861 research outputs found
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
With the long-term rapid increase in incidences of colorectal cancer (CRC),
there is an urgent clinical need to improve risk stratification. The
conventional pathology report is usually limited to only a few
histopathological features. However, most of the tumor microenvironments used
to describe patterns of aggressive tumor behavior are ignored. In this work, we
aim to learn histopathological patterns within cancerous tissue regions that
can be used to improve prognostic stratification for colorectal cancer. To do
so, we propose a self-supervised learning method that jointly learns a
representation of tissue regions as well as a metric of the clustering to
obtain their underlying patterns. These histopathological patterns are then
used to represent the interaction between complex tissues and predict clinical
outcomes directly. We furthermore show that the proposed approach can benefit
from linear predictors to avoid overfitting in patient outcomes predictions. To
this end, we introduce a new well-characterized clinicopathological dataset,
including a retrospective collective of 374 patients, with their survival time
and treatment information. Histomorphological clusters obtained by our method
are evaluated by training survival models. The experimental results demonstrate
statistically significant patient stratification, and our approach outperformed
the state-of-the-art deep clustering methods
Improving SIEM for critical SCADA water infrastructures using machine learning
Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset
Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches
Background: Missing data is classified as missing completely at random (MCAR), missing at
random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying
the most appropriate analysis. The first aim was to compare different methods for identifying this
missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate
whether the reminder-response data can be utilised to help identify the missing data mechanism.
Methods: Five clinical trial datasets that employed a reminder system at follow-up were used.
Some quality of life questionnaires were initially missing, but later recovered through reminders.
Four methods of determining the missing data mechanism were applied. Two response data
scenarios were considered. Firstly, immediate data only; secondly, all observed responses
(including reminder-response).
Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption.
Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight
potential MNAR data in two trials.
Conclusion: The four methods were consistent in determining the missingness mechanism. One
hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate.
Research Training Fellowship (CZF/1/31
The Five Factor Model of personality and evaluation of drug consumption risk
The problem of evaluating an individual's risk of drug consumption and misuse
is highly important. An online survey methodology was employed to collect data
including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11),
sensation seeking (ImpSS), and demographic information. The data set contained
information on the consumption of 18 central nervous system psychoactive drugs.
Correlation analysis demonstrated the existence of groups of drugs with
strongly correlated consumption patterns. Three correlation pleiades were
identified, named by the central drug in the pleiade: ecstasy, heroin, and
benzodiazepines pleiades. An exhaustive search was performed to select the most
effective subset of input features and data mining methods to classify users
and non-users for each drug and pleiad. A number of classification methods were
employed (decision tree, random forest, -nearest neighbors, linear
discriminant analysis, Gaussian mixture, probability density function
estimation, logistic regression and na{\"i}ve Bayes) and the most effective
classifier was selected for each drug. The quality of classification was
surprisingly high with sensitivity and specificity (evaluated by leave-one-out
cross-validation) being greater than 70\% for almost all classification tasks.
The best results with sensitivity and specificity being greater than 75\% were
achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance
abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure
Association of changes in the use of board-certified critical care intensivists with mortality outcomes for trauma patients at a well-established level I urban trauma center
Severe Respiratory Syncytial Virus Bronchiolitis in Infants Is Associated with Reduced Airway Interferon Gamma and Substance P
Severe human respiratory syncytial virus (hRSV) bronchiolitis in previously well infants may be due to differences in the innate immune response to hRSV infection. Aim: to determine if factors mediating proposed mechanisms for severe bronchiolitis differ with severity of disease
SMEs' Confidentiality Concerns for Security Information Sharing
Small and medium-sized enterprises are considered an essential part of the EU
economy, however, highly vulnerable to cyberattacks. SMEs have specific
characteristics which separate them from large companies and influence their
adoption of good cybersecurity practices. To mitigate the SMEs' cybersecurity
adoption issues and raise their awareness of cyber threats, we have designed a
self-paced security assessment and capability improvement method, CYSEC. CYSEC
is a security awareness and training method that utilises self-reporting
questionnaires to collect companies' information about cybersecurity awareness,
practices, and vulnerabilities to generate automated recommendations for
counselling. However, confidentiality concerns about cybersecurity information
have an impact on companies' willingness to share their information. Security
information sharing decreases the risk of incidents and increases users'
self-efficacy in security awareness programs. This paper presents the results
of semi-structured interviews with seven chief information security officers of
SMEs to evaluate the impact of online consent communication on motivation for
information sharing. The results were analysed in respect of the Self
Determination Theory. The findings demonstrate that online consent with
multiple options for indicating a suitable level of agreement improved
motivation for information sharing. This allows many SMEs to participate in
security information sharing activities and supports security experts to have a
better overview of common vulnerabilities. The final publication is available
at Springer via https://doi.org/10.1007/978-3-030-57404-8_22Comment: 10 pages, 2 figures, 14th International Symposium on Human Aspects of
Information Security & Assurance (HAISA 2020
Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh
<p>Abstract</p> <p>Background</p> <p>The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.</p> <p>Methods</p> <p>Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption.</p> <p>Results</p> <p>All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models.</p> <p>Conclusion</p> <p>These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.</p
- …