A Framework for Mining Evolution Rules and Its Application to the Clinical Domain

2015 ◽  
Author(s):  
Pietro Sala ◽  
Carlo Combi ◽  
Matteo Cuccato ◽  
Andrea Galvani ◽  
Alberto Sabaini
Keyword(s):  
Clinical Domain

The imaginability of the single case in clinical domain: When 1 in 200 is riskier than 5 in 1000

2006 ◽  
Author(s):  
Lucia Savadori ◽  
Stefania Pighin ◽  
Elisa Barilli ◽  
Laura Cremonesi ◽  
Sara Bonalumi
Keyword(s):  
Single Case ◽  
Clinical Domain

scholarly journals Sex as a Prognostic Factor in Systematic Reviews: Challenges and Lessons Learned

2021 ◽  
Vol 11 (6) ◽  
pp. 441
Author(s):  
Elena Stallings ◽  
Alba Antequera ◽  
Jesús López-Alcalde ◽  
Miguel García-Martín ◽  
Gerard Urrútia ◽  
...  
Keyword(s):  
Prognostic Factor ◽  
Systematic Reviews ◽  
Lessons Learned ◽  
Disease State ◽  
Sex And Gender ◽  
Funding Agencies ◽  
And Gender ◽  
Baseline Factor ◽  
Clinical Domain ◽  
Made In

Sex is a common baseline factor collected in studies that has the potential to be a prognostic factor (PF) in several clinical areas. In recent years, research on sex as a PF has increased; however, this influx of new studies frequently shows conflicting results across the same treatment or disease state. Thus, systematic reviews (SRs) addressing sex as a PF may help us to better understand diseases and further personalize healthcare. We wrote this article to offer insights into the challenges we encountered when conducting SRs on sex as a PF and suggestions on how to overcome these obstacles, regardless of the clinical domain. When carrying out a PF SR with sex as the index factor, it is important to keep in mind the modifications that must be made in various SR stages, such as modifying the PF section of CHARMS-PF, adjusting certain sections of QUIPS and extracting data on the sex and gender terms used throughout the studies. In this paper, we provide an overview of the lessons learned from carrying out our reviews on sex as a PF in different disciplines and now call on researchers, funding agencies and journals to realize the importance of studying sex as a PF.

Using the diagnostic odds ratio to select multivariate sequential patterns in order to build an interpretable pattern-based classifier in a clinical domain (Preprint)

2021 ◽  
Author(s):  
Isidoro J. Casanova ◽  
Manuel Campos ◽  
Jose M. Juarez ◽  
Antonio Gomariz ◽  
Marta Lorente-Ros ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Odds Ratio ◽  
Diagnostic Odds Ratio ◽  
University Hospital ◽  
Classification Model ◽  
Sequential Patterns ◽  
Pattern Selection ◽  
Essential Property ◽  
Frequency Properties ◽  
Clinical Domain

BACKGROUND It is important to exploit all available data on patients in settings such as Intensive Care Burn Units (ICBUs), where several variables are recorded over time. It is possible to take advantage of the multivariate patterns that model the evolution of patients in order to predict their survival. However, pattern discovery algorithms generate a large number of patterns, of which only some are relevant for classification. The interpretability of the model is, moreover, an essential property in the clinical domain. OBJECTIVE We propose to use the Diagnostic Odds Ratio (DOR) to select the multivariate sequential patterns used in the classification in a clinical domain, rather than employing frequency properties. This makes it possible to employ a terminology closer to the language of clinicians, in which a pattern is considered to be a risk factor or to have a protection factor. METHODS We employ data obtained from the ICBU at the University Hospital of Getafe, where six temporal variables for 465 patients were registered every day during 5 days, and to model the evolution of these clinical variables we use multivariate sequential patterns. We compare four ways in which to employ the DOR for pattern selection: 1) We use it as a threshold in order to select patterns with a minimum DOR; 2) We select patterns whose differential DORs are higher than a threshold as regards their extensions; 3) We select patterns whose DOR confidence intervals do not overlap; and 4) We propose the combination of threshold and non-overlapping confidence intervals in order to select the most discriminative patterns. As a baseline, we compare our proposals with Jumping Emerging Patterns (JEPs), one of the most frequently used techniques for pattern selection that utilize frequency properties. RESULTS We have compared the number and length of the patterns eventually selected, classification performance, and pattern and model interpretability. We show that discretization has a great impact on the accuracy of the classification model, but that a trade off must be found between classification accuracy and the physicians' capacity to interpret the patterns obtained. We have, therefore, opted to use expert discretization without losing too much accuracy. We have also identified that the experiments combining threshold and non-overlapping confidence intervals (Option 4) obtained the fewest number of patterns but also with the smallest size, thus implying the loss of an acceptable accuracy as regards clinician interpretation. CONCLUSIONS A method for the classification of patients’ survival can benefit from the use of sequential patterns, since these patterns consider knowledge about the temporal evolution of the variables in the case of ICBU. We have proved that the DOR can be used in several ways, and that it is a suitable measure with which to select discriminative and interpretable quality patterns.

