We develop methodologies and apply machine-learning algorithms to a database of ALS patients to expose and model underlying mechanisms and relations in the disease. We view the disease state as an ordinal variable (with values between 4 for normal function and 0 for complete loss of function), and show that ordinal classification applied to the data has an advantage over classification that does not utilize the ordinal nature of the domain. To identify important physiological and lab test variables in relation to patient functionality, we rank variables with a decision tree that predicts future disease state using current and past variable instantiations. In addition, we cluster data of patient functionalities in performing daily tasks into higher level groupings of body segments and show how certain variables relate more concretely to certain groupings than to others, thus reducing the dimensionality of the disease state representation in a natural manner that was found to be medically interpretable. Finally, we learn Bayesian networks to detect predictors within the Markov blanket of the disease-state variable and to expose relations among the predictors and with the disease state, as well as to identify value combinations of the predictors that distinguish severe and mild patients.