자연주의적 탐구조사(Naturalistic Inquiry)

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Acute kidney injury (AKI) is a complex disorder for which currently there is no accepted definition. Having a uniform standard for diagnosing and classifying AKI would enhance our ability to manage these patients. Future clinical and translational research in AKI will require collaborative networks of investigators drawn from various disciplines, dissemination of information via multidisciplinary joint conferences and publications, and improved translation of knowledge from pre-clinical research. We describe an initiative to develop uniform standards for defining and classifying AKI and to establish a forum for multidisciplinary interaction to improve care for patients with or at risk for AKI. Members representing key societies in critical care and nephrology along with additional experts in adult and pediatric AKI participated in a two day conference in Amsterdam, The Netherlands, in September 2005 and were assigned to one of three workgroups. Each group's discussions formed the basis for draft recommendations that were later refined and improved during discussion with the larger group. Dissenting opinions were also noted. The final draft recommendations were circulated to all participants and subsequently agreed upon as the consensus recommendations for this report. Participating societies endorsed the recommendations and agreed to help disseminate the results. The term AKI is proposed to represent the entire spectrum of acute renal failure. Diagnostic criteria for AKI are proposed based on acute alterations in serum creatinine or urine output. A staging system for AKI which reflects quantitative changes in serum creatinine and urine output has been developed. We describe the formation of a multidisciplinary collaborative network focused on AKI. We have proposed uniform standards for diagnosing and classifying AKI which will need to be validated in future studies. The Acute Kidney Injury Network offers a mechanism for proceeding with efforts to improve patient outcomes.

Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury

A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling. The dashboard was developed in R Shiny. We present results of our approach applied to an observational study of patients with moderate to severe rheumatoid arthritis (RA) on first-line biologic treatment.

A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

The care for patients with chronic and progressive diseases often requires that reliable estimates of their remaining lifetime are made. The predominant method for obtaining such individual prognoses is to analyze historical data using Cox regression, and apply the resulting model to data from new patients. However, the black-box nature of the Cox regression model makes it unattractive for clinical practice. Instead most physicians prefer to relate a new patient to the histories of similar, individual patients that were treated before. This paper presents a prognostic inference method that combines the k-nearest neighbor paradigm with Cox regression. It yields survival predictions for individual patients, based on small sets of similar patients from the past, and can be used to implement a prognostic case-retrieval system. To evaluate the method, it was applied to data from patients with idiopathic interstitial pneumonia, a progressive and lethal lung disease. Experiments pointed out that the method competes well with Cox regression. The best predictive performance was obtained with a neighborhood size of 20.

A Nearest Neighbor Approach to Predicting Survival Time with an Application in Chronic Respiratory Disease

Participatory research method to assess patients' needs for web-based self-management services: a pilot study

Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data to develop prediction models, this phenomenon is often ignored, leading to sub-optimal performance and generalisability of models when practices evolve. We propose a multi-task recurrent neural network which models three clinical presence dimensions – namely the longitudinal, the inter-observation and the missingness processes – in parallel to the survival outcome. On a prediction task using MIMIC III laboratory tests [1], explicit modelling of these three processes showed improved performance in comparison to state-of-the-art predictive models (C-index at 1 day horizon: 0.878). More importantly, the proposed approach was more robust to change in the clinical presence setting, demonstrated by performance comparison between patients admitted on weekdays and weekends. This analysis demonstrates the importance of studying and leveraging clinical presence to improve performance and create more transportable clinical models.

Niels Peek

Papers

A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis.

A Nearest Neighbor Approach to Predicting Survival Time with an Application in Chronic Respiratory Disease

Participatory research method to assess patients' needs for web-based self-management services: a pilot study

DeepJoint: Robust Survival Modelling Under Clinical Presence Shift

Effectiveness of Participatory Heuristic Evaluation: Case study with a Guideline Based Health Information System