Feature Selection.

<p>Venn diagram shows the overlap of features selected through LASSO, elastic net and random forests modelling. 31 variables were common between all the three methods.</p

Model summary for validation of North India data.

<p><i>Prakriti</i> wise sensitivity and specificity of three models, LASSO, Elastic net and Random forests for validation of North India data.</p

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits

<div><p>In Ayurveda system of medicine individuals are classified into seven constitution types, “<i>Prakriti</i>”, for assessing disease susceptibility and drug responsiveness. <i>Prakriti</i> evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting <i>Prakriti</i> classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme <i>Prakriti types</i>, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for <i>Prakriti</i> respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme <i>Prakriti</i> classes. The supervised modelling approaches could classify individuals, with distinct <i>Prakriti</i> types, in the training and validation sets. This study is the first to demonstrate that <i>Prakriti</i> types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting <i>Prakriti</i> classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.</p></div

Summary of models (extreme vs non-extreme modelling).

<p>Sensitivity and specificity of glm models built from probability scores obtained from LASSO, Elastic-net and Random forests model. The table shows the sensitivity and specificity for the best model each selected from three algorithms.</p

Savannah plot for extreme <i>Prakriti</i> questionnaire data (male, female combined) data.

<p>Black vertical lines represents average silhouette width for a given cluster number obtained from original data, based on which three clusters were found to be optimum. Red vertical lines in the background represent average silhouette width obtained from 100 permuted data. Average silhouette width from permuted data are smaller compared to original data and reveals robust nature of the cluster number derived from original data.</p

Visual representation of <i>Prakriti</i> interpretation process based on original textual references.

<p><i>Prakriti</i> interpretation is based on the combinatorial occurrence of phenotypic feature that are captured through the questionnaire. This is an illustration with few examples of features. The three inner concentric circles represent feature category; feature sub-class and feature values of the questionnaire in each of the sub-classes. The outermost circle indicates the final interpretation in terms of <i>Vata</i> (V)/ <i>Pitta</i> (P)/ <i>Kapha</i> (K) based on the different combinations of values. For example, if skin type is thin it could be either due to <i>Vata</i> or <i>Pitta</i> however if it also dry and rough it would be interpreted as <i>Vata</i> type whereas if it is oily and loose or soft it would be considered as <i>Pitta</i> type. Similarly, if someone has a health problem in cold it could be <i>Vata</i> or <i>Kapha</i> type but humidity can further segregate it viz. health problem in moist with cold is for <i>Kapha</i> type whereas problem in cold and dry would be observed in <i>Vata</i> type.</p

Schematics demonstrating modelling strategy.

<p>Schematic showing the approach taken for modelling of (A) extreme <i>Prakriti</i> types followed by modelling of (B) extreme vs non-extreme using probability score generated from extreme <i>Prakriti</i> model for all the three methods. Maxima Probability scores were utilized to create binomial logistic regression for classification of extreme vs non-extreme.</p

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits - Fig 6

<p>a) Boxplot for maxima of probability scores generated from extreme <i>Prakriti</i> models. Using extreme <i>Prakriti</i> models from all the three approaches probability scores were generated for all the samples. For extreme <i>Prakriti</i> the probability was high while for non-extreme <i>Prakriti</i> probability was comparatively less. The difference in distribution of probability score provided the basis for extreme vs non-extreme <i>Prakriti</i> modelling b) Boxplot of 10 fold cross validation for extreme vs non-extreme modelling. Using maxima probability score glm models were built to classify extreme from non-extreme <i>Prakriti</i>. 10 fold cross validation of the models shows good classification performance of models. Best performing models, one each from LASSO, elastic net and random forests were selected. c) ROC curve for distinguishing extreme from non-extreme <i>Prakriti</i>. Three best glm models selected each from LASSO, elastic net and random forests show good discriminatory ability as evident from AUC.</p

MDS Visualization for unsupervised clusters and original <i>Prakriti</i> labels from extreme <i>Prakriti</i> questionnaire data (male, female combined).

<p>Each shape refers to one individual. Unsupervised clustering of questionnaire data for extreme <i>Prakriti</i> individual gives rise to three clusters. These clusters correspond to original <i>Prakriti</i> membership as shown in the figure in the right panel.</p

Global RECHARGE: Establishing a standard international data set for pulmonary rehabilitation in low- and middle-income countries

Chronic respiratory diseases (CRD) are highly prevalent in low- and middle-income countries (LMICs). People living with CRD are often disabled by breathlessness which can result in reduced health-related quality of life, including reduced exercise tolerance, significant psychological morbidity and reduced ability to work. Implementing clinically and cost-effective interventions to tackle these problems can be challenging in low-resource settings. Pulmonary rehabilitation is a low cost, high impact intervention that reverses CRD-related disability and is supported by the highest level of re-search. Pulmonary rehabilitation is delivered by a multidisciplinary team and has exercise training and education at its core to support effective disease management and improve people’s quality of life. There is an unmet need for pulmonary rehabilitation that is profound in LMICs where the demand greatly outweighs the capacity. The sparse existence of pulmonary rehabilitation in LMICs offers an important opportunity to support the expansion of high quality, benchmarked services as it becomes increasingly recognised and available. Quality assurance procedures for pulmonary rehabilitation in the developed world are now in place; helping to ensure a high standard of patient care. In this paper we discuss a common data set that has been developed by the NIHR Global Health Research Group on Respiratory Rehabilitation (Global RECHARGE). Standardising data collection with a pre-determined set of measurements is proposed whereby collaborators will use common data col-lection tools and procedures. Benchmarking and quality improvement with continuous audit offer a potential to maximise benefits, reduce waste and improve patient outcomes. We welcome expressions of interest from health care professionals and researchers from LMICs, including groups looking to strengthen their local research capacity and from those looking to set up pulmonary rehabilitation through to those already running a service. We believe the wide adoption of this core data set will facilitate quality assurance of pulmonary rehabilitation programmes, provide opportunities to expand services over time, de novo research opportunities offered by trans-national data and enhanced research capacity in partner organisations