Abstract: Described herein are systems and methods used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurological impairments. An exemplary method includes receiving input data of an individual related to a behavioral disorder, neurological impairment, or developmental delay, and evaluating the input data using an evaluation module comprising at least one machine learning model, thereby generating an evaluation result, where the machine learning model comprises one or more decision threshold hyperparameters that differentiate between a positive evaluation, negative evaluation, and an indeterminate evaluation with respect to a presence or an absence of the behavioral disorder, neurological impairment, or developmental delay.

Abstract: The methods and apparatus disclosed herein can diagnose or identify a subject as at risk of having one or more developmental disorders with fewer questions, decreased amounts of time, and determine a plurality of developmental disorders, and provide clinically acceptable sensitivity and specificity in a clinical environment. The methods and apparatus disclosed herein can be configured to diagnose or determine the subject as at risk of a developmental disorder among a plurality of developmental disorders, and decreasing the number of questions presented can be particularly helpful where a subject presents with a plurality of possible developmental disorders. A processor can be configured with instructions to identify a most predictive next question, such that a person can be diagnosed or identified as at risk with fewer questions.

Abstract: The methods and apparatus disclosed herein can diagnose or identify a subject as at risk of having one or more developmental disorders with fewer questions, decreased amounts of time, and determine a plurality of developmental disorders, and provide clinically acceptable sensitivity and specificity in a clinical environment. The methods and apparatus disclosed herein can be configured to diagnose or determine the subject as at risk of a developmental disorder among a plurality of developmental disorders, and decreasing the number of questions presented can be particularly helpful where a subject presents with a plurality of possible developmental disorders. A processor can be configured with instructions to identify a most predictive next question, such that a person can be diagnosed or identified as at risk with fewer questions.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: The methods and apparatus disclosed herein provide digital diagnostics and digital therapeutics to patients. The digital personalized medicine system uses digital data to assess or diagnose symptoms of a patient, and feedback from the patient response to treatment is considered to update the personalized therapeutic interventions. The methods and apparatus disclosed herein can also diagnose and treat cognitive function of a subject, with fewer questions, decreased amounts of time, and determine a plurality of behavioral, neurological or mental health disorders, and provide clinically acceptable sensitivity and specificity in the diagnosis and treatment.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: Disclosed herein is a machine learning platform and system for data analysis including for purposes of providing digital evaluations and therapeutics.

Abstract: Methods and compositions are provided herein for treating autism spectrum disorder in a subject, using one or more metabolites such as glutamate, malate, ursodeoxycholate, 5-dodecenoate, N-acetyl-L-glutamate, citrate, glycodeoxycholate, and carboxyethyl aminobutyric acid (CEGABA) and/or using one or more bacterial species such as bacterial species from the Streptococcaceae, Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, Butyricicoccaceae, and/or Pasteurellaceae families; from the Streptococcus, Blautia, Haemophilus, Faecalibacterium, Bacteroides, Roseburia, Fusicatenibacter, Lachnospira, and/or Agathobaculum genera, or Blautia wexlerae, Bacteroides vulgatus, Bacteroides ovatus, Roseburia inulinivorans, Roseburia intestinalis, Fusicatenibacter saccharivorans, and/or Agathobaculum butyriciproducens.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: Disclosed herein is a machine learning platform and system for data analysis including for purposes of providing digital evaluations and therapeutics.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed.

Abstract: A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: The methods and apparatus disclosed herein can diagnose or identify a subject as at risk of having one or more developmental disorders with fewer questions, decreased amounts of time, and determine a plurality of developmental disorders, and provide clinically acceptable sensitivity and specificity in a clinical environment. The methods and apparatus disclosed herein can be configured to diagnose or determine the subject as at risk of a developmental disorder among a plurality of developmental disorders, and decreasing the number of questions presented can be particularly helpful where a subject presents with a plurality of possible developmental disorders. A processor can be configured with instructions to identify a most predictive next question, such that a person can be diagnosed or identified as at risk with fewer questions.

Abstract: The methods and apparatus disclosed herein can diagnose or identify a subject as at risk of having one or more developmental disorders with fewer questions, decreased amounts of time, and determine a plurality of developmental disorders, and provide clinically acceptable sensitivity and specificity in a clinical environment. The methods and apparatus disclosed herein can be configured to diagnose or determine the subject as at risk of a developmental disorder among a plurality of developmental disorders, and decreasing the number of questions presented can be particularly helpful where a subject presents with a plurality of possible developmental disorders. A processor can be configured with instructions to identify a most predictive next question, such that a person can be diagnosed or identified as at risk with fewer questions.

Abstract: A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed.