Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all‐cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all‐cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free‐living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random Under‐ Sampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data‐driven and disease‐agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness

Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults

Definitive Radiotherapy in Invasive Vaginal Carcinoma: A Systematic Review

AIM: This study systematically reviews the recent literature on the role of definitive radiotherapy (RT) in the management of vaginal cancer (VC) and presents comprehensive data on clinical outcomes and toxicity. METHODS: The authors performed a literature search using PubMed (2007-2016) to identify all prospective and retrospective studies that have been published on RT in invasive VC. RESULTS: Of the 199 identified studies, 13 met the inclusion criteria. All studies had a retrospective design. Overall, 793 patients (median, 45; range, 26-138) were included. A high heterogeneity was found across studies in terms of RT techniques, assessment criteria, and reported outcomes. The majority of the patients were treated with a combination of external beam RT and brachytherapy (74.2%). Acute and late grade 653 toxicity rates ranged from 0.0% to 24.4% (median, 8.7%) and from 0.0% to 22.5% (median, 12.8%), respectively. The 5-year local control rates ranged between 39% and 79%. The 5-year overall survival ranged between 34% and 71.0% (median, 63.5%). Early stage of the disease (International Federation of Gynecology and Obstetrics stages I-II vs. III-IV), small tumor size (<4 cm), previous hysterectomy, high pretreatment/treatment hemoglobin levels ( 6512/12.5 mg/dL), and patients' age <70 or <64 years were correlated with better clinical outcomes. CONCLUSION: Only retrospective studies, in a limited number, have been published on RT in VC in the past decade, with significant heterogeneity in terms of treatment characteristic and evaluation criteria. Clinical results were strongly influenced by tumor stage. Prospective randomized studies are needed to improve patients' outcomes, especially in advanced-stage disease.;23:1-10 IMPLICATIONS FOR PRACTICE: This study systematically reviews the recent literature on the role of definitive radiotherapy in the management of vaginal cancer and presents comprehensive data on clinical outcome and toxicity. The prognosis of patients is dismal, with a 5-year overall survival of approximately 50%. Early stage of the disease, small tumor size, previous hysterectomy, high pretreatment/treatment hemoglobin levels, and patients' age were correlated with a better clinical outcome. A brachytherapy boost should be delivered, especially in patients with higher-stage disease. The addition of concurrent weekly cisplatin should be considered in most patients, and transfusion should be used to maintain high hemoglobin levels.