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Antimicrobial Stewardship & Healthcare... 2023To describe a novel attribution metric estimating the causal source location of healthcare-associated and compare it with the current US National Healthcare Safety...
OBJECTIVE
To describe a novel attribution metric estimating the causal source location of healthcare-associated and compare it with the current US National Healthcare Safety Network (NHSN) surveillance reporting standard.
DESIGN
Quality improvement study.
SETTING
Two acute care facilities.
METHODS
A novel attribution metric assigned days of attribution to locations where patients were located for 14 days before and the day of their diagnosis. We correlated the NHSN-assigned unit attribution with the novel attribution measure and compared the proportion of attribution assigned to inpatient units.
RESULTS
During a 30-month period, there were 727 NHSN healthcare-associated infections (HAIs) and 409 non-HAIs; the novel metric attributed 17,034 days. The correlation coefficients for NHSN and novel attributions among non-ICU units were 0.79 (95% CI, 0.76-0.82) and 0.74 (95% CI, 0.70-0.78) and among ICU units were 0.70 (95% CI, 0.63-0.76) and 0.69 (95% CI, 0.60-0.77) at facilities A and B, respectively. The distribution of difference in percent attribution showed higher inpatient unit attribution using NHSN measure than the novel attribution metric: 38% of ICU units and 15% of non-ICU units in facility A, and 20% of ICU units and 25% of non-ICU units in facility B had a median difference >0; no inpatient units showed a greater attribution using the novel attribution metric.
CONCLUSION
The novel attribution metric shifts attribution from inpatient units to other settings and correlates modestly with NHSN methodology of attribution. If validated, the attribution metric may more accurately target reduction efforts.
PubMed: 38156213
DOI: 10.1017/ash.2023.516 -
Nature Mar 2022Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of...
Ancient history relies on disciplines such as epigraphy-the study of inscribed texts known as inscriptions-for evidence of the thought, language, society and history of past civilizations. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.
Topics: Archaeology; Deep Learning; Greece, Ancient; Handwriting; History, Ancient; Humans; Software; Writing
PubMed: 35264762
DOI: 10.1038/s41586-022-04448-z -
PloS One 2022Extended-spectrum beta-lactamase (ESBL)-producing Escherichia (E.) coli have been widely described as the cause of treatment failures in humans around the world. The...
Extended-spectrum beta-lactamase (ESBL)-producing Escherichia (E.) coli have been widely described as the cause of treatment failures in humans around the world. The origin of human infections with these microorganisms is discussed controversially and in most cases hard to identify. Since they pose a relevant risk to human health, it becomes crucial to understand their sources and the transmission pathways. In this study, we analyzed data from different studies in Germany and grouped ESBL-producing E. coli from different sources and human cases into subtypes based on their phenotypic and genotypic characteristics (ESBL-genotype, E. coli phylogenetic group and phenotypic antimicrobial resistance pattern). Then, a source attribution model was developed in order to attribute the human cases to the considered sources. The sources were from different animal species (cattle, pig, chicken, dog and horse) and also from patients with nosocomial infections. The human isolates were gathered from community cases which showed to be colonized with ESBL-producing E. coli. We used the attribution model first with only the animal sources (Approach A) and then additionally with the nosocomial infections (Approach B). We observed that all sources contributed to the human cases, nevertheless, isolates from nosocomial infections were more related to those from human cases than any of the other sources. We identified subtypes that were only detected in the considered animal species and others that were observed only in the human population. Some subtypes from the human cases could not be allocated to any of the sources from this study and were attributed to an unknown source. Our study emphasizes the importance of human-to-human transmission of ESBL-producing E. coli and the different role that pets, livestock and healthcare facilities may play in the transmission of these resistant bacteria. The developed source attribution model can be further used to monitor future trends. A One Health approach is necessary to develop source attribution models further to integrate also wildlife, environmental as well as food sources in addition to human and animal data.
Topics: Animals; Anti-Bacterial Agents; Cattle; Cross Infection; Dogs; Escherichia coli; Escherichia coli Infections; Germany; Horses; Humans; Phylogeny; Swine; beta-Lactamases
PubMed: 35839265
DOI: 10.1371/journal.pone.0271317 -
Risk Analysis : An Official Publication... Dec 2023Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of...
Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.
Topics: Animals; Humans; Swine; Campylobacter Infections; Bayes Theorem; Chickens; Australia; Multilocus Sequence Typing; Campylobacter; Campylobacter jejuni; Ruminants; Gastroenteritis
PubMed: 37032319
DOI: 10.1111/risa.14138 -
Schizophrenia Research. Cognition Jun 2016Studies on attribution biases in schizophrenia have produced mixed results, whereas such biases have been more consistently reported in people with anxiety disorders....
Studies on attribution biases in schizophrenia have produced mixed results, whereas such biases have been more consistently reported in people with anxiety disorders. Anxiety comorbidities are frequent in schizophrenia, in particular social anxiety disorder, which could influence their patterns of attribution biases. The objective of the present study was thus to determine if individuals with schizophrenia and a comorbid social anxiety disorder (SZ+) show distinct attribution biases as compared with individuals with schizophrenia without social anxiety (SZ-) and healthy controls. Attribution biases were assessed with the Internal, Personal, and Situational Attributions Questionnaire in 41 individual with schizophrenia and 41 healthy controls. Results revealed the lack of the normal externalizing bias in SZ+, whereas SZ- did not significantly differ from healthy controls on this dimension. The personalizing bias was not influenced by social anxiety but was in contrast linked with delusions, with a greater personalizing bias in individuals with current delusions. Future studies on attribution biases in schizophrenia should carefully document symptom presentation, including social anxiety.
