Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Background: To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) “black-box” approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Methods: Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients’ attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/ pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Results: Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians’ decision pathways to diagnose AMD. C Conclusions: Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support

Social Inequalities of Functioning and Perceived Health in Switzerland–A Representative Cross-Sectional Analysis

Many people worldwide live with a disability, i.e. limitations in functioning. The prevalence is expected to increase due to demographic change and the growing importance of non-communicable disease and injury. To date, many epidemiological studies have used simple dichotomous measures of disability, even though the WHO's International Classification of Functioning, Disability, and Health (ICF) provides a multi-dimensional framework of functioning. We aimed to examine associations of socio-economic status (SES) and social integration in 3 core domains of functioning (impairment, pain, limitations in activity and participation) and perceived health. We conducted a secondary analysis of representative cross-sectional data of the Swiss Health Survey 2007 including 10,336 female and 8,424 male Swiss residents aged 15 or more. Guided by a theoretical ICF-based model, 4 mixed effects Poisson regressions were fitted in order to explain functioning and perceived health by indicators of SES and social integration. Analyses were stratified by age groups (15–30, 31–54, ≥55 years). In all age groups, SES and social integration were significantly associated with functional and perceived health. Among the functional domains, impairment and pain were closely related, and both were associated with limitations in activity and participation. SES, social integration and functioning were related to perceived health. We found pronounced social inequalities in functioning and perceived health, supporting our theoretical model. Social factors play a significant role in the experience of health, even in a wealthy country such as Switzerland. These findings await confirmation in other, particularly lower resourced settings

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding