Abstract: The present disclosure generally relates to generating a care and treatment plan for a patient using machine-learning techniques. An exemplary computer-implemented method comprises: receiving a plurality of care records; determining, based on a first machine learning model, that the plurality of care records are to be merged into an episode, wherein the episode is a predefined data representation comprising a predefined collection of data fields; identifying, from the plurality of care records, a plurality of attribute values corresponding to a particular attribute; determining, based on a second machine learning model, an aggregated attribute value based on the plurality of attribute values; updating, based on the aggregated attribute value, a data field of the episode; adding the episode to the patient profile, wherein the patient profile comprises a plurality of episodes associated with the patient.

Abstract: Embodiments of the present disclosure include systems and methods for generating a medical recommendation. Methods according to the present disclosure include receiving patient data associated with a first patient and receiving second patient data associated with a second patient. The methods further include inputting the first patient data and the second patient data into a trained machine-learning model to determine a first set of one or more risk values for the first patient and a second set of one or more risk values for the second patient. The methods further include comparing the first set of one or more risk values and the second set of one or more risk values to determine a priority for distributing care to the first patient and the second patient. In accordance with the determination that the first patient has priority, the system can generate a medical recommendation.

Abstract: The present specification describes a system for diagnosing or screening one or more pathologies in a patient. The system includes a headset with at least one microphone or accelerometer to passively receive vibrations generated by the cerebral vasculature of the patient's brain, computing devices coupled with the headset for processing the received vibrations to obtain a unique signal, and a signal analyzer to analyze the signal in order to determine if the data includes patterns uniquely indicative of at least one of tension headaches, migraines, depression, dementia, Alzheimer's disease, epilepsy, Parkinson's disease, autism, cerebral vasospasm and meningitis.

Abstract: The present specification describes a system for diagnosing or screening one or more pathologies in a patient. The system includes a headset with at least one microphone or accelerometer to passively receive vibrations generated by the cerebral vasculature of the patient's brain, computing devices coupled with the headset for processing the received vibrations to obtain a unique signal, and a signal analyzer to analyze the signal in order to determine if the data includes patterns uniquely indicative of at least one of tension headaches, migraines, depression, dementia, Alzheimer's disease, epilepsy, Parkinson's disease, autism, cerebral vasospasm and meningitis.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: The present specification describes a system for diagnosing or screening one or more pathologies in a patient. The system includes a headset with at least one microphone or accelerometer to passively receive vibrations generated by the cerebral vasculature of the patient's brain, computing devices coupled with the headset for processing the received vibrations to obtain a unique signal, and a signal analyzer to analyze the signal in order to determine if the data includes patterns uniquely indicative of at least one of tension headaches, migraines, depression, dementia, Alzheimer's disease, epilepsy, Parkinson's disease, autism, cerebral vasospasm and meningitis.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Abstract: Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.