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Despite a newfound wealth of data and information, the healthcare sector is lacking in actionable knowledge. This is largely because healthcare data, though plentiful, tends to be inherently complex and fragmented. Health data analytics, with an emphasis on predictive analytics, is emerging as a transformative tool that can enable more proactive and preventative treatment options. This review considers the ways in which predictive analytics has been applied in the for-profit business sector to generate well-timed and accurate predictions of key outcomes, with a focus on key features that may be applicable to healthcare-specific applications. Published medical research presenting assessments of predictive analytics technology in medical applications are reviewed, with particular emphasis on how hospitals have integrated predictive analytics into their day-to-day healthcare services to improve quality of care. This review also highlights the numerous challenges of implementing predictive analytics in healthcare settings and concludes with a discussion of current efforts to implement healthcare data analytics in the developing country, Saudi Arabia.

Inflammatory bowel disease patients have impaired quality of life with physical, social and emotional dysfunction. This project aimed to assess the effects of socio-demographic and clinical variables on quality of life and to identify its predictors. In a cross-sectional paper-based study, 50 consecutive non-selected patients attending the teaching hospital completed a disease-specific McMaster quality of life tool. Socio-demographic and clinical data were collected from patients' records. The t-test and Mann-Whitney test were used to determine the probability of significant differences between quality of life domains and independent variables. Multiple linear regression was used to determine quality of life predictors. Younger and highly educated patients had higher social scores. Those with shorter disease durations had higher systemic scores. Patients in remission had higher systemic, social, bowel and overall scores. Relapse was a significant predictor of decreased systemic, social, bowel and overall scores. Long disease duration was a significant predictor of decreased systemic and overall scores. Younger age at disease onset was a significant predictor of decreased emotional score. However, high education was a significant predictor of improved social score. Relapse, long disease duration, low education and young age at disease onset were associated with low quality of life. Prospective studies should investigate how interventions addressing these predictors may lead to improved quality of life.

Purpose Predictive analytics (PA) is a new trending approach in the field of healthcare that uses machine learning to build a prediction model using supervised learning algorithms. Isolated coronary artery bypass grafting (iCABG), an open-heart surgery, is commonly performed in the treatment of coronary heart disease. Aim The aim of this study was to develop and evaluate a model to predict postoperative length of stay (PLoS) for iCABG patients using supervised machine learning techniques, and to identify the features with the highest contribution to the model. Methods This is a retrospective study that uses historic data of adult patients who underwent isolated CABG (iCABG). After initial data pre-processing, data imputation using the kNN method was applied. The study used five prediction models using Naïve Bayes, Decision Tree, Random Forest, Logistic Regression and k Nearest Neighbor algorithms. Data imbalance was managed using the following widely used methods: oversampling, undersampling, “Both”, and random over-sampling examples (ROSE). The features selection process was conducted using the Boruta method. Two techniques were applied to examine the performance of the models, (70%, 30%) split and cross-validation, respectively. Models were evaluated by comparing their performance using AUC and other metrics. Results In the final dataset, six distinct features and 621 instances were used to develop the models. A total of 20 models were developed using R statistical software. The model generated using Random Forest with “Both” resampling method and cross-validation technique was deemed the best fit (AUC=0.81; F1 score=0.82; and recall=0.82). Attributes found to be highly predictive of PLoS were pulmonary artery systolic, age, height, EuroScore II, intra-aortic balloon pump used, and complications during operation. Conclusion This study demonstrates the significance and effectiveness of building a model that predicts PLoS for iCABG patients using patient specifications and pre-/intra-operative measures.