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This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.

Piia Lavikainen,1,* Gunjan Chandra,2,* Pekka Siirtola,2 Satu Tamminen,2 Anusha T Ihalapathirana,2 Juha Röning,2 Tiina Laatikainen,3– 5 Janne Martikainen1 1School of Pharmacy, University of Eastern Finland, Kuopio, Finland; 2Biomimetics and Intelligent Systems Group, Faculty of ITEE, University of Oulu, Oulu, Finland; 3Joint Municipal Authority for North Karelia Social and Health Services (Siun Sote), Joensuu, Finland; 4Department of Public Health and Social Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; 5Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland*These authors contributed equally to this workCorrespondence: Piia Lavikainen, School of Pharmacy C/O Faculty of Health Sciences, University of Eastern Finland, P.O. Box 1627, Kuopio, FI-70211, Finland, Tel +358 40 7024682, Email piia.lavikainen@uef.fiPurpose: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors.Patients and Methods: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually.Results: Three HbA1c trajectories were distinguished over six years: “stable, adequate” (86.5%), “improving, but inadequate” (7.3%), and “fluctuating, inadequate” (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels.Conclusion: Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient’s unique risk factors offering a practical tool for clinicians to support treatment planning.Keywords: type 2 diabetes, cluster, HbA1c, machine learning, SHAP

Type 1 diabetes patients are prone to life-threatening conditions. Severe hypoglycemia and diabetic ketoacidosis are such conditions that often require urgent hospital care. Recently, artificial intelligence (AI) techniques have been used to improve the quality of diabetes care and management. These techniques provide a more comprehensive and better experience for patients and their loved ones. The objective of this study is to implement an AI-based explainable solution to predict possible severe hypoglycemia and diabetic ketoacidosis events in T1D patients within the next 12 months. The initial models in this study were built with baseline factors identified in prior research. However, baseline factors alone did not provide enough information, and the models were improved by introducing more features and separating the population by gender. The final predictive models highlighted some of the baseline factors in the original study when predicting the outcomes. Decision support systems based on machine learning models have become a viable way to enhance patient safety by locating and prioritizing high-risk patients. The final models were used to build a decision support system that facilitates precision medicine by prioritizing the high-risk patient group. Moreover, it helps to potentially reduce medical expenses through more efficient resource management.

A parallel corpus aligned at both sentence and word level is an important prerequisite in statistical machine translation. However, manual creation of such a parallel corpus is time consuming, and requires experts fluent in both languages. This paper presents the first ever empirical evaluation carried out to identify the best unsupervised word alignment technique for Sinhala and Tamil. It also presents a novel approach that combines the output of individual aligners, which outperforms the solitary use of these aligners. Sentence aligned parallel text from annual reports and letters of Sri Lankan Government institutions, and order papers from the Parliament of Sri Lanka were used in the evaluation.