AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Symptom Assessment

Showing 31 to 40 of 45 articles

Clear Filters

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We...

Mining Disease-Symptom Relation from Massive Biomedical Literature and Its Application in Severe Disease Diagnosis.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Disease-symptom relation is an important biomedical relation that can be used for clinical decision support including building medical diagnostic systems. Here we present a study on mining disease-symptom relation from massive biomedical literature a...

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.

Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP)
BACKGROUND: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification.

Assessing ADHD symptoms in children and adults: evaluating the role of objective measures.

Behavioral and brain functions : BBF
BACKGROUND: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Fu...

A systematic literature review and classification of knowledge discovery in traditional medicine.

Computer methods and programs in biomedicine
INTRODUCTION AND OBJECTIVE: Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of th...

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information doc...

Outcome measurement in mental health services: insights from symptom networks.

BMC psychiatry
BACKGROUND: In mental health, outcomes are currently measured by changes of individual scores. However, such an analysis on individual scores does not take into account the interaction between symptoms, which could yield crucial information while inv...

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers.

Scientific reports
The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is...

Machine Learning Diagnosis of Peritonsillar Abscess.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to ma...