a ballistocardiograph sleep tracker against polysomnography.

Study Objectives: Consumer home sleep trackers provide a great opportunity for longitudinal objective sleep monitoring. Nonwearable sleep devices cause little to no disruption in the daily life routine and need little maintenance. However, their validity needs further investigation.

Home-Based Sleep Sensor Measurements in an Older Australian Population: Before and during a Pandemic.

Older adults are susceptible to poor night-time sleep, characterized by short sleep duration and high sleep disruptions (i.e., more frequent and longer awakenings).

A prospective cohort study of prodromal Alzheimer's disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA).

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally.

Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals.

Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been discharged with a different initial diagnosis. Machine learning approaches have been devised to expedite the process and detect the cases that demand instant follow up.

Active learning reduces annotation time for clinical concept extraction.

Objective: To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation.

Materials And Methods: There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation.

Active learning: a step towards automating medical concept extraction.

Objective: This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined.

Materials And Methods: The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach.