Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.
Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.
The field of medical coding enables to assign codes of medical classifications such as the international classification of diseases (ICD) to clinical notes, which are medical reports about patients' conditions written by healthcare professionals in natural language. These texts potentially include medical terms that define diagnosis, symptoms, drugs, treatments, etc., and the use of spontaneous language is challenging for automatic processing. Medical coding is usually performed manually by human medical coders becoming time-consuming and prone to errors. This research aims at developing new approaches that combine deep learning elements together with traditional technologies. A semantic-based proposal supported by a proprietary knowledge graph (KG), neural network implementations, and an ensemble model to resolve the medical coding are presented. A comparative discussion between the proposals where the advantages and disadvantages of each one is analysed. To evaluate approaches, two main corpus have been used: MIMIC-III and private de-identified clinical notes.
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