Locating previously unknown patterns in data-mining results: a dual data- and knowledge-mining method

BACKGROUND: Data mining can be utilized to automate analysis of substantial amounts of data produced in many organizations. However, data mining produces large numbers of rules and patterns, many of which are not useful. Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck. In this paper we propose a method for automatically acquiring knowledge to shorten the pattern list by locating the novel and interesting ones. METHODS: The dual-mining method is based on automatically comparing the strength of patterns mined from a database with the strength of equivalent patterns mined from a relevant knowledgebase. When these two estimates of pattern strength do not match, a high "surprise score" is assigned to the pattern, identifying the pattern as potentially interesting. The surprise score captures the degree of novelty or interestingness of the mined pattern. In addition, we show how to compute p values for each surprise score, thus filtering out noise and attaching statistical significance. RESULTS: We have implemented the dual-mining method using scripts written in Perl and R. We applied the method to a large patient database and a biomedical literature citation knowledgebase. The system estimated association scores for 50,000 patterns, composed of disease entities and lab results, by querying the database and the knowledgebase. It then computed the surprise scores by comparing the pairs of association scores. Finally, the system estimated statistical significance of the scores. CONCLUSION: The dual-mining method eliminates more than 90% of patterns with strong associations, thus identifying them as uninteresting. We found that the pruning of patterns using the surprise score matched the biomedical evidence in the 100 cases that were examined by hand. The method automates the acquisition of knowledge, thus reducing dependence on the knowledge elicited from human expert, which is usually a rate-limiting step

Effectiveness of a multidisciplinary care program on recovery and return to work of patients after gynaecological surgery; design of a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Return to work after gynaecological surgery takes much longer than expected, irrespective of the level of invasiveness. In order to empower patients in recovery and return to work, a multidisciplinary care program consisting of an e-health intervention and integrated care management including participatory workplace intervention was developed.</p> <p>Methods/Design</p> <p>We designed a randomized controlled trial to assess the effect of the multidisciplinary care program on full sustainable return to work in patients after gynaecological surgery, compared to usual clinical care. Two hundred twelve women (18-65 years old) undergoing hysterectomy and/or laparoscopic adnexal surgery on benign indication in one of the 7 participating (university) hospitals in the Netherlands are expected to take part in this study at baseline. The primary outcome measure is sick leave duration until full sustainable return to work and is measured by a monthly calendar of sickness absence during 26 weeks after surgery. Secondary outcome measures are the effect of the care program on general recovery, quality of life, pain intensity and complications, and are assessed using questionnaires at baseline, 2, 6, 12 and 26 weeks after surgery.</p> <p>Discussion</p> <p>The discrepancy between expected physical recovery and actual return to work after gynaecological surgery contributes to the relevance of this study. There is strong evidence that long periods of sick leave can result in work disability, poorer general health and increased risk of mental health problems. We expect that this multidisciplinary care program will improve peri-operative care, contribute to a faster return to work of patients after gynaecological surgery and, as a consequence, will reduce societal costs considerably.</p> <p>Trial registration</p> <p>Netherlands Trial Register (NTR): <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2087">NTR2087</a></p

Diagnosis and management of Cornelia de Lange syndrome:first international consensus statement

Cornelia de Lange syndrome (CdLS) is an archetypical genetic syndrome that is characterized by intellectual disability, well-defined facial features, upper limb anomalies and atypical growth, among numerous other signs and symptoms. It is caused by variants in any one of seven genes, all of which have a structural or regulatory function in the cohesin complex. Although recent advances in next-generation sequencing have improved molecular diagnostics, marked heterogeneity exists in clinical and molecular diagnostic approaches and care practices worldwide. Here, we outline a series of recommendations that document the consensus of a group of international experts on clinical diagnostic criteria, both for classic CdLS and non-classic CdLS phenotypes, molecular investigations, long-term management and care planning