PubMed: 28740807
DOI: 10.1016/j.scog.2016.01.001 -
Scientific Reports Feb 2020Research on the attribution of incentive salience to drug cues has furthered our understanding of drug self-administration in animals and addiction in humans. The...
Research on the attribution of incentive salience to drug cues has furthered our understanding of drug self-administration in animals and addiction in humans. The influence of social cues on drug-seeking behavior has garnered attention recently, but few studies have investigated how social cues gain incentive-motivational value. In the present study, a Pavlovian conditioned approach (PCA) procedure was used to identify rats that are more (sign-trackers; STs) or less (goal-trackers; GTs) prone to attribute incentive salience to food reward cues. In Experiment 1, a novel procedure employed social 'peers' to compare the tendency of STs and GTs to attribute incentive salience to social reward cues as well as form a social-conditioned place preference. In Experiment 2, social behavior of STs and GTs was compared using social interaction and choice tests. Finally, in Experiment 3, levels of plasma oxytocin were measured in STs and GTs seven days after the last PCA training session, because oxytocin is known to modulate the mesolimbic reward system and social behavior. Compared to GTs, STs attributed more incentive salience to social-related cues and exhibited prosocial behaviors (e.g., social-conditioned place preference, increased social interaction, and social novelty-seeking). No group differences were observed in plasma oxytocin levels. Taken together, these experiments demonstrate individual variation in the attribution of incentive salience to both food- and social-related cues, which has important implications for the pathophysiology of addiction.
Topics: Animals; Behavior, Addictive; Behavior, Animal; Conditioning, Classical; Food; Male; Motivation; Oxytocin; Rats, Sprague-Dawley; Reward; Social Behavior
PubMed: 32054901
DOI: 10.1038/s41598-020-59378-5 -
PloS One 2016Impaired mental state attribution is a core social cognitive deficit in schizophrenia. With functional magnetic resonance imaging (fMRI), this study examined the extent...
Impaired mental state attribution is a core social cognitive deficit in schizophrenia. With functional magnetic resonance imaging (fMRI), this study examined the extent to which the core neural system of mental state attribution is involved in mental state attribution, focusing on belief attribution and emotion attribution. Fifteen schizophrenia outpatients and 14 healthy controls performed two mental state attribution tasks in the scanner. In a Belief Attribution Task, after reading a short vignette, participants were asked infer either the belief of a character (a false belief condition) or a physical state of an affair (a false photograph condition). In an Emotion Attribution Task, participants were asked either to judge whether character(s) in pictures felt unpleasant, pleasant, or neutral emotion (other condition) or to look at pictures that did not have any human characters (view condition). fMRI data were analyzing focusing on a priori regions of interest (ROIs) of the core neural systems of mental state attribution: the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ) and precuneus. An exploratory whole brain analysis was also performed. Both patients and controls showed greater activation in all four ROIs during the Belief Attribution Task than the Emotion Attribution Task. Patients also showed less activation in the precuneus and left TPJ compared to controls during the Belief Attribution Task. No significant group difference was found during the Emotion Attribution Task in any of ROIs. An exploratory whole brain analysis showed a similar pattern of neural activations. These findings suggest that while schizophrenia patients rely on the same neural network as controls do when attributing beliefs of others, patients did not show reduced activation in the key regions such as the TPJ. Further, this study did not find evidence for aberrant neural activation during emotion attribution or recruitment of compensatory brain regions in schizophrenia.
Topics: Adult; Brain; Case-Control Studies; Culture; Emotions; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Schizophrenia
PubMed: 27812142
DOI: 10.1371/journal.pone.0165546 -
Journal of Epidemiology Aug 2023Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved,...
BACKGROUND
Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging.
METHODS
The authors propose a disease attribution method based on aggregate data or summary statistics of individual-level data, possibly from multiple data sources.
RESULTS
Using the proposed method, the burden of disease is apportioned to the independent and interaction effects of each of its major risk factors and all the other factors as a whole. This scheme guarantees that 100% is the total share of the burden.
CONCLUSION
The calculation is simple and straightforward; therefore, it is recommended for use in studies on disease burden.
Topics: Humans; Disease Attributes; Cost of Illness; Public Health; Japan; Causality
PubMed: 35283399
DOI: 10.2188/jea.JE20210084 -
Journal of Cheminformatics Jan 2023Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a...
BACKGROUND
Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, [Formula: see text], and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES's notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens.
RESULTS
We propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases.
CONCLUSIONS
Data scientists can use XSMILES to understand their models' behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users' needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.
PubMed: 36609340
DOI: 10.1186/s13321-022-00673-w -
Frontiers in Artificial Intelligence 2021Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary...
Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary narratives are highly ambiguous constructs, it is often impossible to decide between diverging attributions of a specific passage by hermeneutic means. Instead, we hypothesise that attribution decisions are often influenced by annotator bias, in particular an annotator's literary preferences and beliefs. We present first results on the correlation between the literary attitudes of an annotator and their attribution choices. In a second set of experiments, we present a neural classifier that is capable of imitating individual annotators as well as a common-sense annotator, and reaches accuracies of up to 88% (which improves the majority baseline by 23%).
PubMed: 35187471
DOI: 10.3389/frai.2021.725321