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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. [ABSTRACT FROM AUTHOR]

Background: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality. Objectives: The primary objective was to correlate wearable device data with subjective sleep quality perceptions. Methods: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier. Results: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%. Discussion: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior. [ABSTRACT FROM AUTHOR]

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms. [ABSTRACT FROM AUTHOR]

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identified. Objectives: This study investigated the effects of de-identification on classification. Methods: Two pseudonymisation and two classification algorithms were applied to clinical messages from a telehealth system. Divergence in classification compared to clear text classification was measured. Results: Overall, de-identification notably altered classification. The delicate classification algorithm was severely impacted, especially losses of sensitivity were noticeable. However, the simpler classification method was more robust and in combination with a more yielding pseudonymisation technique, had only a negligible impact on classification. Conclusion: The results indicate that deidentification can impact text classification and suggest, that considering deidentification during development of the classification methods could be beneficial. [ABSTRACT FROM AUTHOR]

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms.

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identified., Objectives: This study investigated the effects of de-identification on classification., Methods: Two pseudonymisation and two classification algorithms were applied to clinical messages from a telehealth system. Divergence in classification compared to clear text classification was measured., Results: Overall, de-identification notably altered classification. The delicate classification algorithm was severely impacted, especially losses of sensitivity were noticeable. However, the simpler classification method was more robust and in combination with a more yielding pseudonymisation technique, had only a negligible impact on classification., Conclusion: The results indicate that de-identification can impact text classification and suggest, that considering de-identification during development of the classification methods could be beneficial.