Following visible successes on a wide range of predictive tasks, machine learning techniques are attracting substantial interest from medical researchers and clinicians. We address the need for capacity development in this area by providing a conceptual introduction to machine learning alongside a practical guide to developing and evaluating predictive algorithms using freely-available open source software and public domain data. We demonstrate the use of machine learning techniques by developing three predictive models for cancer diagnosis using descriptions of nuclei sampled from breast masses. These algorithms include regularized General Linear Model regression (GLMs), Support Vector Machines (SVMs) with a radial basis function kernel, and single-layer Artificial Neural Networks. The publicly-available dataset describing the breast mass samples (N=683) was randomly split into evaluation (n=456) and validation (n=227) samples. We trained algorithms on data from the evaluation sample before they were used to predict the diagnostic outcome in the validation dataset. We compared the predictions made on the validation datasets with the real-world diagnostic decisions to calculate the accuracy, sensitivity, and specificity of the three models. We explored the use of averaging and voting ensembles to improve predictive performance. We provide a step-by-step guide to developing algorithms using the open-source R statistical programming environment. The trained algorithms were able to classify cell nuclei with high accuracy (.94 -.96), sensitivity (.97 -.99), and specificity (.85 -.94). Maximum accuracy (.96) and area under the curve (.97) was achieved using the SVM algorithm. Prediction performance increased marginally (accuracy =.97, sensitivity =.99, specificity =.95) when algorithms were arranged into a voting ensemble. We use a straightforward example to demonstrate the theory and practice of machine learning for clinicians and medical researchers. The principals which we demonstrate here can be readily applied to other complex tasks including natural language processing and image recognition.

Background: The importance of patient-reported outcome measurement in chronic kidney disease (CKD) populations has been established. However, there remains a lack of research that has synthesised data around CKD-specific symptom and health-related quality of life (HRQOL) burden globally, to inform focused measurement of the most relevant patient-important information in a way that minimises patient burden. The aim of this review was to synthesise symptom prevalence/severity and HRQOL data across the following CKD clinical groups globally: (1) stage 1–5 and not on renal replacement therapy (RRT), (2) receiving dialysis, or (3) in receipt of a kidney transplant. Methods and findings: MEDLINE, PsycINFO, and CINAHL were searched for English-language cross-sectional/longitudinal studies reporting prevalence and/or severity of symptoms and/or HRQOL in CKD, published between January 2000 and September 2021, including adult patients with CKD, and measuring symptom prevalence/severity and/or HRQOL using a patient-reported outcome measure (PROM). Random effects meta-analyses were used to pool data, stratified by CKD group: not on RRT, receiving dialysis, or in receipt of a kidney transplant. Methodological quality of included studies was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data, and an exploration of publication bias performed. The search identified 1,529 studies, of which 449, with 199,147 participants from 62 countries, were included in the analysis. Studies used 67 different symptom and HRQOL outcome measures, which provided data on 68 reported symptoms. Random effects meta-analyses highlighted the considerable symptom and HRQOL burden associated with CKD, with fatigue particularly prevalent, both in patients not on RRT (14 studies, 4,139 participants: 70%, 95% CI 60%–79%) and those receiving dialysis (21 studies, 2,943 participants: 70%, 95% CI 64%–76%). A number of symptoms were significantly (p < 0.05 after adjustment for multiple testing) less prevalent and/or less severe within the post-transplantation population, which may suggest attribution to CKD (fatigue, depression, itching, poor mobility, poor sleep, and dry mouth). Quality of life was commonly lower in patients on dialysis (36-Item Short Form Health Survey [SF-36] Mental Component Summary [MCS] 45.7 [95% CI 45.5–45.8]; SF-36 Physical Component Summary [PCS] 35.5 [95% CI 35.3–35.6]; 91 studies, 32,105 participants for MCS and PCS) than in other CKD populations (patients not on RRT: SF-36 MCS 66.6 [95% CI 66.5–66.6], p = 0.002; PCS 66.3 [95% CI 66.2–66.4], p = 0.002; 39 studies, 24,600 participants; transplant: MCS 50.0 [95% CI 49.9–50.1], p = 0.002; PCS 48.0 [95% CI 47.9–48.1], p = 0.002; 39 studies, 9,664 participants). Limitations of the analysis are the relatively few studies contributing to symptom severity estimates and inconsistent use of PROMs (different measures and time points) across the included literature, which hindered interpretation. Conclusions: The main findings highlight the considerable symptom and HRQOL burden associated with CKD. The synthesis provides a detailed overview of the symptom/HRQOL profile across clinical groups, which may support healthcare professionals when discussing, measuring, and managing the potential treatment burden associated with CKD. Protocol registration: PROSPERO CRD42020164737.

