605 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
Factors affecting post-fire crown regeneration in cork oak (Quercus suber L.) trees
Cork oak (Quercus suber) forests are acknowledged
for their biodiversity and economic (mainly cork
production) values. WildWres are one of the main threats
contributing to cork oak decline in the Mediterranean
Basin, and one major question that managers face after Wre
in cork oak stands is whether the burned trees should be
coppiced or not. This decision can be based on the degree
of expected crown regeneration assessed immediately after
Wre. In this study we carried out a post-Wre assessment of
the degree of crown recovery in 858 trees being exploited
for cork production in southern Portugal, 1.5 years after a
wildWre. Using logistic regression, we modelled good or
poor crown recovery probability as a function of tree and
stand variables. The main variables inXuencing the likelihood
of good or poor crown regeneration were bark thickness,
charring height, aspect and tree diameter. We also
developed management models, including simpler but easier
to measure variables, which had a lower predictive
power but can be used to help managers to identify, immediately
after Wre, trees that will likely show good crown
regeneration, and trees that will likely die or show poor
regeneration (and thus, potential candidates for trunk
coppicin
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
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
A prognostic tool to identify adolescents at high risk of becoming daily smokers
<p>Abstract</p> <p>Background</p> <p>The American Academy of Pediatrics advocates that pediatricians should be involved in tobacco counseling and has developed guidelines for counseling. We present a prognostic tool for use by health care practitioners in both clinical and non-clinical settings, to identify adolescents at risk of becoming daily smokers.</p> <p>Methods</p> <p>Data were drawn from the Nicotine Dependence in Teens (NDIT) Study, a prospective investigation of 1293 adolescents, initially aged 12-13 years, recruited in 10 secondary schools in Montreal, Canada in 1999. Questionnaires were administered every three months for five years. The prognostic tool was developed using estimated coefficients from multivariable logistic models. Model overfitting was corrected using bootstrap cross-validation. Goodness-of-fit and predictive ability of the models were assessed by R<sup>2</sup>, the c-statistic, and the Hosmer-Lemeshow test.</p> <p>Results</p> <p>The 1-year and 2-year probability of initiating daily smoking was a joint function of seven individual characteristics: age; ever smoked; ever felt like you needed a cigarette; parent(s) smoke; sibling(s) smoke; friend(s) smoke; and ever drank alcohol. The models were characterized by reasonably good fit and predictive ability. They were transformed into user-friendly tables such that the risk of daily smoking can be easily computed by summing points for responses to each item. The prognostic tool is also available on-line at <url>http://episerve.chumontreal.qc.ca/calculation_risk/daily-risk/daily_smokingadd.php</url>.</p> <p>Conclusions</p> <p>The prognostic tool to identify youth at high risk of daily smoking may eventually be an important component of a comprehensive tobacco control system.</p
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