Accurately predicting patients' risk of 30-day hospital readmission would enable hospitals to efficiently allocate resource-intensive interventions. We develop a new method, Categorical Co-Frequency Analysis (CoFA), for clustering diagnosis codes from the International Classification of Diseases (ICD) according to the similarity in relationships between covariates and readmission risk. CoFA measures the similarity between diagnoses by the frequency with which two diagnoses are split in the same direction versus split apart in random forests to predict readmission risk. Applying CoFA to de-identified data from Berkshire Medical Center, we identified three groups of diagnoses that vary in readmission risk. To evaluate CoFA, we compared readmission risk models using ICD majors and CoFA groups to a baseline model without diagnosis variables. We found substituting ICD majors for the CoFA-identified clusters simplified the model without compromising the accuracy of predictions. Fitting separate models for each ICD major and CoFA group did not improve predictions, suggesting that readmission risk may be more homogeneous that heterogeneous across diagnosis groups.

Progress in ML for health care to date has been limited by the lack of well-defined questions and a dearth of annotated datasets. Many ML researchers remain focused on questions for which annotations are readily available, without necessarily questioning the clinical relevance of the problems and their solutions. For example, a popular benchmark challenge in the community focuses on predicting in-hospital mortality on the basis of data collected during the first 48 hours after admission to the intensive care unit4. Clearly annotated data are publicly available, and in recent years, performance on this task has approached an area under the curve (Box 1) of 0.9 (ref. However, assessing clinical utility requires careful evaluation against the scenario in which the model will be used.

Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases, the purpose of the risk assessment tool is to inform actions, such as medical treatments or release conditions, often with the aim of reducing the likelihood of an adverse event such as hospital readmission or recidivism. Problematically, most tools are trained and evaluated on historical data in which the outcomes observed depend on the historical decision-making policy. These tools thus reflect risk under the historical policy, rather than under the different decision options that the tool is intended to inform. Even when tools are constructed to predict risk under a specific decision, they are often improperly evaluated as predictors of the target outcome. Focusing on the evaluation task, in this paper we define counterfactual analogues of common predictive performance and algorithmic fairness metrics that we argue are better suited for the decision-making context. We introduce a new method for estimating the proposed metrics using doubly robust estimation. We provide theoretical results that show that only under strong conditions can fairness according to the standard metric and the counterfactual metric simultaneously hold. Consequently, fairness-promoting methods that target parity in a standard fairness metric may --- and as we show empirically, do --- induce greater imbalance in the counterfactual analogue. We provide empirical comparisons on both synthetic data and a real world child welfare dataset to demonstrate how the proposed method improves upon standard practice.

Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, this paper proposed a denoising autoencoder neural network (DAE) algorithm which can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Through experiments, compared with the denoising autoencoder neural network (DAE) with oversampling process and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.

Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much behavior a process model allows for that was never observed in the event log. Generalization is concerned with quantifying how well a process model generalizes to behavior that is possible in the business process but was never observed in the event log. Many recall, precision, and generalization measures have been developed throughout the years, but they are often defined in an ad-hoc manner without formally defining the desired properties up front. To address these problems, we formulate 21 conformance propositions and we use these propositions to evaluate current and existing conformance measures. The goal is to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures). Additionally, this paper serves as an overview of the conformance checking measures that are available in the process mining area.

By routing your camera feeds to our AI Engine, you can be informed in just 3 seconds when a firearm is detected in surveillance cameras. Additionally, this AI technology can track shooters in real time, providing shooter location(s) and fast live updates to police, school security and educators. In the wake of school shooting incidents over the past 10 years, people are anxious about creating safe environments. The ability to detect weapons on premises is unfortunately a necessity now. Cameras are already in place at most schools.

Automatic speech recognition systems like those at the core of Alexa convert speech into text, and one of their components is a model that predicts which word will come after a sequence of words. They're typically n-gram based, meaning they suss out the probability of next words given the past n-1 words. But architectures like recurrent neural networks, which are commonly used in speech recognition because of their ability to learn long-range dependencies, are tough to incorporate into real-time systems and often struggle to ingest data from multiple corpora. That's why researchers at Amazon's Alexa research division investigated techniques to make such AI models more practical for speech recognition. In a blog post and accompanying paper ("Scalable Multi Corpora Neural Language Models for ASR") scheduled to be presented at the upcoming Interspeech 2019 conference in Graz, Austria, they claim they can reduce word recognition error rate by 6.2% over the baseline.

It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition. Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

An e xtreme wind speed estimation method that consider s wind hazard climate type s is critical for design wind load calculation for building structure s affected by mixed climate s . However, it is very difficult to obtain wind hazard climate type s from meteorologi cal data records, because they restrict the application of extreme wind speed estimation in mixed climates . This paper first proposes a wind hazard type identification algorithm based on a numerical pattern recognition method that utilizes feature extraction and generalization . Next, it compares six commonly used machine learning models using K - fold cross - validation. Finally, it takes meteorological data from three locations near the southeast coast of China as example s to examine t he algor ithm's performance . Based on classification results, the extreme wind speed s calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discus sed . Extreme wind speed; Mixed climates; Data - driven method; Pattern Recognition; Machine Learning; 1. Introduction Wind effects are key factors in structural design, and extreme wind speeds are the starting point . F or flexible structures such as long - span bridges, long - span roofs and high - rise buildings, wind loads are normally the predominant loads. I n order to meet both the ultimate safety and performance requirements of wind - resistant structural design, it s necessary to accurately estimate the extreme wind speed s for different recurrence period s . For significant buildings and infrastructures, it is necessary to estimat e the extreme wind speed through probabilistic methods from local wind speed record s .

This week's opinion piece is from technology influencer and futurist Dr Sarah-Jayne Gratton The latest statistics around breast cancer send a stark reminder of just how important early detection is in combating this brutal disease. With revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. One of the leading causes of death for cancer patients is a late diagnosis, too often brought about by inferior testing facilities, human factors, such as fatigue and loss of concentration, or by the patients themselves, who put off seeing a specialist due to the fear of what they might discover. But now, thanks to nothing short of revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. AI is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation.