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Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge.

Frailty and falls are a major public health problem in older adults. Muscle weakness of the lower and upper extremities are risk factors for any, as well as recurrent falls including injuries and fractures. While the Timed Up-and-Go (TUG) test is often used to identify frail members and fallers, tensiomyography (TMG) can be used as a non-invasive tool to assess the function of skeletal muscles. In a clinical study, we evaluated the correlation between the TMG parameters of the skeletal muscle contraction of 23 elderly participants (22 f, age 86.74 ± 7.88) and distance-based TUG test subtask times. TUG tests were recorded with an ultrasonic-based device. The sit-up and walking phases were significantly correlated to the contraction and delay time of the muscle vastus medialis (ρ = 0.55–0.80, p < 0.01). In addition, the delay time of the muscles vastus medialis (ρ = 0.45, p = 0.03) and gastrocnemius medialis (ρ = −0.44, p = 0.04) correlated to the sit-down phase. The maximal radial displacements of the biceps femoris showed significant correlations with the walk-forward times (ρ = −0.47, p = 0.021) and back (ρ = −0.43, p = 0.04). The association of TUG subtasks to muscle contractile parameters, therefore, could be utilized as a measure to improve the monitoring of elderly people’s physical ability in general and during rehabilitation after a fall in particular. TUG test subtask measurements may be used as a proxy to monitor muscle properties in rehabilitation after long hospital stays and injuries or for fall prevention.

Lower-limb strength is a marker of functional decline in elders. This work studies the feasibility of using the quasi-periodic nature of the distance between a subjects’ back and the chair backrest during a 30-s chair–stand test (CST) to carry out unsupervised measurements based on readings from a low-cost ultrasound sensor. The device comprises an ultrasound sensor, an Arduino UNO board, and a Bluetooth module. Sit-to-stand transitions are identified by filtering the signal with a moving minimum filter and comparing the output to an adaptive threshold. An inter-rater reliability (IRR) study was carried out to validate the device ability to count the same number of valid transitions as the gold-standard manual count. A group of elders (age: mean (m) = 80.79 years old, SD = 5.38; gender: 21 female and seven male) were asked to perform a 30-s CST using the device while a trained nurse manually counted valid transitions. Ultimately, a moving minimum filter was necessary to cancel the effect of outliers, likely produced because older people tend to produce more motion artefacts and, thus, noisier signals. While the intra-class correlation coefficient (ICC) for this study was good (ICC = 0.86, 95% confidence interval (CI) = 0.73, 0.93), it is not yet clear whether the results are sufficient to support clinical decision-making.

Heart failure is a common cardiac disease in elderly patients. After discharge, approximately 50% of all patients are readmitted to a hospital within six months. Recent studies show that home monitoring of heart failure patients can reduce the number of readmissions. Still, a large number of false positive alarms as well as underdiagnoses in other cases require more accurate alarm generation algorithms. New low-cost sensors for leg edema detection could be the missing link to help home monitoring to its breakthrough. We evaluated a 3D camera-based measurement setup in order to geometrically detect and quantify leg edemas. 3D images of legs were taken and geometric parameters were extracted semi-automatically from the images. Intra-subject variability for five healthy subjects was evaluated. Thereafter, correlation of 3D parameters with body weight and leg circumference was assessed during a clinical study at the Medical University of Graz. Strong correlation was found in between both reference values and instep height, while correlation in between curvature of the lower leg and references was very low. We conclude that 3D imaging might be a useful and cost-effective extension of home monitoring for heart failure patients, though further (prospective) studies are needed.