Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study.

Introduction: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom.

Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents.

Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly.

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records.

To use available electronic administrative records to identify data reliability, predict discharge destination, and identify risk factors associated with specific outcomes following hospital admission with stroke, compared to stroke specific clinical factors, using machine learning techniques. The study included 2,531 patients having at least one admission with a confirmed diagnosis of stroke, collected from a regional hospital in Australia within 2009-2013. Using machine learning (penalized regression with Lasso) techniques, patients having their index admission between June 2009 and July 2012 were used to derive predictive models, and patients having their index admission between July 2012 and June 2013 were used for validation.

Identification of predictors and model for predicting prolonged length of stay in dengue patients.

Purpose: Our objective is to identify the predictive factors and predict hospital length of stay (LOS) in dengue patients, for efficient utilization of hospital resources.

Methods: We collected 1360 medical patient records of confirmed dengue infection from 2012 to 2017 at Max group of hospitals in India. We applied two different data mining algorithms, logistic regression (LR) with elastic-net, and random forest to extract predictive factors and predict the LOS.

Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient.

Background: The last decade has seen a major increase in the availability of genomic data. This includes expert-curated databases that describe the biological activity of genes, as well as high-throughput assays that measure gene expression in bulk tissue and single cells. Integrating these heterogeneous data sources can generate new hypotheses about biological systems.

Predictive Model Based on Health Data Analysis for Risk of Readmission in Disease-Specific Cohorts.

Background: Intervention planning to reduce 30-day readmission post-acute myocardial infarction (AMI) in an environment of resource scarcity can be improved by readmission prediction score. The aim of study is to derive and validate a prediction model based on routinely collected hospital data for identification of risk factors for all-cause readmission within zero to 30 days post discharge from AMI.

Methods: Our study includes 2,849 AMI patient records (January 2005 to December 2014) from a tertiary care facility in India.

Improving the Tensile Properties of Wet Spun Silk Fibers Using Rapid Bayesian Algorithm.

Wet spinning of silkworm silk has the potential to overcome the limitations of the natural spinning process, producing fibers with exceptional mechanical properties. However, the complexity of the extraction and spinning processes have meant that this potential has so far not been realized. The choice of silk processing parameters, including fiber degumming, dissolving, and concentration, are critical in producing a sufficiently viscous dope, while avoiding silk's natural tendency to gel via self-assembly.

Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes.

The scale-up of laboratory procedures to industrial production is the main challenge standing between ideation and the successful introduction of novel materials into commercial products. Retaining quality while ensuring high per-batch production yields is the main challenge. Batch processing and other dynamic strategies that preserve product quality can be applied, but they typically involve a variety of experimental parameters and functions that are difficult to optimize because of interdependencies that are often antagonistic.

Optimizing a High-Entropy System: Software-Assisted Development of Highly Hydrophobic Surfaces using an Amphiphilic Polymer.

In materials science, the investigation of a large and complex experimental space is time-consuming and thus may induce bias to exclude potential solutions where little to no knowledge is available. This work presents the development of a highly hydrophobic material from an amphiphilic polymer through a novel, adaptive artificial intelligence approach. The hydrophobicity arises from the random packing of short polymer fibers into paper, a highly entropic, multistep process.

Rapid Bayesian optimisation for synthesis of short polymer fiber materials.

The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as complexity increases, exploration of variables can become impractical using traditional combinatorial approaches. We describe an iterative method which uses machine learning to optimise process development, incorporating multiple qualitative and quantitative objectives.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

Background: As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs.

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys.

Web search activity data accurately predict population chronic disease risk in the USA.

Background: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors.

A framework for feature extraction from hospital medical data with applications in risk prediction.

Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task.

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data.

Objective: Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.

Methods: The study included 1660 consecutive AMI admissions.