Development and external validation of clinical prediction models for pituitary surgery

Introduction: Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. Research question: This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. Material and methods: With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. Results: The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63–0.80) for GTR, 0.69 (0.52–0.83) for BR, as well as 0.82 (0.76–0.89) for IMP. Discussion and conclusion: All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient

Recovering Role-based Access Control Security Models from Dynamic Web Applications

Abstract. Security of dynamic web applications is a serious issue. While Model Driven Architecture (MDA) techniques can be used to generate applications with given access control security properties, analysis of existing web applications is more problematic. In this paper we present a model transformation technique to automatically construct a role-based access control (RBAC) security model of dynamic web applications from previously recovered structural and behavioral models. The SecureUML model generated by this technique can be used to check for security properties of the original application. We demonstrate our approach by constructing an RBAC security model of PhpBB, a popular internet bulletin board system.

Using Active Learning to Synthesize Models of Applications That Access Databases

We present a new technique that uses active learning to infer models of applications that manipulate relational databases. This technique comprises a domain-specific language for modeling applications that access databases (each model is a program in this language) and an associated inference algorithm that infers models of applications whose behavior can be expressed in this language. The inference algorithm generates test inputs and database configurations, runs the application, then observes the resulting database traffic and outputs to progressively refine its current model hypothesis. The end result is a model that completely captures the behavior of the application. Because the technique works only with the externally observable inputs, outputs, and databases, it can infer the behavior of applications written in arbitrary languages using arbitrary coding styles (as long as the behavior of the application is expressible in the domain-specific language). We also present a technique for automatically regenerating an implementation from the inferred model. The regenerator can produce a translated implementation in a different language and systematically include relevant security and error checks