Objective To test the effectiveness of an integrated collaborative care model for people with depression and long term physical conditions. Design Cluster randomised controlled trial. Setting 36 general practices in the north west of England. Participants 387 patients with a record of diabetes or heart disease, or both, who had depressive symptoms (≥10 on patient health questionaire-9 (PHQ-9)) for at least two weeks. Mean age was 58.5 (SD 11.7). Participants reported a mean of 6.2 (SD 3.0) long term conditions other than diabetes or heart disease; 240 (62%) were men; 360 (90%) completed the trial. Interventions Collaborative care included patient preference for behavioural activation, cognitive restructuring, graded exposure, and/or lifestyle advice, management of drug treatment, and prevention of relapse. Up to eight sessions of psychological treatment were delivered by specially trained psychological wellbeing practitioners employed by Improving Access to Psychological Therapy services in the English National Health Service; integration of care was enhanced by two treatment sessions delivered jointly with the practice nurse. Usual care was standard clinical practice provided by general practitioners and practice nurses. Main outcome measures The primary outcome was reduction in symptoms of depression on the self reported symptom checklist-13 depression scale (SCL-D13) at four months after baseline assessment. Secondary outcomes included anxiety symptoms (generalised anxiety disorder 7), self management (health education impact questionnaire), disability (Sheehan disability scale), and global quality of life (WHOQOL-BREF). Results 19 general practices were randomised to collaborative care and 20 to usual care; three practices withdrew from the trial before patients were recruited. 191 patients were recruited from practices allocated to collaborative care, and 196 from practices allocated to usual care. After adjustment for baseline depression score, mean depressive scores were 0.23 SCL-D13 points lower (95% confidence interval −0.41 to −0.05) in the collaborative care arm, equal to an adjusted standardised effect size of 0.30. Patients in the intervention arm also reported being better self managers, rated their care as more patient centred, and were more satisfied with their care. There were no significant differences between groups in quality of life, disease specific quality of life, self efficacy, disability, and social support. Conclusions Collaborative care that incorporates brief low intensity psychological therapy delivered in partnership with practice nurses in primary care can reduce depression and improve self management of chronic disease in people with mental and physical multimorbidity. The size of the treatment effects were modest and were less than the prespecified effect but were achieved in a trial run in routine settings with a deprived population with high levels of mental and physical multimorbidity. Trial registration ISRCTN80309252.

Background: There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data. Methods: We used open-source software and a publicly available dataset to train and validate multiple ML models to classify breast masses into benign or malignant using mammography image features and patient age. We compared algorithm predictions to the ground truth of histopathologic evaluation. We provide step-by-step instructions with accompanying code lines. Findings: Performance of the five algorithms at classifying breast masses as benign or malignant based on mammography image features and patient age was statistically equivalent (P > 0.05). Area under the receiver operating characteristics curve (AUROC) for the logistic regression with elastic net penalty was 0.89 (95% CI 0.85 – 0.94), for the Extreme Gradient Boosting Tree 0.88 (95% CI 0.83 – 0.93), for the Multivariate Adaptive Regression Spline algorithm 0.88 (95% CI 0.83 – 0.93), for the Support Vector Machine 0.89 (95% CI 0.84 – 0.93), and for the neural network 0.89 (95% CI 0.84 – 0.93). Interpretation: Our paper allows clinicians and medical researchers who are interested in using ML algorithms to understand and recreate the elements of a comprehensive ML analysis. Following our instructions may help to improve model generalizability and reproducibility in medical ML studies.

Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient’s future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.

The Hospital Anxiety and Depression Scale (HADS) is commonly used to assess symptoms of anxiety and depression in motor neurone disease (MND). The measure has never been specifically validated for use within this population, despite questions raised about the scale's validity. This study seeks to analyse the construct validity of the HADS in MND by fitting its data to the Rasch model.