Introduction:
Pulmonary exacerbations are critical events in paediatric patients with asthma or cystic fibrosis (CF). These exacerbation events are often associated with sudden health deterioration and increased healthcare burden. The early prediction of exacerbations events could allow for timely interventions, and thus improved patient outcomes. This thesis attempted to develop a machine learning (ML) model to predict pulmonary exacerbations before they occur in a paediatric population using remote patient monitoring (RPM) data.

Methods:
A retrospective study was conducted using continuous data from wearable devices, daily spirometry, environmental data, and patient-reported outcomes. Predictions were focused on the occurrence of an exacerbation within three prediction windows (1-day, 3-day, and 7-day). Two ML approaches were considered: anomaly detection (using Gaussian mixture model, Isolation forest, One-class-SVM, and Local outlier factor), and classification models (Logistic regression, Random forest), using 5-fold nested cross-validation. Time-related transformations were performed to capture the temporal dependency of time-series data, including the feature engineering of clinical features related to heart rate and physical activity.

Results:
A total of 2401 home monitoring days of 90 paediatric patients, with 10 observed exacerbation events were included in the analysis. All models struggled to achieve high predictive value, with PR-AUC values below 0.20 and ROC-AUC values ranging from 0.43 to 0.72 across different time windows. No single model consistently outperformed the others. Despite the low performance, the models demonstrated better than random prediction for secondary outcomes, such as weekends and holidays, suggesting the ability to capture patterns in the data.

Conclusion:
This thesis shows the potential and limitations of using ML techniques for predicting pulmonary exacerbations using RPM data. The current anomaly detection and classification model performances are insufficient for clinical application. The low incidence of exacerbation events and the limitations in data quality contribute to these results. These findings point to the need for further refinement and more robust datasets to fully realise the potential of ML in the context of predicting pulmonary exacerbations.

Outlier detection is an essential part of modern systems. It is used to detect anomalies in behaviour or performance of systems or subjects, such as fall detection in smartwatches or voltage irregularity detection in batteries. This provides early indications of something of potential problems.

A part of outlier detection that is not often analysed is the performance of algorithms in environments with data from only one subject, versus environments with data from multiple subjects. This paper aims to answer the questions regarding the performance of Gaussian Mixture Models (GMM) and DBSCAN in these different environments. This paper focuses on time series data collected from consumer-grade wearables like smartwatches. In this paper, the outliers are defined manually, as the used data set did not contain predefined outliers. This research considers both outliers defined within the subject data, and the use of other subjects as outliers.

Results from this paper indicate that the amount of subjects in the environment is not the sole factor in the performance of these algorithms. Rather, it is a combination of the amount of subjects in the environment and the type of outlier to be detected. Results show that a GMM has difficulty distinguishing subjects that are similar when using another subject as outlier data. On average, DBSCAN outperforms a GMM in almost all cases, and DBSCAN is a lot more consistent in its performance than a GMM.

The aim of this paper is to complete the gap in the knowledge and experiment using as little as only the heart rate of some subjects to manage to successfully authorise them in some supposed system. The focus will be on the Gaussian Mixture model and the One Class Support Vector Machine, both outlier detectors, because most of the past research was focused on supervised models. Using these two, this paper will experiment with recognising intruders with models trained to distinguish one authorised person and multiple authorised persons. In the first case, multiple data processing methods and hyperparameters will be tested together and compared. In the second case, the goal will be to use and modify the best-found parameters of the first case to train models that are able to detect multiple persons as authorised. This time, there are two methods that this paper is going to look into and compare their performance: training one single model to detect all the subjects and training multiple models, one per subject. The most notable results are with one authorised person, with a score of 0.936, with two authorised, 0.88, and with 12 authorised, 0.713, when using the area under the curve metric.

Heart rate data and other data collected by consumer-grade wearable devices can give away quite useful information about the user. It can for example be used by machine learning algorithms such as Deep Neural Networks (DNN) to learn patterns about cardiovascular disease and fitness, or be used for identification. Heart rate patterns can also change quickly within the span of several months, which could make older heart rate data less useful when training a DNN. This paper shows that the DNN did indeed perform significantly worse when trying to identify people on older data compared to recent data. The accuracy calculated from the test set was 63.64% when trained on the most recently available training data, in comparison to 33.88% when trained on the least recent data which was more than 200 days older. When changing the recentness of training data only for a single user, there was also always an improvement in the accuracy of the model to identify that particular person. The accuracy to identify all users however did not necessarily increase, and sometimes even decreased. Using more data for training still outperforms using a smaller amount of samples of more recent data by slight margins, showing the trade-off between the recentness of data and the amount of data used for training. However, if fast training times are required, taking the most recent data windows can still lead to a similar performance as when training on all available data.

