Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

The current work addresses the unifi cation of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifi es multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing fl exibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profi le) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the defi nition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains

Can nudge-interventions address health service overuse and underuse? Protocol for a systematic review

IntroductionNudge-interventions aimed at health professionals are proposed to reduce the overuse and underuse of health services. However, little is known about their effectiveness at changing health professionals’ behaviours in relation to overuse or underuse of tests or treatments.ObjectiveThe aim of this study is to systematically identify and synthesise the studies that have assessed the effect of nudge-interventions aimed at health professionals on the overuse or underuse of health services.Methods and analysisWe will perform a systematic review. All study designs that include a control comparison will be included. Any qualified health professional, across any specialty or setting, will be included. Only nudge-interventions aimed at altering the behaviour of health professionals will be included. We will examine the effect of choice architecture nudges (default options, active choice, framing effects, order effects) and social nudges (accountable justification and pre-commitment or publicly declared pledge/contract). Studies with outcomes relevant to overuse or underuse of health services will be included. Relevant studies will be identified by a computer-aided search of the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library), MEDLINE, CINAHL, Embase and PsycINFO databases. Two independent reviewers will screen studies for eligibility, extract data and perform the risk of bias assessment using the criteria recommended by the Cochrane Effective Practice and Organisation of Care (EPOC) group. We will report our results in a structured synthesis format, as recommended by the Cochrane EPOC group.Ethics and disseminationNo ethical approval is required for this study. Results will be presented at relevant scientific conferences and in peer-reviewed literature

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted on an Indonesian population. The results obtained are very interesting as despite being tabular, binary categorical and multiclass data, the DNN model achieves performance and results much higher than the well-known XGB used in literature for data of this type

A clinician-mediated, longitudinal tracking system for the follow-up of clinical results

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references (p. 36-37).Failure to follow-up on abnormal tests is a common clinical concern comprising the quality of care. Although many clinicians track their patient follow-up by scheduling follow-up visits or by leaving physical reminders, most feel that automated, computerized systems to track abnormal test results would be useful. While existing clinical decision support systems and computerized clinical reminders focus on providing assistance with choosing the appropriate follow-up management, they fail by not tracking that follow-up effectively. We believe that clinicians do not want suggestions how to manage their patients, but instead want help tracking follow-up results once they have decided the management plan. We believe that a well-designed system can successfully track this follow-up and only require a small amount of information and time from the clinician. We have designed and implemented a complete tracking system including 1) an authoring tool to define tracking guidelines, 2) a query tool to search electronic medical records and identify patients without follow-up, and 3) a clinical tool to send reminders to clinicians and allow them to easily choose the follow-up management. Our tracking system has made improvements on previous reminder systems by 1) using our unique risk-management guideline model that more closely mirrors, yet does not attempt to replicate, the clinical decision process, 2) our use of massive population-based queries for tracking all patients simultaneously, and 3) our longitudinal approach that documents all steps in the patient follow-up cycle. With these developments, we are able to track 450 million pieces of clinical data for 1.8 million patients daily.(cont.) Keyword follow-up tracking; reminder system; preventive medicine; computerized medical record system; practice guidelines; clinical decision support systemby Daniel Todd Rosenthal.S.M

Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach

The current work addresses the unification of Electronic Health Records related to cervical cancer into a single medical knowledge source, in the context of the EU-funded ASSIST research project. The project aims to facilitate the research for cervical precancer and cancer through a system that virtually unifies multiple patient record repositories, physically located in different medical centers/hospitals, thus, increasing flexibility by allowing the formation of study groups “on demand” and by recycling patient records in new studies. To this end, ASSIST uses semantic technologies to translate all medical entities (such as patient examination results, history, habits, genetic profile) and represent them in a common form, encoded in the ASSIST Cervical Cancer Ontology. The current paper presents the knowledge elicitation approach followed, towards the definition and representation of the disease’s medical concepts and rules that constitute the basis for the ASSIST Cervical Cancer Ontology. The proposed approach constitutes a paradigm for semantic integration of heterogeneous clinical data that may be applicable to other biomedical application domains