Decision making for health care professionals: use of decision trees within the community mental health setting

Moving towards more sustainable diets: Is there potential for a personalised approach in practice?

Discourse on the relationship between food production, healthy eating and sustainability has become increasingly prominent and controversial in recent years. Research groups often take one perspective when reporting on sustainable diets, and several often neglect considerations for the multiple aspects that make a diet truly sustainable, such as cultural acceptability, differences in nutritional requirements amongst the population and the efficiency of long-term dietary change. Plant-based diets are associated with lower greenhouse gas emissions (GHGEs) and have been linked with better health outcomes, including lower risk of diet-related chronic disease. However, foods associated with higher GHGE, such as lean red meat, fish and dairy, have beneficial nutritional profiles and contribute significantly to micronutrient intakes. Some research has shown that diets associated with lower GHGE can be less nutritionally adequate. Several countries now include sustainability recommendations in dietary guidelines but use vague language such as "increase" or "consume regularly" when referring to plant-based foods. General population-based nutrition advice has poor adherence and does not consider differences in nutritional needs. Although modelling studies show potential to significantly reduce environmental impact with dietary changes, personalising such dietary recommendations has not been studied. Adapting recommendations to the individual through reproducible methods of personalised nutrition has been shown to lead to more favourable and longer-lasting dietary changes compared to population-based nutrition advice. When considering sustainable healthy dietary guidelines, personalised feedback may increase the acceptability, effectiveness and nutritional adequacy of the diet. A personalised approach has the potential for delivering a new structure of more sustainable healthy food-based dietary guidelines. This review evaluates the potential to develop personalised sustainable healthy food-based dietary guidelines and discusses potential implications for policy and practice.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Role of patient descriptors in predicting antimicrobial resistance in urinary tract infections using a decision tree approach: A retrospective cohort study

BACKGROUND

In general practice, many infections are treated empirically prior to or without microbiological confirmation. Prediction of antimicrobial susceptibility could optimise prescribing thus improving patient outcomes. Decision tree models are a novel idea to predict AMR at the time of clinical presentation. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of pathogens causing urinary tract infections (UTI) for patients over 65 years based on pre-existing routine laboratory data.

METHODS

Data were extracted from the database of the microbiological laboratory of the University Hospitals Galway (UHG). All urine results from patients over 65 years, their microbiological analysis and susceptibility (AST) results from January 2011 to December 2015 were included. The primary endpoint was culture result and resistance to antimicrobials (nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav, and amoxicillin) commonly used to treat UTI. A non-parametric regression tree analysis i.e. a decision tree model was generated with the 75% of the dataset (training set) and validated with the remaining 25% (test set). The model performance was evaluated measuring Area Under the Curve Receiver Operating Characteristic (AUC_ROC) curve.

RESULTS

A total of 99,101 urine samples of patients over 65 years were submitted for culture over the five years and 27% had significant bacteriuria (≥10⁴ cfu/ml) and AST. The most common identified causative organisms were E.coli, Klebsiella spp. and Proteus spp. E.coli was more often resistant to amoxicillin (66%) followed by Proteus spp. (41%). Klebsiella spp. and Proteus spp. were more often resistant to trimethoprim (78% and 54% respectively). E. coli resistance to nitrofurantoin is low (<10%). The decision tree model showed an AUC-ROC score of 0.68 for culture and in between 0.60 to 0.97 for antimicrobial resistance of the pathogens, with the inclusion of patient's descriptors only. Including the uropathogen in the model did not change model performance.

CONCLUSIONS

The decision tree models using patient descriptors available at the time of presentation showed fair to excellent performance in predicting culture and antimicrobial resistance. The presented models provide an alternative approach to decision making on antimicrobial prescribing for UTIs. Increasing more predictors in the model could improve the model performance. Prospective data collection, validation and feasibility testing of the model including data from other laboratories will progress the practical implementation of similar models.

The process and utility of classification and regression tree methodology in nursing research

Aim

This paper presents a discussion of classification and regression tree analysis and its utility in nursing research.

Background

Classification and regression tree analysis is an exploratory research method used to illustrate associations between variables not suited to traditional regression analysis. Complex interactions are demonstrated between covariates and variables of interest in inverted tree diagrams.

Design

Discussion paper.

Data sources

English language literature was sourced from eBooks, Medline Complete and CINAHL Plus databases, Google and Google Scholar, hard copy research texts and retrieved reference lists for terms including classification and regression tree* and derivatives and recursive partitioning from 1984–2013.

Discussion

Classification and regression tree analysis is an important method used to identify previously unknown patterns amongst data. Whilst there are several reasons to embrace this method as a means of exploratory quantitative research, issues regarding quality of data as well as the usefulness and validity of the findings should be considered.

Implications for Nursing Research

Classification and regression tree analysis is a valuable tool to guide nurses to reduce gaps in the application of evidence to practice. With the ever-expanding availability of data, it is important that nurses understand the utility and limitations of the research method.

Conclusion

Classification and regression tree analysis is an easily interpreted method for modelling interactions between health-related variables that would otherwise remain obscured. Knowledge is presented graphically, providing insightful understanding of complex and hierarchical relationships in an accessible and useful way to nursing and other health professions.

Predicting functional outcomes in patients suffering from ischaemic stroke using initial admission variables and physiological data: a comparison between tree model and multivariate regression analysis

PURPOSE

This study was aimed to compare the application of a tree model and regression approach for developing data-driven models that identified frisk factors related to functional outcomes among ischaemic stroke patients.

METHODS

Data were derived from 271 hospitalised patients with a first-ever ischaemic stroke. The Barthel Index (BI) and Modified Rankin Scale (MRS) were used to assess the functional outcomes. The stroke severity at admission and related information from 2006 to December 2007 were extracted retrospectively from a chart review.

RESULTS

In the regression approach, including age, the National Institutes of Health Stroke Scale (NIHSS) score and glucose level were the most significant predictors affecting both the BI and MRS. After applying the tree model, different tree structures were found. For the BI score, the NIHSS score interact with glucose, age and systolic blood pressure to form the tree structure. By contrast, the NIHSS score mainly interact with patients' age to form the tree model for MRS.

CONCLUSION

Both models have their pros and cons. The tree model otherwise provides risk interactions, and can effectively discriminate the risk groups for different functional outcomes. Applying both models to specific situations will provide a different angle for functional assessment and intervention in stroke rehabilitation.

Clinical cognition and embodiment

I first identify two different distinctions: between Cartesian cognition and embodied cognition, and between calculative rationality and intuitive know-how. I then suggest that, in the nursing literature, these two distinctions are run together, to create an opposition between 'Cartesian rationality' and 'embodied know-how'. However, it is vital to keep the two distinctions apart, because 'embodied knowing' is very frequently rational. In separating the idea of embodied cognition from non-rational intuition, I show how 'embodiment' leads to the concepts of distributed cognition and distributed expertise. This has extensive and important implications for how we understand clinical cognition in nursing.

Decision trees: an overview and their use in medicine

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.