Abstract: A method for treating amyotrophic lateral sclerosis includes administering an effective amount of 3-methyl-1-phenyl-2-pyrazolin-5-one or a physiologically acceptable salt thereof to a patient who is in need thereof and meets two or more features selected from a group of identified features.

Abstract: A method, computer program product, and system continually obtain machine-readable data sets related to a patient population diagnosed with a medical condition from one or more databases (different computing nodes in the distributed environment). The processor(s) continually applies a recurrent neural network to the plurality of data sets to machine learn optimal features for classifying patients into multiple categories related to presence or progression of the medical condition. The processor(s) continually generates, based on the machine learned optimal set of features, intermediate features, based on the weightings of a portion of the machine learned optimal set of features, i.e., a model. The processor(s) evaluates the records and classifies records into the categories, based on a current model (model generated in real-time).

Abstract: A computer system, computer-implemented method, and computer program product include a processor(s) (executing code) that obtains a data set(s) related to a patient population diagnosed with a medical condition a database(s). The processor(s) identifies common features, generates patterns of the common features, and generates machine learning algorithms based on the patterns to identify presence or absence of the given medical condition in an undiagnosed patient. The processor(s) compiles a training set of data and tunes the machine learning algorithms with the training set of data. The processor(s) integrates the machine learning algorithms into a graphical user interface.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A method for treating amyotrophic lateral sclerosis includes administering an effective amount of 3-methyl-1-phenyl-2-pyrazolin-5-one or a physiologically acceptable salt thereof to a patient who is in need thereof and meets two or more features selected from a group of identified features.

Abstract: A computer system, computer-implemented method, and computer program product include a processor(s) (executing code) that obtains a data set(s) related to a patient population diagnosed with a medical condition a database(s). The processor(s) identifies common features, generates patterns of the common features, and generates machine learning algorithms based on the patterns to identify presence or absence of the given medical condition in an undiagnosed patient. The processor(s) compiles a training set of data and tunes the machine learning algorithms with the training set of data. The processor(s) integrates the machine learning algorithms into a graphical user interface.