Tremor assessment using smartphone sensor data and fuzzy reasoning.

BACKGROUND: Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments. METHODS: We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient's intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules. RESULTS: Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78-81% compared to linear models and by 71-74% compared to a model based on decision trees. CONCLUSION: This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

No age thresholds in the emergency department: A retrospective cohort study on age differences.

Emergency care in elderly patients has gained attention by researchers due to high utilization rate and the importance of emergency services in elderly care. We examine if there is a clear age threshold between young and old patients at which there is a need for extra care and facilities in the emergency department. This retrospective cohort study uses emergency department (ED) data collected over the course of a year, containing information about 31,491 patient visits. The measured variables are treatment time, waiting time, number of tests, number of medical procedures, number of specialties involved and the patient's length of stay on the ED. To examine the multivariate differences between different patient groups, the data set is split into eighteen age groups and a MANOVA analysis is conducted to compare group means. The results show that older patients tend to have a longer stay on the ED. They also require more medical tests, have higher resource utilization and admission rates to the hospital. When the patients are grouped according to life stages (<18, 18-39, 40-64 and ≥65), each life stage shows significantly different characteristics across all variables. To understand where these differences start, age bins of five years are analyzed and almost none of the consecutive groups are significantly different in any variable. A significant difference between all groups is observed when age interval of the bins is increased to 10 years. This indicates that although age has an effect on the patient's treatment, a clear age threshold that identifies the group of elderly patients is not observable from emergency room variables. The results of this study show no clear age boundary between young and old patients. In other words, we could not find support for favoring the often-used age boundary of 65 over other boundaries (e.g. 60 or 70) to distinguish the group of elderly patients on the ED.