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ML strategies employ analytical algorithms to extract unique patient features, including all of the data obtained during a patient visit with a practitioner.
FREMONT, CA: Artificial Intelligence (AI) must effectively sort through the presented data to ‘learn’ and create a network with various healthcare data out in the field. Two types of data can be sorted in the realm of healthcare data: unstructured and structured. Machine Learning (ML) techniques, a neural network system, and modern deep learning are three distinct forms of hierarchical learning techniques. Natural Language Processing (NLP) is used with all unstructured data.
ML strategies employ analytical algorithms to extract unique patient features, including all of the data obtained during a patient visit with a practitioner. Physical exam outcomes, drugs, symptoms, basic measurements, disease-specific data, medical imaging, gene expressions, and various laboratory tests all add to the standardized data obtained. Patient results can then be predicted using machine learning. In one study, Neural Networking was used to sort 6,567 genes and pair them with texture information from the subjects' mammograms in a breast cancer diagnostic process. This combination of logged genetic and physical characteristics resulted in a more specific tumor indicator.
Supervised learning is the most prevalent form of ML used in clinical settings. Supervised learning uses the patient's physical characteristics combined with a database of information (in this case, breast cancer genes) to have a more focused result. Modern Deep Learning, which is considered to go beyond the surface of ML, is another method of learning that is used. Deep Learning employs the same inputs as ML but feeds them into a computerized neural network, which then files the data into a more simplified output. This helps clinicians narrow down many alternative diagnoses to one or two outcomes, helping them reach a more conclusive and realistic conclusion.
NLP is similar to structured data processing, but it focuses on all unstructured data in a clinical environment. When a doctor visits a patient, this type of data comes from clinical reports and documented speech to text processing. This data contains physical examination narratives, laboratory reports, and exam summaries. NLP allows the use of historical databases of disease-related keywords to help in the diagnosis decision-making process. These approaches will provide a patient with a more precise and effective diagnosis, saving time for the doctor and, more importantly, speeding up the care process. The sooner a patient can be on the path to recovery, the quicker, more targeted, and specific the diagnosis.