Competition Overview - Innovation Funding Service

#artificialintelligence

This is a Small Business Research Initiative (SBRI) competition funded by Opportunity North East and NHS Scotland. Successful applicants will receive 100% funding and have access to advice from NHS Grampian, NHS Greater Glasgow and Clyde (NHSGGC), the University of Aberdeen, the Canon Medical Research Europe and the funders. The overall programme will be delivered in 2 phases. A decision to proceed with phase 2 will depend on the outcomes from phase 1. Only successful applicants from phase 1 will be able to apply to take part in phase 2. NHS Scotland and Opportunity North East (ONE) are investing up to £240,000, including VAT, in innovative data analytics technology.


Integrating Learning from Examples into the Search for Diagnostic Policies

AAAI Conferences

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP).


Integrating Learning from Examples into the Search for Diagnostic Policies

arXiv.org Artificial Intelligence

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on todays desktop computers.


Third 'given wrong initial heart attack diagnosis'

BBC News

Almost a third of patients in England and Wales are being given the wrong initial diagnosis after a heart attack - with women having a far higher chance of being affected, a study suggests. University of Leeds research examined NHS data on about 600,000 heart attack cases over a period of nine years. Women were 50% more likely than men to have an initial diagnosis different from their final diagnosis, it said. NHS England said it was working to improve the diagnosis of heart attacks. The British Heart Foundation is urging people to be aware of the symptoms of a heart attack.


Artificial Intelligence Better Than Doctors At Diagnosing Skin Cancer

#artificialintelligence

Skin cancer was found to be diagnosed more accurately by artificial intelligence than experienced dermatologists in a new international study. Researchers tested a form of machine learning known as a deep learning convolutional neural network (CNN) to reach this conclusion. The study titled "Artificial intelligence for melanoma diagnosis: How can we deliver on the promise?" was published in the cancer journal Annals of Oncology on May 28. Malignant melanoma accounts for 1 percent of all skin cancers but causes a majority of skin cancer-related deaths. The American Cancer Society estimates 9,320 people will die from melanoma in 2018 while 91,270 new cases will be diagnosed.