We use the expectation operator in different contexts in the proof. Here, we show the full derivation of the lower bound for negative mutual-information. We derive the lower bound for the general case where there are both observed and unobserved confounders. The VDE optimization involves the expectations of distributions with parameters with respect to a distribution that also has parameters. In our experiments, we let the control function be a categorical variable.

For the past 2 years, the world has been in an era of pandemics because of Covid-19. Although now conditions have become better, still new variants of Covid are rising. Doctors are working 24x7 to tackle this problem. Besides Covid, there are other diseases for which doctors are needed, but can there be a solution, which can make the task of doctors easy? Yes, there is Artificial Intelligence.

Artificial intelligence (AI) is transforming industries across the globe at a rapid pace. The contingencies like the COVID crisis act as a trigger to the technological adoption in healthcare and AI comes out as a boon to the industry. AI-ML-powered telemedicine is the buzzword today. AI-based telehealth services are connecting healthcare providers to their clients, doctors to patients,bringing all the stakeholders within a loop. Let's see how AI in healthcare is transforming the industry with massive digital capabilities the world over.

The impact of deploying Artificial Intelligence (AI) for radiation cancer therapy in a real-world clinical setting has been tested by Princess Margaret researchers in a unique study involving physicians and their patients. A team of researchers directly compared physician evaluations of radiation treatments generated by an AI machine learning (ML) algorithm to conventional radiation treatments generated by humans. They found that in the majority of the 100 patients studied, treatments generated using ML were deemed to be clinically acceptable for patient treatments by physicians. Overall, 89% of ML-generated treatments were considered clinically acceptable for treatments, and 72% were selected over human-generated treatments in head-to-head comparisons to conventional human-generated treatments. Moreover, the ML radiation treatment process was faster than the conventional human-driven process by 60%, reducing the overall time from 118 hours to 47 hours.

Clinical decision making about treatments and interventions based on personal characteristics leads to effective health improvement. Machine learning (ML) has been the central concern of the diagnosis support and disease prediction based on comprehensive patient information. Because the black-box problem in ML is serious for medical applications, explainable artificial intelligence (XAI) techniques to explain the reasons for ML models predictions have been focused. A remaining important issue in clinical situations is discovery of concrete and realistic treatment processes. This paper proposes an innovative framework to plan concrete treatment processes based on an ML model. A key point of our proposed framework is to evaluate an "actionability" of the treatment process using a stochastic surrogate model constructed through hierarchical Bayesian modeling. The actionability is an essential concept for suggesting a realistic treatment process, which leads to clinical applications for personal health improvement. This paper also presents two experiments to evaluate our framework. We first demonstrate the feasibility of our framework from the viewpoint of the methodology using a synthetic dataset. Subsequently, our framework is applied to an actual health checkup dataset, which comprises 3,132 participants, considering an application to improve systolic blood pressure values at a personal level. We confirmed that the computed treatment processes are actionable and consistent with clinical knowledge for lowering blood pressure. These results demonstrate that our framework can contribute to decision making in the medical field. Our framework can be expected to provide clinicians deeper insights by proposing concrete and actionable treatment process based on the ML model.

Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inputs. The missing inputs can happen in two cases. First, the historical data for training BO often contain missing values. Second, when performing the function evaluation (e.g. computing alloy strength in a heat treatment process), errors may occur (e.g. a thermostat stops working) leading to an erroneous situation where the function is computed at a random unknown value instead of the suggested value. To deal with this problem, a common approach just simply skips data points where missing values happen. Clearly, this naive method cannot utilize data efficiently and often leads to poor performance. In this paper, we propose a novel BO method to handle missing inputs. We first find a probability distribution of each missing value so that we can impute the missing value by drawing a sample from its distribution. We then develop a new acquisition function based on the well-known Upper Confidence Bound (UCB) acquisition function, which considers the uncertainty of imputed values when suggesting the next point for function evaluation. We conduct comprehensive experiments on both synthetic and real-world applications to show the usefulness of our method.

Water management issues are at the center of environmental debates taking place across the globe. Irrational distribution, leakages, contamination, and overuse of groundwater are some of the biggest challenges associated with the water management industry. Today, industry leaders are exploring AI development services for water management systems to mitigate the water crisis using AI and IoT devices. Together, these technologies provide effective mechanisms to monitor water quality, detect leakages, analyze demand, and streamline global water management. This blog post explores and highlights some AI use cases for the diverse water industry. Today, the availability of real-time data generated by IoT devices is encouraging global leaders to invest in AI development services for water management and monitoring systems.