AIMC Topic: Finland

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Perspectives on Speech Timing: Coupled Oscillator Modeling of Polish and Finnish.

Phonetica
This stud y was ai med at analyzing empirical duration data for Polish spoken at different tempos using an updated version of the Coupled Oscillator Model of speech timing and rhythm variability (O'Dell and Nieminen, 1999, 2009). We use Bayesian infe...

Forecasting Subjective Cognitive Decline: AI Approach Using Dynamic Bayesian Networks.

Journal of medical Internet research
BACKGROUND: Several potentially modifiable risk factors are associated with subjective cognitive decline (SCD). However, developmental patterns of these risk factors have not been used before to forecast later SCD. Practical tools for the prevention ...

Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developin...

The adoption of care robots in home care-A survey on the attitudes of Finnish home care personnel.

Journal of clinical nursing
AIMS AND OBJECTIVES: This article examines the attitudes of Finnish home care registered nurses, licensed vocational nurses and other health and social care personnel towards the introduction and use of care robots in home care.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis.

British journal of anaesthesia
BACKGROUND: To prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients' performance in a preoperatively performed ton...

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Journal of Alzheimer's disease : JAD
BACKGROUND AND OBJECTIVE: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.