In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes. Particularly in the medical domain, a specific list of symptoms can be labeled as one of various different conditions. Some of these conditions may be more prevalent than others by several orders of magnitude. Here we present a novel unsupervised Domain Adaptation scheme for such datasets. The scheme, based on a specific type of Quantification, is designed to work under both label and conditional shifts. It is demonstrated on datasets generated from Electronic Health Records and provides high quality results for both Quantification and Domain Adaptation in very challenging scenarios. Potential benefits of using this scheme in the current COVID-19 outbreak, for estimation of prevalence and probability of infection, are discussed.

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance degradation. To address this issue, we propose a novel measure to assess the local label imbalance of multi-label datasets, as well as two multi-label sampling approaches based on the local label imbalance, namely MLSOL and MLUL. By considering all informative labels, MLSOL creates more diverse and better labeled synthetic instances for difficult examples, while MLUL eliminates instances that are harmful to their local region. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

Johann Gagnon-Bartsch, Yotam Shem-Tov.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1464--1483.

Abstract:
The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not control the assignment mechanism and instead may rely upon natural experiments or matching methods as a substitute to experimental randomization. The standard testable implication of random assignment is covariate balance between the treated and control units. Covariate balance is commonly used to validate the claim of as good as random assignment. We propose a new nonparametric test of covariate balance. Our Classification Permutation Test (CPT) is based on a combination of classification methods (e.g., random forests) with Fisherian permutation inference. We revisit four real data examples and present Monte Carlo power simulations to demonstrate the applicability of the CPT relative to other nonparametric tests of equality of multivariate distributions.

Funeral parlors' enterprising staff offered burial packages to suit every social strata and budget

A $28.8 million spending bill nearing legislative approval would allocate funding to combat lead in Michigan drinking water systems and investigate sexual assaults by clergy.

"Park ambassadors" will soon be on patrol at some of Ottawa's busiest public green spaces to help confused residents navigate the newly loosened COVID-19 restrictions, Mayor Jim Watson announced Friday.

1. Coronavirus Outbreak Updates: Maharashtra reports 1,089 new COVID-19 cases across state, 748 in Mumbai; total infections at 19,063  Firstpost
2. Migrant workers | Maharashtra train accident victims were battling hunger  The Hindu
3. Maharashtra: They thought no trains were running, loco driver spotted them too late to stop  The Indian Express
4. ‘Stay where you are’: Shivraj Chouhan appeals to migrants after train tragedy  Hindustan Times
5. MP Ministers Ask Shivraj to Probe Tenure of Kamal Nath to ‘Expose Massive Corruption’  News18
6. View Full coverage on Google News

1. ‘Army won’t be deployed in Mumbai, will fight Covid-19 together’: Uddhav Thackeray  Hindustan Times
2. Maharashtra may extend lockdown to end of May, hints CM Uddhav Thackeray  Times of India
3. Maharashtra CM Uddhav Thackeray announces compensation for kin of deceased  TIMES NOW
4. Restrict entry-exit of migrants in Maharashtra: Raj Thackeray  Deccan Chronicle
5. Uddhav Thackeray: You are the soldiers, no need for the Army  Mumbai Mirror
6. View Full coverage on Google News

FIR against 15 members for song ’n dance event  Mumbai Mirror

An OM team has a rare opportunity to share the Gospel with the Doma people at the funeral of a senior member of the community.