The coding of medical diagnosis and treatment has always been a challenging issue. Translating a patient's complex symptoms, and a clinician's efforts to address them, into a clear and unambiguous classification code was difficult even in simpler times. Now, however, hospitals and health insurance companies want very detailed information on what was wrong with a patient and the steps taken to treat them-- for clinical record-keeping, for hospital operations review and planning, and perhaps most importantly, for financial reimbursement purposes. The current international standard for medical coding is ICD-10 (the tenth version of International Classification of Disease codes), from the World Health Organization (WHO). ICD‑10 has over 14,000 codes for diagnoses.

With the lag of tech in healthcare, will AI/ML improve patient care or remain a smart idea? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare. Applications have the potential to streamline workflows and reduce human errors, speeding drug discovery, assisting surgery, and provisioning better billing and coding methods. But, in an industry that typically lags in digital maturity by as much as 10 years, according to a 2017 study, is AI in healthcare an empty promise or truly a forward-thinking and innovative reality? Technology experts have promised artificial intelligence (AI) and machine learning (ML) will revolutionize healthcare.

RENO, Nev., Oct. 04, 2018 (GLOBE NEWSWIRE) -- via OTC PR WIRE -- China Food and Beverage Company (OTC:CHIF), announced today that its healthcare division in cooperation with Shanghai based, American Medical Care and Insurance (AMC Shanghai) has launched an Artificial Intelligence (AI) software program based on a Traditional Chinese Medicine Clinical Decision Support System (TCM CDSS). The system is an AI assisted diagnosis and treatment cloud platform. In addition, the system integrates various functions such as centralized medical research, analysis and statistics, quantitative calculation, chart analysis, intelligent auxiliary diagnosis, medical record interpretation, and prescription matching. The TCM CDSS Cloud Platform is part of the Company's continued commitment to transform healthcare in China and around the globe. Among the goals of the platform are to keep patients out of hospitals, early detection of life threatening diseases and major clinical research studies of diseases such as diabetes and cardio vascular disease, both of which have reached the epidemic stage in the world's largest country.

The international health community has decided that if you play video games like Fortnite or World of Warcraft a lot, you might suffer from a mental-health issue: Gaming Disorder. It's a behavioral condition that the World Health Organization has added to the proposed 11th revision of its International Statistical Classification of Diseases and Related Health Problems, or ICD-11, the first update to the classification since 1992. If you play a lot of chess or Settlers of Catan on a card table in the den, don't worry, you're fine--according to WHO, gaming disorder is a digital affliction. If you play obsessively online with other people, to the detriment of other activities, that's one possible sign of trouble. But playing offline and alone--Candy Crush, say, or even Tetris--is also a potential red flag.

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.

While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11.