|Publication type:||Conference other|
|Type of review:||Not specified|
|Title:||Real and assumed insights : statistical models and imaging biomarkers for disease characterization of intracranial aneurysms|
|Conference details:||1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019|
|Subject (DDC):||005: Computer programming, programs and data |
616: Internal medicine and diseases
|Abstract:||Clinical data science is an emerging discipline, owing to recent developments in the acquisition, storage and processing of large amounts of clinical data. The increasing wealth of data, however, demands for interdisciplinary collaborations, which imposes new challenges. The need for properly dealing with selection biases and establishing balanced databases becomes a key issue to be addressed in the field of digital health. Pre-existing beliefs about the disease sometimes are badly supported by evidence. Wrong assumptions or selection biases, however, will skew the subsequent analyses and mislead the interpretation of results. Subjective or approximate assessments by clinicians, just like missing/censored information about the patients, the data acquisition process or the pathology under examination make data scientific approaches introduce uncertainty about the data to be processed. All this requires robust approaches and very good domain knowledge to avoid false predictions. To successfully reach the clinically relevant statements calls for transparent methods and efficient tools for creating and communicating insights to practitioners. This talk will present some of the experiences made throughout our research on intracranial aneurysms and discuss how we manage these challenges. It will touch the detection of visual biomarkers with machine learning, present an approach to quantifying rather vaguely defined subjective estimations and demonstrate how visualization helps to detect clinical pathways in high dimensional diagnostic data.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Computational Life Sciences (ICLS)|
|Published as part of the ZHAW project:||AneuX|
|Appears in collections:||Publikationen Life Sciences und Facility Management|
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