Evaluating and extending the Informed Consent Ontology for representing permissions from the clinical domain

2022 ◽  
pp. 1-16
Author(s):  
Elizabeth E. Umberfield ◽  
Cooper Stansbury ◽  
Kathleen Ford ◽  
Yun Jiang ◽  
Sharon L.R. Kardia ◽  
...  
Keyword(s):  
Informed Consent ◽  
Formative Evaluation ◽  
Health Data ◽  
Federal Regulations ◽  
Biomedical Ontologies ◽  
Evaluation Design ◽  
Consent Forms ◽  
Critical Gap ◽  
Object Properties ◽  
Clinical Domain

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

Neuropsychologist, Psychologist, 3 years’ experience, USA

2020 ◽  
pp. 59-62
Author(s):  
Markus Reuber ◽  
Gregg H. Rawlings ◽  
Steven C. Schachter
Keyword(s):  
Large Scale ◽  
Structural Integrity ◽  
Small Scale ◽  
Brain Functioning ◽  
Clear Explanation ◽  
Neurological Problem ◽  
Psychological Origin ◽  
The Brain ◽  
Connection Problems ◽  
Clinical Domain

This chapter examines how, when treating individuals with Psychogenic Non-Epileptic Events (PNEE), a neuropsychologist operates from a viewpoint of PNEE as a “psycho-neurological” problem. The distinction between a “neurological” and “psychological” origin has important implications for the training necessary to perform the services offered by each specialist, but both specialties actually refer to the same origin of the PNEE. In both cases PNEE clearly arise from the brain. The neuropsychologist views these spheres not as separate and distinct, but rather as being on a continuous spectrum, with the major difference being the scale and scope at which the brain is considered. Neurological problems are typically viewed as large scale, concretely identifiable, and dealing with fundamental functioning and structural integrity. Psychological problems are viewed as small-scale neurological connection problems, too granular for the primary attention of neurologists. This is the clinical domain of psychiatrists, psychologists, and neuropsychologists. PNEE clearly have their origins in the brain, falling within the sphere of small-scale connection problems of brain functioning. Finally this chapter considers the lack of a clear explanation for why and how PNEE manifest in the brain because their true origin remains elusive.

Iterative heuristic design of temporal graphic displays with clinical domain experts

2020 ◽  
Author(s):  
Thomas J. Reese ◽  
Noa Segall ◽  
Guilherme Del Fiol ◽  
Joseph E. Tonna ◽  
Kensaku Kawamoto ◽  
...  
Keyword(s):  
Domain Experts ◽  
Graphic Displays ◽  
Clinical Domain

Clinical sublanguages

Terminology ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 41-65 ◽  
Author(s):  
Leonie Grön ◽  
Ann Bertels
Keyword(s):  
Language Use ◽  
Term Weighting ◽  
Semantic Composition ◽  
Fine Grained ◽  
Term Variation ◽  
Automatic Term Recognition ◽  
Clinical Records ◽  
Clinical Domain

Abstract Due to its specific linguistic properties, the language found in clinical records has been characterized as a distinct sublanguage. Even within the clinical domain, though, there are major differences in language use, which has led to more fine-grained distinctions based on medical fields and document types. However, previous work has mostly neglected the influence of term variation. By contrast, we propose to integrate the potential for term variation in the characterization of clinical sublanguages. By analyzing a corpus of clinical records, we show that the different sections of these records vary systematically with regard to their lexical, terminological and semantic composition, as well as their potential for term variation. These properties have implications for automatic term recognition, as they influence the performance of frequency-based term weighting.

scholarly journals Temporal Annotation in the Clinical Domain

2014 ◽  
Vol 2 ◽  
pp. 143-154 ◽  
Author(s):  
William F. Styler ◽  
Steven Bethard ◽  
Sean Finan ◽  
Martha Palmer ◽  
Sameer Pradhan ◽  
...  
Keyword(s):  
Formal Specification ◽  
Temporal Information ◽  
Clinical Notes ◽  
Annotation Guideline ◽  
Temporal Annotation ◽  
Clinical Domain

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.

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