Cardiovascular diseases are one of the primary causes of mortality worldwide. Paroxysmal atrial fibrillation is a specific type that is difficult to detect and diagnose in a short time frame. To overcome this, we investigated if long-term wearable data can be used for the detection of heart diseases. The BigIdeasLab_STEP dataset and long-term Fitbit data from the ME-TIME study were used to examine this.

Our analysis showed a correlation between the window/stride size and accuracy when performing activity classification with the BigIdeasLAB_STEP dataset. Moreover, variability was found between subjects due to differences in the physical structure of their hearts. Normalization proved to be a crucial step to minimize the subject variability and significantly improved performance. Grouping subjects and performing classification inside a group helped to improve performance and decrease inter-subject variability. Integrating handcrafted features in deep learning networks also improved classification performance.

Analysis of the long-term Fitbit data showed that there is a difference between individuals based on their health condition. Classification of individual peaks was possible and worked best when utilizing a time series-specific support vector machine and grouping peaks together. Grouping peaks per week from a person and calculating a percentage of heart disease-predicted peaks also worked relatively well to distinguish between heart disease and reference subjects. Like with the previous dataset, normalization proved to be a crucial step to minimize subject variability.

The findings indicate that heart rate time series can be utilized for classification tasks like predicting activity or the detection of heart diseases. However, normalization and grouping techniques need to be chosen carefully to minimize the issue of subject variability.

Rhyming words are one of the most important features in poems. They add rhythm to a poem, and poets use this literary device to portray emotion and meaning to their readers. Thus, detecting rhyming words will aid in adding emotions and enhancing readability when generating poems. Previous studies have been done on the topic of poem generation. However, those works did not put too much emphasis on the rhyme detector. Thus, this research will solely focus on rhyme detection and its evaluation. The aim of this research is to determine the most accurate way of detecting whether two English words rhyme. English rhyming words will be detected using combinations of features. Five features are used: edit distance, hamming distance, jaccard similarity, longest common substring, and vowel and consonant weights. We also experiment with two methods of retrieving phonemes: using the entire phoneme translation, and using part of the phoneme translation. We find that using only hamming distance and jaccard similarity with part of the phoneme translation, we can already obtain an accuracy of 90.05% with a log loss of 0.25 when trained on a balanced dataset. The reason for this remains unclear because there is no clear separation between the two classes.

This paper compares the performance of two phonetic notations, IPA and ASJPcode, with the alphabetical notation for word-level language identification. Two machine learning models, a Multilayer Percerptron and a Logistic Regression model, are used to classify words using each of the three notations. With both models the IPA notation outperforms the other two notations.

Text classification has a wide range of usage such as extracting the sentiment out of a product review, analyzing the topic of a document and spam detection. In this research, the text classification task is to predict from which TV-show a given line is. The skip-gram model, originally used to train the Word2Vec sentence embeddings [Mikolov et al, 2013], is adapted to determine the likelihood of occurrence of a sentence in a TV-show. Based on this feature, a classifier is built to perform the task of this research. The results of the cross-validation show that it reaches an accuracy of 58% when running on the transcript data of 3 shows and 43% on 4 shows, while the accuracies of random guessing are supposed to be 33% and 25%. The difference between the neural networks and the skip-gram model becomes smaller when more shows are added to evaluate the model. Among each 5 fold cross-validation of the two models, the best results appear in the midmost iterations.

Using transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF for this feature extraction, achieving an accuracy rate of 58\% and 67\% respectively on a binary classification. The paper also explores the effect of several data cleaning techniques on the results.

Git repository containing the source code used in the paper - https://github.com/David-Happel/scene-location-NLP

Word embeddings are useful for various applications, such as sentiment classification (Tang et al., 2014), word translation (Xing, Wang, Liu, & Lin, 2015) and résumé parsing (Nasser, Sreejith, & Irshad, 2018). Previous research has determined that word embeddings contain gender bias, which can be problematic in certain applications such as résumé parsing. This research has addressed the question whether gender bias is present in word embeddings of different languages. Gender bias has been measured on word embedding of 26 different lan- guages with the help of the Word Embedding Association Test by Caliskan, Bryson, and Narayanan (2017). The results show that most of the tested languages seem to have bias towards male, while a few languages seem to have a bias towards female. This result is in line with previous literature.