AB1047 Towards the Design of a Decision Support Tool for Precise Care for Arthritis

BackgroundDecision Support requires the ability to classify individuals into subpopulations that differ in their susceptibility to diseases or their response to a specific treatment. Preventive or therapeutic interventions can then be focused on those who will benefit, sparing expense and side effects for those who will not. Thus, it is the tailoring of medical treatment to the individual characteristics of each patient and their susceptibility to various chronic diseases.ObjectivesBig Data analytics will empower physicians at the point of care to diagnose early arthritis stages, choose treatment approaches, decide when to refer to a subspecialist, and mitigate co-morbidities.Co-morbidity refers to co-occurrence of more than one disease in a person at a time. Examples include Diabetes, Cardiovascular diseases, renal diseases, Arthritis, etc. These diseases can occur by chance or there can be complex pathological associations. These indirect causal factors are only partially understood. It has been observed that the number of hospital admissions, as well as the mortality rate of comorbid patients, is significantly high. Hence, there is a need for early detection of these diseases. The aim of this project is to develop a clinical decision support system to study the clinical and genomic factors responsible for causing these diseases. Based on these findings, educate clinicians about how certain clinical and genomic factors are responsible for causing these diseases.MethodsMost genetic variations among people is a result of single nucleotide polymorphisms (SNPs), which are differences in a single nucleotide within a stretch of DNA. SNPs can result in the production of different RNA molecules and proteins, thus altering the body's metabolism and physiology. With approximately 10 million SNPs in the human genome, “big data” analytical methods are the most efficient means for discovering which SNPs are associated with a particular disease. Candidate gene studies and genome-wide association studies (GWAS) serve a similar purpose on a much smaller scale, but are infeasible for analyzing large amounts of data.ResultsDesign and Methodology: a.From a large EMR database extract records of persons with arthritis.b.Obtain information about SNP known to be risk causing from SNPedia, dbSNP.c.Integrate clinical and genomic data to obtain a universal feature vector.d.Perform feature extraction to extract relevant attributes.e.Run data mining algorithms like simple k-means to obtain clusters of patients and study similarity between them.The application systems interconnection logic is depicted in the diagram.ConclusionsThe proposed framework will enable a decision support tool for precision medicine in treatment of persons with arthritis.AcknowledgementsThis research has been sponsored by the U.S. Arthritis Foundation.Disclosure of InterestNone declare

Toward conducting motivational interviewing with an on-demand clinician avatar for tailored health behavior change interventions

In this article we describe work-in-progress about the development of avatar-based personalized assistants that can delivered motivational interviewing health behavior change interventions, tailored to its specific users Our approach combines the latest progress in Embodied Conversational Agents (ECAs), believable agents, and dialog systems. We discuss how we use different platforms to aim at providing accessibility of personalized health assistant, anytime anywhere

Uniform pacemaker and ICD information system in the Netherlands

The Central Pacemaker Patient Registry (CPPR) in the Netherlands (founded in 1977) collects information of pacemaker patients from all 110 Dutch hospitals. It contains data of over 98.000 patients, 118.500 pacemakers, 1.950 ICD's and 131.000 leads. Initially data was entered manually. As local databases started to evolve, data-transmission using modems was initiated in 1989. Currently 62 hospitals send data to the CPPR by e-mail. All these databases however showed some drawbacks. They allowed free text entry for the pacemaker type (existing or non-existing), could not send data as requested by the EURID protocol for ICD's, etc. An integrity study has shown that the quality of the data can be improved by enhanced plausibility checking. These conclusions initiated the development of one national Pacemaker and ICD information system for all clinics. After an extensive evaluation one software package was selected as the base of this new system. The locally entered data is checked on completeness and plausibility using centrally distributed reference files on pacemaker types, general practitioners, etc. All data is transmitted through e-mail. Patient data is encrypted. Only the holder of the central database is enabled to decrypt this data. The first version of the system has been released in the fall of 2001 and contained for each clinic a copy of its previously centrally stored data. Frequently updates of for instance new pacemaker types are sent to all clinics. The set-up for connection with the local hospital information systems is incorporated. A pacemaker follow-up agenda, statistical functions and export modules have been added. Customer satisfaction has been the most challenging part of introducing the information system. The conversion of the previously stored data into the new standard proved to be cumbersome. The uniform approach facilitates maintenance, while feedback from the clinics remains essential for maintaining the system to the state of the art. A study about the follow-up in the clinics will use the system to collect data. A major further challenge is the connection of pacemaker programming devices to the system through the HL-7 protocol