Pedro Amaral

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related… Exibir mais

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient’s condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient’s current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians’ experience. Exibir menos

Companies rely on contact centers to act as communication links with their clients. Outbound dialing is often used to reach existing or new customers. This task is generally performed by automatic dialers, which initiate new calls depending on the amount of working agents. The probability of a customer answering a call, however, depends on a set of conditions, such as the time schedule or the type of day. This fact presents itself as a challenge to automatic dialers, since contact lists with… Exibir mais

Companies rely on contact centers to act as communication links with their clients. Outbound dialing is often used to reach existing or new customers. This task is generally performed by automatic dialers, which initiate new calls depending on the amount of working agents. The probability of a customer answering a call, however, depends on a set of conditions, such as the time schedule or the type of day. This fact presents itself as a challenge to automatic dialers, since contact lists with low answer probability can make the contact center’s agent occupation rate very low. Predictive dialers tackle this problem in an automated way by generating more calls than the number of available agents. The majority of predictive dialer algorithms use statistical approaches to adjust the automatic dialer intensity, which is used to decide on the amount of calls that should be initiated at each time. In this paper, we propose a method of optimizing the automatic dialer intensity using genetic algorithms – evolutionary methods based on natural selection and genetics. We implement the proposed algorithm by modifying the current proprietary Altitude Software predictive dialer and perform a comparative evaluation between both versions. Our method obtained superior results to those achieved by the original algorithm, with a slightly higher agent utilization rate. Exibir menos

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease causing motor impairment. It is the third in the ranking of incidence of neurodegenerative diseases. It has no cure and it makes survival time after diagnosis very limited. Since most patients die from respiratory failure, predicting the onset of hypoventilation is crucial to prolong survival and improve the patients’ quality of life.
This paper describes the preprocessing of clinical data to be used in a later… Exibir mais

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease causing motor impairment. It is the third in the ranking of incidence of neurodegenerative diseases. It has no cure and it makes survival time after diagnosis very limited. Since most patients die from respiratory failure, predicting the onset of hypoventilation is crucial to prolong survival and improve the patients’ quality of life.
This paper describes the preprocessing of clinical data to be used in a later phase to predict the need for non-invasive ventilation in patients with ALS. We propose a hierarchical clustering based approach to preprocess longitudinal clinical data. We use this approach to cluster temporally-related clinical exams, while constructing the instances needed to train the machine learning algorithms. We consider that an instance is a snapshot of a patient’s condition at a given time window, which is characterized by a set of attributes resulting from merging clinical exams. We compare our approach with the standard approach followed by clinicians, and show the superiority of our results. Exibir menos

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease causing motor impairment. It is the third in the ranking of incidence of neurodegenerative diseases, has no cure, and its survival time after diagnosis is very limited. Since most patients die from respiratory failure, predicting the onset of hypoventilation is of major importance to prolong survival and improve the patients’ quality of live. In this paper, we use real data from patients with ALS to predict their… Exibir mais

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease causing motor impairment. It is the third in the ranking of incidence of neurodegenerative diseases, has no cure, and its survival time after diagnosis is very limited. Since most patients die from respiratory failure, predicting the onset of hypoventilation is of major importance to prolong survival and improve the patients’ quality of live. In this paper, we use real data from patients with ALS to predict their need for non-invasive ventilation. We use state of the art supervised learning techniques combined with feature selection approaches, achieving a performance over 80%. The problem of data unbalancement is also tackled. Furthermore, we propose an approach based on hierarchical clustering to preprocess longitudinal clinical data. We use this approach to cluster temporarily related clinical exams, while constructing the instances needed to train the classifiers. We consider that an instance is a snapshot of a patient’s condition at a given time window, which is characterized by a set of attributes resulting from clinical exams. Each instance is labelled with a class indicating whether or not non-invasive ventilation was needed. Exibir menos