Adolescents with Mental Disorders: The Efficacy of a Multiprofessional Approach

In the book "Mental Illnesses - Evaluation, Treatments and Implications" attention is focused on background factors underlying mental illness. It is crucial that mental illness be evaluated thoroughly if we want to understand its nature, predict its long-term outcome, and treat it with specific rather than generic treatment, such as pharmacotherapy for instance. Additionally, community-wide and cognitive-behavioral approaches need to be combined to decrease the severity of symptoms of mental illness. Unfortunately, those who should profit the most by combination of treatments, often times refuse treatment or show poor adherence to treatment maintenance. Most importantly, what are the implications of the above for the mental health community? Mental illness cannot be treated with one single form of treatment. Combined individual, community, and socially-oriented treatments, including recent distance-writing technologies will hopefully allow a more integrated approach to decrease mental illness world-wide

A supervised learning approach for prognostic prediction in ALS using disease progression groups and patient profiles

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2018A Esclerose Lateral Amiotrófica (ELA) é uma Doença Neurodegenerativa caracterizada pela perda progressiva de neurónios motores, que causam inervação e comprometimento muscular. Pacientes que sofrem de ELA não têm geralmente um prognóstico promissor, morrendo entre de 3 a 5 anos após o início da doença. A causa mais comum de morte é a insuficiência respiratória. Não havendo uma cura para a ELA, muitos esforços estão concentrados na elaboração de melhores tratamentos para prevenir a progressão da doença. Tem sido comprovado que a Ventilação Não Invásiva (VNI) melhora o prognóstico quando administrado atempadamente. Esta dissertação propõe abordagens de aprendizagem automática para criar modelos capazes de prever a necessidade de VNI em pacientes com ELA dentro de um intervalo de tempo de k dias, possibilitando assim aos médicos antecipar a prescrição de VNI. No entanto, a heterogeneidade da doença apresenta um desafio para encontrar tratamentos e soluções que possam ser utilizadas para todos os pacientes. Com isso em mente, propomos duas abordagens de estratificação de pacientes, com o objetivo de criar modelos especializados que possam prever melhor a necessidade de VNI para cada um dos grupos criados. A primeira abordagem consiste em criar grupos com base na taxa de progressão do paciente, e a segunda consiste em criar perfis de pacientes agrupando avaliações de pacientes mais semelhantes usando métodos de agrupamento e perfis clínicos baseados em subconjuntos de características (Geral, Prognóstico, Respiratório e Funcional). Também testamos um conjunto de seleção de atributos, para avaliar o valor preditivo dos mesmos, bem como uma abordagem de imputação de valores ausentes para lidar com a alta proporção dos mesmos, característica comum para dados clínicos. Os modelos prognósticos propostos mostraram ser uma boa solução para a previsão da necessidade do uso de NIV, apresentando resultados geralmente promissores. Além disso, mostramos que o uso de estratificação de pacientes para criar modelos especializados, melhorando assim o desempenho dos modelos prognósticos, pode contribuir para um acompanhamento mais personalizado de acordo com as necessidades de cada paciente, melhorando assim o seu prognóstico e qualidade de vida.Amyotrophic Lateral Sclerosis (ALS) is a Neurodegenerative Disease characterized by the progressive loss of motor neurons, which cause muscular innervation and impairment. Patients who suffer from ALS usually do not have a promising prognosis, dying within 3-5 years from the disease onset. The most common cause of death is respiratory failure. With the lack of a cure for ALS, many efforts are focused in designing better treatments to prevent disease progression. Non-Invasive Ventilation (NIV) has been proven to improve prognosis when administered earlier on. This dissertation proposes machine learning approaches to create learning models capable to predict the need for NIV in ALS patients within a time window of k days, enabling clinicians to anticipate NIV prescription beforehand. However, the heterogeneity of the disease presents as a challenge to find treatments and solutions that can be used for all patients. With that in mind, we proposed two patient stratification approaches, with the aim of creating specialized models that can better predict the need for NIV for each of the created groups. The first approach consists in creating groups based on the patient’s progression rate, and the second approach consists in creating patient profiles by grouping patient evaluations that are more similar using clustering and clinical profiles based on subset of features (General, Prognostic, Respiratory, and Functional). We also tested a feature selection ensemble, to evaluate the predictive value of the features, as well as a Missing value imputation approach to deal with the high proportion of missing values, common characteristic for clinical data. The proposed prognostic models showed to be a good solution for prognostic prediction of NIV outcome, presenting overall promising results. Furthermore, we show that the use of patient stratification to create specialized models, thus improving performance in prognostic models that can contribute to a better-personalized care according to each patient needs, thus improving their prognostic and quality of life

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil (GRS) abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers. Hence, in this paper, a novel hybrid artificial intelligence (AI)-based model was developed by the combination of artificial neural network (ANN) and Harris hawks’ optimisation (HHO), that is, ANN-HHO, to predict the settlement of the GRS abutments. Five other robust intelligent models such as support vector regression (SVR), Gaussian process regression (GPR), relevance vector machine (RVM), sequential minimal optimisation regression (SMOR), and least-median square regression (LMSR) were constructed and compared to the ANN-HHO model. The predictive strength, relalibility and robustness of the model were evaluated based on rigorous statistical testing, ranking criteria, multi-criteria approach, uncertainity analysis and sensitivity analysis (SA). Moreover, the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature. The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models. Therefore, it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments. Finally, the model has been converted into a simple mathematical formulation for easy hand calculations, and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations

Aeronautical Engineering: A special bibliography with indexes, supplement 32, June 1973

This special bibliography lists 372 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1973

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival