Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes.

A Novel Computational Tool for Mining Real-Life Data: Application in the Metastatic Colorectal Cancer Care Setting.

Background: Randomized clinical trials constitute the gold-standard for evaluating new anti-cancer therapies; however, real-life data are key in complementing clinically useful information. We developed a computational tool for real-life data analysis and applied it to the metastatic colorectal cancer (mCRC) setting. This tool addressed the impact of oncology/non-oncology parameters on treatment patterns and clinical outcomes.

Understanding Deviations from Clinical Practice Guidelines in Adult Soft Tissue Sarcoma.

In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation.

Analyzing the "CareGap": assessing gaps in adherence to clinical guidelines in adult soft tissue sarcoma.

Clinical decision support systems (CDSSs) are gaining popularity as tools that assist physicians in optimizing medical care. These systems typically comply with evidence-based medicine and are designed with input from domain experts. Nonetheless, deviations from CDSS recommendations are abundant across a broad spectrum of disorders, raising the question as to why this phenomenon exists.

Bringing research to the point of care: Hypergenes project study.

With advance of health information IT systems and increasing volumes of disparate biomedical information repositories, harvesting them for research purposes is becoming more difficult. This is partly due to the proprietary nature of the current systems, but also due to diverse requirements of different research paradigms. On the flip side, ever larger amounts of clinical and genomic data are currently accumulated in research projects.

Knowledge-analytics synergy in Clinical Decision Support.

Clinical Decision Support (CDS) systems hold tremendous potential for improving patient care. Most existing systems are knowledge-based tools that rely on relatively simple rules. More recent approaches rely on analytics techniques to automatically mine EHR data to reveal meaningful insights.

Automatic detection of inconsistencies between free text and coded data in Sarcoma discharge letters.

Discordance between data stored in Electronic Health Records (EHR) may have a harmful effect on patient care. Automatic identification of such situations is an important yet challenging task, especially when the discordance involves information stored in free text fields. Here we present a method to automatically detect inconsistencies between data stored in free text and related coded fields.

Evicase: an evidence-based case structuring approach for personalized healthcare.

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside.

Prognostic data-driven clinical decision support - formulation and implications.

Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization.

MEDAL: measuring of emergency departments' adaptive load.

We propose an innovative approach for measuring real-time operational load within emergency departments. Medical informatics, operations researchers, and other decision makers in the health care field have yet to come to an agreement regarding standardized matrices for measuring operational load within emergency departments. As a result, it is difficult to develop methods and approaches for reducing operational load.