The Requirements for Ontologies in Medical Data Integration: A Case Study

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

"There are too many, but never enough": qualitative case study investigating routine coding of clinical information in depression.

We sought to understand how clinical information relating to the management of depression is routinely coded in different clinical settings and the perspectives of and implications for different stakeholders with a view to understanding how these may be aligned

Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

Engaging with clinicians to implement and evaluate the ICF in neurorehabilitation practice

INTRODUCTION: Although deemed a globally accepted framework, there remains scare evidence on the process and outcome of implementing the International Classification of Functioning, Disability and Health (ICF) within neurorehabilitation. OBJECTIVES: This review briefly explores the existing, broader literature and then reports on two action research projects, undertaken in England, specifically within stroke and neurorehabilitation. Working with participants, including clinicians from in-patient and community settings, there are now 35 different ways identified for the use of the ICF. CONCLUSION: The outcome of the first project highlights that using the ICF enhances communication within and beyond the acute stroke service, fosters holistic thinking and clarifies team roles. To adopt it into clinical practice, the ICF must be adapted to meet local service needs. The use of action research has facilitated the knowledge translation process which has enabled the ICF to become a clinical reality in neurorehabilitation, with clinicians identifying a range of potential uses

A qualitative insight into rural casemix education, CHERE Project Report No 10

NSW, while often regarded as the non- Casemix state, has been using Casemix information to assist planning and funding of hospitals. However, the use of this tool and the necessary education and knowledge about Casemix has not been evenly spread throughout the state, with health service staff in metropolitan areas relatively more familiar with its use then their colleagues in rural NSW. In 1998, both NSW Health and the NSW Casemix Clinical Committee (NCCC) proposed that an effort be made to increase the knowledge and participation of rural clinical and health service staff in Casemix activities. This research was proposed as a means of establishing the current situation regarding Casemix, knowledge in rural areas, providing advice regarding the best methods of implementing Casemix education for rural staff and, if possible, evaluating the success of the education. Casemix is a broad term referring to the tools and information system used to assist in such activities as planning, benchmarking, managing and funding health care services. Casemix is underpinned by classification systems that allow meaningful comparisons of workload or throughput between facilities. In this study, qualitative research methods were used to examine the issues faced by rural health service staff in gaining knowledge of and using Casemix. This information was supplemented by a survey, which assessed the level of knowledge and understanding of Casemix in two rural areas.Casemix, hospital funding

Predictive modeling of housing instability and homelessness in the Veterans Health Administration

OBJECTIVE: To develop and test predictive models of housing instability and homelessness based on responses to a brief screening instrument administered throughout the Veterans Health Administration (VHA). DATA SOURCES/STUDY SETTING: Electronic medical record data from 5.8 million Veterans who responded to the VHA's Homelessness Screening Clinical Reminder (HSCR) between October 2012 and September 2015. STUDY DESIGN: We randomly selected 80% of Veterans in our sample to develop predictive models. We evaluated the performance of both logistic regression and random forests—a machine learning algorithm—using the remaining 20% of cases. DATA COLLECTION/EXTRACTION METHODS: Data were extracted from two sources: VHA's Corporate Data Warehouse and National Homeless Registry. PRINCIPAL FINDINGS: Performance for all models was acceptable or better. Random forests models were more sensitive in predicting housing instability and homelessness than logistic regression, but less specific in predicting housing instability. Rates of positive screens for both outcomes were highest among Veterans in the top strata of model‐predicted risk. CONCLUSIONS: Predictive models based on medical record data can identify Veterans likely to report housing instability and homelessness, making the HSCR screening process more efficient and informing new engagement strategies. Our findings have implications for similar instruments in other health care systems.U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSR&D), Grant/Award Number: IIR 13-334 (IIR 13-334 - U.S. Department of Veterans Affairs (VA) Health Services Research and Development (HSRD))Accepted manuscrip