Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology created for this project involving the comparison of all possible pairs of temporal data points for each patient. This data was then used to train various machine learning models. Models were evaluated using 7-fold cross-validation on the training dataset and confirmed using data from a separate testing dataset (110 patients). A neural network model was effective (mAUC = 0.866) at predicting the progression of AD on a month-by-month basis, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.Comment: 10 pages; updated acknowledgement

Deep learning methods to predict amyotrophic lateral sclerosis disease progression

Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient’s treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression

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

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Learning predictive models from temporal three-way data using triclustering: applications in clinical data analysis

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020O conceito de triclustering estende o conceito de biclustering para um espaço tridimensional, cujo o objetivo é encontrar subespaços coerentes em dados tridimensionais. Considerando dados com dimensão temporal, a necessidade de aprender padrões temporais interessantes e usá-los para aprender modelos preditivos efetivos e interpretáveis, despoleta necessidade em investigar novas metodologias para análise de dados tridimensionais. Neste trabalho, propomos duas metodologias para esse efeito. Na primeira metodologia, encontramos os melhores parâmetros a serem usados em triclustering para descobrir os melhores triclusters (conjuntos de objetos com um padrão coerente ao longo de um dado conjunto de pontos temporais) para que depois estes padrões sejam usados como features por um dos mais apropriados classificadores encontrados na literatura. Neste caso, propomos juntar o classificador com uma abordagem de triclustering temporal. Para isso, idealizámos um algoritmo de triclustering com uma restrição temporal, denominado TCtriCluster para desvendar triclusters temporalmente contínuos (constituídos por pontos temporais contínuos). Na segunda metodologia, adicionámos uma fase de biclustering para descobrir padrões nos dados estáticos (dados que não mudam ao longo do tempo) e juntá-los aos triclusters para melhorar o desempenho e a interpretabilidade dos modelos. Estas metodologias foram usadas para prever a necessidade de administração de ventilação não invasiva (VNI) em pacientes com Esclerose Lateral Amiotrófica (ELA). Neste caso de estudo, aprendemos modelos de prognóstico geral, para os dados de todos os pacientes, e modelos especializados, depois de feita uma estratificação dos pacientes em 3 grupos de progressão: Lentos, Neutros e Rápidos. Os resultados demonstram que, além de serem bastante equiparáveis e por vezes superiores quando comparados com os resultados obtidos por um classificador de alto desempenho (Random Forests), os nossos classificadores são capazes de refinar as previsões através das potencialidades da interpretabilidade do modelo. De facto, quando usados os triclusters (e biclusters) como previsores, estamos a promover o uso de padrões de progressão da doença altamente interpretáveis. Para além disso, quando usados para previsão de prognóstico em doentes com ELA, os nossos modelos preditivos interpretáveis desvendaram padrões clinicamente relevantes para um grupo específico de padrões de progressão da doença, ajudando os médicos a entender a elevada heterogeneidade da progressão da ELA. Os resultados mostram ainda que a restrição temporal tem impacto na melhoria da efetividade e preditividade dos modelos.Triclustering extends biclustering to the three-dimensional space, aiming to find coherent subspaces in three-way data (sets of objects described by subsets of features in a subset of contexts). When the context is time, the need to learn interesting temporal patterns and use them to learn effective and interpretable predictive models triggers the need for new research methodologies to be used in three-way data analysis. In this work, we propose two approaches to learn predictive models from three-way data: 1) a triclustering-based classifier (considering just temporal data) and 2) a mixture of biclustering (with static data) and triclustering (with temporal data). In the first approach, we find the best triclustering parameters to uncover the best triclusters (sets of objects with a coherent pattern along a set of time-points) and then use these patterns as features in a state-of-the-art classifier. In the case of temporal data, we propose to couple the classifier with a temporal triclustering approach. With this aim, we devised a temporally constrained triclustering algorithm, termed TCtriCluster algorithm to mine time-contiguous triclusters. In the second approach, we extended the triclustering-based classifier with a biclustering task, where biclusters are discovered in static data (not changed over the time) and integrated with triclusters to improve performance and model explainability. The proposed methodologies were used to predict the need for non-invasive ventilation (NIV) in patients with Amyotrophic Lateral Sclerosis (ALS). In this case study, we learnt a general prognostic model from all patients data and specialized models after patient stratification into Slow, Neutral and Fast progressors. Our results show that besides comparable and sometimes outperforming results, when compared to a high performing random forest classifier, our predictive models enhance prediction with the potentialities of model interpretability. Indeed, when using triclusters (and biclusters) as predictors, we promoting the use of highly interpretable disease progression patterns. Furthermore, when used for prognostic prediction in ALS, our interpretable predictive models unravelled clinically relevant and group-specific disease progression patterns, helping clinicians to understand the high heterogeneity of ALS disease progression. Results further show that the temporal restriction is effective in improving the effectiveness of the predictive models