Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling.

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Domain-specific analytical language modeling--the chief complaint as a case study.

PURPOSE: A large share of the information in electronic medical records (EMRs) consists of free-text compositions. From a computational point-of-view, the continuing prevalence of free-text entry is a major hindrance when the goal is to increase automation in EMRs. However, the efforts in developing standards for the structured representation of medical information have not proven to be a panacea. The information space of clinical medicine is very diverse and constantly evolving, making it challenging to develop standards for the domain. This paper reports a study aiming to increase automation in the EMR through the computational understanding of specific class of medical text in English, namely emergency department chief complaints. METHODS: We apply domain-specific analytical modeling for the computational understanding of chief complaints. We evaluate the performance of this approach in the automatic classification of chief complaints, e.g., for use in automatic syndromic surveillance. RESULTS: The evaluation in a multi-hospital setting showed that the presented algorithm was accurate in terms of classification correctness. Also, use of approximate matching in the algorithm to cope with typographic variance did not affect classification correctness while increasing classification completeness.

Toward reflective management of emergency department chief complaint information.

An approach coined as "reflective information management" is presented as a technique for the management of emergency department chief complaint information. The architecture of a system integrating principles from this approach is described and its performance is evaluated in providing categorical information from free-text chief complaints for use, e.g., in automated syndromic surveillance.

Experiences from the Architectural Migration of a Joint Replacement Surgery Information System.

The goal of this study is to present the experiences gathered from the migration of an existing and deployed joint replacement surgery information system from a classical 2-tier architecture to a 4-tier architecture. These include discussion on the motivation for the migration and on the technical benefits of the chosen technical migration path and an evaluation of user experiences. The results from the analysis of clinical end-user and administrator experiences show an increase in the perceived performance and maintainability of the system and a high level of acceptance for the new system version.

Reorganization of oral anticoagulant (warfarin) treatment follow-up.

OBJECTIVE: To evaluate the impact of the reorganization of oral AC treatment management in a primary healthcare setting in terms of quality of care and cost of care. The evaluated reorganization involved the adoption of an AC nurse service model supported by the use of information and communication technology for storing and handling follow-up data. DESIGN: Statistical analysis of AC follow-up data, personnel task distribution and resource use analysis, and survey of personnel experiences. SETTING: The Paloheinä health station in Helsinki, Finland. SUBJECTS: All 122 Paloheinä patients on oral AC treatment and the related Paloheinä personnel. Also, follow-ups from two comparison health stations from Helsinki were used. MAIN OUTCOME MEASURES: Laboratory P-INR and follow-up density distributions before and after follow-up reorganization for Paloheinä and comparison station patients, task distribution, and personnel resource use in treatment guideline assignment before and after reorganization and gathered personnel experiences. RESULTS: No statistically significant changes (Pearson's chi-square test, p < 0.05) were observed in P-INR distributions, considering also the comparison stations. Adoption of the AC nurse model resulted in a 66%/34% task distribution between nurses and physicians in guideline assignment. Personnel resource use analysis showed a cost decrease of 1.61 euros per follow-up in guideline assignment. Users who responded to the survey were generally satisfied with the reorganization and its subjective impacts. CONCLUSION: Reorganization resulted in a potential reduction in cost of care per follow-up without a change in the observed quality of care in the Paloheinä setting. Personnel generally accepted the reorganization.