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Mining patient data for an efficient clinical decision support system dedicated to the treatment of mental illnesses LIRMM tutors: Sandra BRINGAY, Pascal Poncelet and Mathieu Roche, http://www2.lirmm.fr/TATOO/ Optimal Medicine tutor: Janet Munro, http://www.optimalmedicine.com/index.htm INSERM tutor: Karen Ritchie, http://www.montp.inserm.fr/u1061/SiteU1061/index.htm Internship Project Summary: Optimal Medicine is a personalised medicine company developing clinical decision support (CDS) software to improve the treatment of mental illnesses by tailoring treatment regimes to care optimally for each individual patient. Effective CDS software requires the capture and assimilation of the knowledge base, including guidelines, publications, expert opinion and research datasets. For example, risk factors for side effects of schizophrenia drug treatments (antipsychotics) have been identified: antipsychotic-­‐induced weight gain is influenced by gender, ethnicity, age, baseline weight, duration of previous treatment, drug prescribed, etc. Publications report both universal and discrepant findings. Doctors also have their own clinical experience of which patients are at risk. The same problems exist for predicting treatment efficacy (symptom improvement). How can we use clinical research data, publications and different expert opinions to identify and “weight” the risk factors so that each individual patient’s profile can be used to guide treatment? The project will explore techniques to identify predictors underpinning a decision support system, which can recommend the best consensus treatment actions for a given patient. The project will comprise four distinct phases: PHASE I: Data set preparation. Phase I will involve preparation of a dataset for analysis during Phase II. One of Optimal Medicine’s anonymised datasets of clinical variables + biomarker data obtained from a cohort of consenting patients treated with antipsychotic medication (n>500) will be used in the project. The dataset will be used to explore “predictors” of response in four different symptom groups which antipsychotic medications are used to treat. In Phase I the attribute variables (putative predictors) and outcomes will be defined and the dataset prepared for Phase II. PHASE II: Data mining. Several techniques of data mining will be used to extract patterns (in particular association rules (Agrawal 1993) and sequential pattern (Agrawal 1995)) which will be used as predictors. An open theoretical question associated with this problematic would be: Can we define an interestingness measure, which improves the prediction and reduces scalability problems? PHASE III: Literature integration. Phase III will explore the use of the published literature to create a consensus of “weighted” attributes associated with the four symptom responses (Sallaberry 2011). Phase II and Phase II methods will be compared. The feasibility of using the published literature as a “validation” of the Phase II attributes will be explored. PHASE IV: Translational application. Phase IV will explore the feasibility of translating the project findings into a tool for clinical decision support. The project will be hosted jointly with Optimal Medicine’s partners, INSERM U1061 at the Colombière Hospital in Montpellier. Professor Karen Ritchie, the unit director, will be the INSERM project lead. The project will benefit enormously from the dual perspectives offered by the commercial and clinical research partners. Student Profile: Work under the internship will be principally in English. Students should have an interest to learn from Optimal Medicine and INSERM about the rapidly emerging e-­‐health and clinical decision support field from both the business and clinical care perspectives. The students will have the motivation and intellectual curiosity to work with Optimal Medicine, INSERM and local psychiatrists to explore methods for creating a computerised “decision support system” underpinning a clinical decision support tool. Skills will include a basic understanding of the principles of knowledge capture and data mining and an ability to use a dataset to explore one or more methods in detail. Students require the interpersonal skills to interact with local mental health care personnel. Bibliography Agrawal R., Imielinski T. and Swami A., Mining Association Rules between Sets of Items in Large Databases, 1993, 207-­‐216. Agrawal R. and Srikant R., Mining Sequential Patterns, 1995, 3-­‐14. Sallaberry A., Pecheur N., Bringay S., Roche M., Teisseire M., Sequential patterns mining and gene sequence visualization to discover novelty from microarray data. J Biomed Inform. 2011 Oct;44(5):760-­‐74. Epub 2011 Apr 16.