Decision Support Systems


Cera is building an AI for social care decision support

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That's the pitch underpinning UK home care provider Cera's plans. These human workers will be tasked with creating the data points to fill out the care records that will be used to power the chatbot's future care recommendations and alerts. The startup, which bills itself as a "tech-enabled home care provider" launched its social care matching platform last November, and has raised some $3.4 million to date from investors including Kima Ventures and Credo Ventures. It has "hundreds" of care workers on its platform at this point, according to Maruthappu, and has delivered tens of thousands of care hours -- "accruing millions of data points", as it couches it.


Cera is building an AI for social care decision support

#artificialintelligence

That's the pitch underpinning UK home care provider Cera's plans. These human workers will be tasked with creating the data points to fill out the care records that will be used to power the chatbot's future care recommendations and alerts. The startup, which bills itself as a "tech-enabled home care provider" launched its social care matching platform last November, and has raised some $3.4 million to date from investors including Kima Ventures and Credo Ventures. It has "hundreds" of care workers on its platform at this point, according to Maruthappu, and has delivered tens of thousands of care hours -- "accruing millions of data points", as it couches it.


Combinostics uses AI to improve early diagnosis

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New Finnish technology is tapping into machine learning and artificial intelligence to support healthcare professionals in early diagnosis of neurological disorders. Thurfjell emphasises that Combinostics is not trying to replace doctors, but rather offer them intelligent tools to support accurate early diagnosis. Combinostics' technology is built on years of research at the VTT Technical Research Centre of Finland studying AI and machine learning for diagnostics. Combinostics' brain image assessment tool is currently used by a handful of university hospitals and radiology service providers in Finland and Sweden, and it will be soon complemented with a clinical decision support tool to provide full patient data analysis.


Quantitative Imaging Market to Exceed $500M in 2021 - Signify Research

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Quantitative imaging tools have been available for many years and are typically sold as applications for advanced visualisation platforms. This relatively new class of products, called Decision Support Tools, is forecast to take-off in the coming years, with the first products now entering the market. Several companies are developing Computer-aided Diagnosis (CADx) systems that provide the functionality of Decision Support Tools and provide interpretation of medical images, for example, a probability score for the presence of cancer. The introduction of CADx systems with broad diagnostic capabilities, at an affordable price point, will be the trigger for more widespread uptake of CADx systems, but this is likely to be several years away.


Navigation Guidance for Intensive-Care Doctors

AITopics Original Links

To change this, Boston-area startup Etiometry is building a clinical-decision support system that can interpret large volumes of real-time patient data and provide doctors with a snapshot view of actionable information. Clinical-decision support tools guide diagnoses and treatments by plugging patient data into predictive models that have been built on prior patient outcomes. Another challenge has been the predominance of paper-based data storage, which has limited the amount of data available to researchers trying to use machine learning to build better models of patient care. The team says their framework can interpret all patient data generated in an ICU--from instantaneous data, such as heart rate, to data collected over multiple hours, such as blood work.


Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies (PDF Download Available)

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Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social insects - swarm intelligence - resides not in complex individual abilities but rather in networks of interactions that exist among individuals and between individuals and their environment. In the present work we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we present a novel strategy (ACLUSTER) to tackle unsupervised data exploratory analysis as well as data retrieval problems. Moreover and according to our knowledge, this is also the first application of ant systems into digital image retrieval problems.


MediaGamma Launches Next Generation Artificial Intelligence Product Set to Reshape the Ad Tech Market

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By applying deep learning to unique data sets, coupled with MediaGamma's unique AI Decision Support Engine, the product is set to provide players in the ecosystem with over 90% certainty about a user's interests and demographic profile. The new product will help people to navigate uncertainty to make better decisions, and a major telecoms company has already signed up. By analysing relationships and signals that are difficult to pick up in datasets, our AI Decision Support Engine can make digital businesses more efficient, more relevant and more profitable." The MediaGamma probabilistic Decision Support Engine was developed for the edge-case of real time web scale advertising, but the engineering required to make that work also makes it applicable to many other business applications - the deal with a major telecoms company underlines this.


MediaGamma Launches Next Generation Artificial Intelligence Product Set to Reshape the Ad Tech Market

#artificialintelligence

By applying deep learning to unique data sets, coupled with MediaGamma's unique AI Decision Support Engine, the product is set to provide players in the ecosystem with over 90% certainty about a user's interests and demographic profile. The new product will help people to navigate uncertainty to make better decisions, and a major telecoms company has already signed up. By analysing relationships and signals that are difficult to pick up in datasets, our AI Decision Support Engine can make digital businesses more efficient, more relevant and more profitable." The MediaGamma probabilistic Decision Support Engine was developed for the edge-case of real time web scale advertising, but the engineering required to make that work also makes it applicable to many other business applications – the deal with a major telecoms company underlines this.


CIOReview Names MedyMatch in 100 Most Promising Big Data Solutions 2016

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"It is an honor to be recognized by CIOReview for MedyMatch's achievements in cognitive analytics, artificial intelligence and medical imaging," said Robert Mehler, coFounder & COO. MedyMatch addresses the needs of healthcare providers and patients by utilizing deep vision, advanced cognitive analytics, and artificial intelligence to deliver real time decision support tools to improve clinical outcomes in acute medical scenarios. MedyMatch utilizes advanced cognitive analytics and artificial intelligence to deliver real-time decision support tools to improve clinical outcomes in acute medical scenarios. The MedyMatch team of artificial intelligence, machine learning, deep learning and algorithmic experts along with its medical and science advisory boards are achieving breakthroughs in standards of cost and care.


A General Context-Aware Framework for Improved Human-System Interactions

AI Magazine

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool.