Historical records suggest that, thousands of years ago, many societies carried out ritual homicides to appease their gods. What historians still don't understand, however, is why. "How could something so costly and devastating have been so common in early human societies?" asks Joseph Watts, a doctoral student studying cultural evolution at the University of Auckland in New Zealand. Some researchers have hypothesized that ritual killings helped reinforce social hierarchies, contributing to the evolution of social classes that still exist today. A new study digs into this hypothesis to see if there is a link between the stratification of a society and whether such killings were part of its history.
Healthcare professionals …. please help fuel the research. I know you have a lot to share about these new developments (artificial intelligence) and hence will request your comments. A few years back, I served as a board member of a newly formed accountable care entity. This multi-hospital, multi-county accountable care entity spent significant time and effort to develop a risk stratification and care coordination model. The goal was that the model will not only provide efficient and effective care but will also be based upon evidence based medicine.
The Palmerston North-based Health Hub Project in New Zealand is aiming to reduce health inequities and increase access to care with the help of artificial intelligence, machine learning and blockchain. Project co-founder David Hill is a GP at the Health Hub Project in Palmerston North, which runs four general practices with around 9000 patients. Hill says clinically trained people are a diminishing resource in healthcare and the system cannot rely on that to ensure its sustainability in the future, therefore technology needs to be used to "balance that inequity of supply and demand". "The whole point of what we are doing is trying to make sure that we use IT in a way that allows or permits greater equity of access to patients and starts to reduce the reliance on the ever-dwindling resource of healthcare workers," he says. "Also, to advance the value proposition that we give to patients."
In this paper, we apply a multiple instance learning paradigm to signal-based risk stratification for cardiovascular outcomes. In contrast to methods that require handcrafted features or domain knowledge, our method learns a representation with state-of-the-art predictive power from the raw ECG signal. We accomplish this by leveraging the multiple instance learning framework. This framework is particularly valuable to learning from biometric signals, where patient-level labels are available but signal segments are rarely annotated. We make two contributions in this paper: 1) reframing risk stratification for cardiovascular death (CVD) as a multiple instance learning problem, and 2) using this framework to design a new risk score, for which patients in the highest quartile are 15.9 times more likely to die of CVD within 90 days of hospital admission for an acute coronary syndrome.
In the recent years, we have witnessed the development of multi-label classification methods which utilize the structure of the label space in a divide and conquer approach to improve classification performance and allow large data sets to be classified efficiently. Yet most of the available data sets have been provided in train/test splits that did not account for maintaining a distribution of higher-order relationships between labels among splits or folds. We present a new approach to stratifying multi-label data for classification purposes based on the iterative stratification approach proposed by Sechidis et. al. in an ECML PKDD 2011 paper. Our method extends the iterative approach to take into account second-order relationships between labels. Obtained results are evaluated using statistical properties of obtained strata as presented by Sechidis. We also propose new statistical measures relevant to second-order quality: label pairs distribution, the percentage of label pairs without positive evidence in folds and label pair - fold pairs that have no positive evidence for the label pair. We verify the impact of new methods on classification performance of Binary Relevance, Label Powerset and a fast greedy community detection based label space partitioning classifier. Random Forests serve as base classifiers. We check the variation of the number of communities obtained per fold, and the stability of their modularity score. Second-Order Iterative Stratification is compared to standard k-fold, label set, and iterative stratification. The proposed approach lowers the variance of classification quality, improves label pair oriented measures and example distribution while maintaining a competitive quality in label-oriented measures. We also witness an increase in stability of network characteristics.