Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models.

Patient no-shows and suboptimal patient appointment length scheduling reduce clinical efficiency and impair the clinic's quality of service. The main objective of this study is to improve appointment scheduling in hospital outpatient clinics. We developed generic supervised machine learning models to predict patient no-shows and patient's length of appointment (LOA). We performed a retrospective study using more than 100,000 records of patient appointments in a hospital outpatient clinic. Several machine learning algorithms were used for the development of our prediction models. We trained our models on a dataset that contained patients', physicians', and appointments' characteristics. Our feature set combines both unstudied features and features adopted from previous studies. In addition, we identified the influential features for predicting LOA and no-show. Our LOA model's performance was 6.92 in terms of MAE, and our no-show model's performance was 92.1% in terms of F-score. We compared our models' performance to the performance of previous research models by applying their methods to our dataset; our models demonstrated better performance. We show that the major effector of such differences is the use of our novel features. To evaluate the effect of our prediction results on the quality of schedules produced by appointment systems (AS), we developed an interface layer between our prediction models and the AS, where prediction results comprise the AS input. Using our prediction models, there was an 80% improvement in the daily cumulative patient waiting time and a 33% reduction in the daily cumulative physician idle time.

Surgery duration: Optimized prediction and causality analysis.

Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients' waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose accuracy outperforms earlier results. In addition, unlike previous studies, we identify the features that influence DOS prediction. Further, in difference from others, we study the causal relationship between the feature set and DOS. The feature sets used in prior studies included a subset of the features presented in this study. This study aimed to derive influential effectors of duration of surgery via optimized prediction and causality analysis. We implemented an array of machine learning algorithms and trained them on datasets comprising surgery-related data, to derive DOS prediction models. The datasets we acquired contain patient, surgical staff, and surgery features. The datasets comprised 23,293 surgery records of eight surgery types performed over a 10-year period in a public hospital. We have introduced new, unstudied features and combined them with features adopted from previous studies to generate a comprehensive feature set. We utilized feature importance methods to identify the influential features, and causal inference methods to identify the causal features. Our model demonstrates superior performance in comparison to DOS prediction models in the art. The performance of our DOS model in terms of the mean absolute error (MAE) was 14.9 minutes. The algorithm that derived the model with the best performance was the gradient boosted trees (GBT). We identified the 10 most influential features and the 10 most causal features. In addition, we showed that 40% of the influential features have a significant (p-value = 0.05) causal relationship with DOS. We developed a DOS prediction model whose accuracy is higher than that of prior models. This improvement is achieved via the introduction of a novel feature set on which the model was trained. Utilizing our prediction model, hospitals can improve the efficiency of surgery schedules, and by exploiting the identified causal relationship, can influence the DOS. Further, the feature importance methods we used can help explain the model's predictions.

The role of semantics in the success of crowdfunding projects.

Crowdfunding platforms allow entrepreneurs to publish projects and raise funds for realizing them. Hence, the question of what influences projects' fundraising success is very important. Previous studies examined various factors such as project goals and project duration that may influence the outcomes of fundraising campaigns. We present a novel model for predicting the success of crowdfunding projects in meeting their funding goals. Our model focuses on semantic features only, whose performance is comparable to that of previous models. In an additional model we developed, we examine both project metadata and project semantics, delivering a comprehensive study of factors influencing crowdfunding success. Further, we analyze a large dataset of crowdfunding project data, larger than reported in the art. Finally, we show that when combining semantics and metadata, we arrive at F1 score accuracy of 96.2%. We compare our model's accuracy to the accuracy of previous research models by applying their methods on our dataset, and demonstrate higher accuracy of our model. In addition to our scientific contribution, we provide practical recommendations that may increase project funding success chances.