Understanding patterns of care for musculoskeletal patients using routinely collected National Health Service data from general practices in England

Musculoskeletal conditions are extremely common and represent a costly and growing problem in the United Kingdom. Understanding patterns of care and how they vary between individual patients and patient groups is necessary for effective and efficient disease management. In this article, we present a novel approach to understanding patterns of care for musculoskeletal patients in which trajectories are constructed from clinical and administrative data that are routinely collected by clinicians and healthcare professionals. Our approach is applied to routinely collected National Health Service data for musculoskeletal patients who were registered to a set of general practices in England and highlights both known and previously unreported variations in the prescribing of opioid analgesics by gender and presence of pre-existing depression. We conclude that the application of our approach to routinely collected National Health Service data can extend the dimensions over which patterns of care can be understood for musculoskeletal patients and for patients with other long-term conditions

An approach for mining care trajectories for chronic diseases

International audienceWith the increasing burden of chronic illnesses, administrative health care databases hold valuable information that could be used to monitor and assess the processes shaping the trajectory of care of chronic patients. In this context, temporal data mining methods are promising tools, though lacking flexibility in addressing the complex nature of medical events. Here, we present a new algorithm able to extract patient trajectory patterns with different levels of granularity by relying on external taxonomies. We show the interest of our approach with the analysis of trajectories of care for colorectal cancer using data from the French casemix information system

AN APPROACH FOR AUTO-GENERATING SOLUTION TO USER-GENERATED MEDICAL CONTENT USING DEEP LEARNING TECHNIQUES

One of many things humans are obsessive about is health. Presently, when faced with a health-related issue one goes to the web first, to find closure to his/her problem. The community Question Answering (cQA) forum allows people to pose their query and/or discuss it. Due to alike or unique nature of the health query it may go unanswered. Many a time the answers provided are ill-founded, leaving the user discontent. This indicates that the process is dependent on supplementary users or experts, in relation to their ability and/or the time taken to answer the question. Hence, the need to create an answer predictor which provides instant and better-quality result. We, therefore propose a novel scheme where deep learning is used to produce appropriate answer to the given health query. Both historical data i.e. cQA and general medical data are used to form a powerful Knowledge Base (KB), to assist the health predictor

Representation and Analysis of Multi-Modal, Nonuniform Time Series Data: An Application to Survival Prognosis of Oncology Patients in an Outpatient Setting

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which was explored initially through discrete sampling and behavioral representation. The length of survival was evaluated with both classification and regression methods initially. The first conclusion determined that including more discrete samples in the model showed no statistical benefit and the addition of behavioral approaches did improve the prognostic accuracy. From this result, the adaption of Piecewise Aggregate Approximation was made to accommodate the multi-modal time series data of the outpatient clinical data, and evaluated with the regression methodologies. This representation approach demonstrated promise due to the simplicity but had decreased performance in the length of survival prognosis compared with behavioral representation and discrete samples approach. A solution was a new representation approach made which incorporates a genetic algorithm to select the window boundaries of the Piecewise Aggregate Approximation method. This selection is based on the fraction of the Piecewise Aggregate Approximation windows that contain values other than zero. The new representation improved the performance in some cases by a 20% reduction in median relative error

A Temporal Abstraction Framework for Classifying Clinical Temporal Data

The increasing availability of complex temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient’s time-series data based on temporal abstractions. The proposed STF-Mine algorithm automatically mines discriminative temporal abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems