6 research outputs found
Enabling scalable clinical interpretation of ML-based phenotypes using real world data
The availability of large and deep electronic healthcare records (EHR)
datasets has the potential to enable a better understanding of real-world
patient journeys, and to identify novel subgroups of patients. ML-based
aggregation of EHR data is mostly tool-driven, i.e., building on available or
newly developed methods. However, these methods, their input requirements, and,
importantly, resulting output are frequently difficult to interpret, especially
without in-depth data science or statistical training. This endangers the final
step of analysis where an actionable and clinically meaningful interpretation
is needed.This study investigates approaches to perform patient stratification
analysis at scale using large EHR datasets and multiple clustering methods for
clinical research. We have developed several tools to facilitate the clinical
evaluation and interpretation of unsupervised patient stratification results,
namely pattern screening, meta clustering, surrogate modeling, and curation.
These tools can be used at different stages within the analysis. As compared to
a standard analysis approach, we demonstrate the ability to condense results
and optimize analysis time. In the case of meta clustering, we demonstrate that
the number of patient clusters can be reduced from 72 to 3 in one example. In
another stratification result, by using surrogate models, we could quickly
identify that heart failure patients were stratified if blood sodium
measurements were available. As this is a routine measurement performed for all
patients with heart failure, this indicated a data bias. By using further
cohort and feature curation, these patients and other irrelevant features could
be removed to increase the clinical meaningfulness. These examples show the
effectiveness of the proposed methods and we hope to encourage further research
in this field.Comment: 27 pages, 14 figure
Wearable-Based Affect Recognition—A Review
Affect recognition is an interdisciplinary research field bringing together researchers from natural and social sciences. Affect recognition research aims to detect the affective state of a person based on observables, with the goal to, for example, provide reasoning for the person’s decision making or to support mental wellbeing (e.g., stress monitoring). Recently, beside of approaches based on audio, visual or text information, solutions relying on wearable sensors as observables, recording mainly physiological and inertial parameters, have received increasing attention. Wearable systems enable an ideal platform for long-term affect recognition applications due to their rich functionality and form factor, while providing valuable insights during everyday life through integrated sensors. However, existing literature surveys lack a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods and best practices of wearable affect and stress recognition. Following a summary of different psychological models, we detail the influence of affective states on the human physiology and the sensors commonly employed to measure physiological changes. Then, we outline lab protocols eliciting affective states and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain and review common approaches related to the preprocessing, feature extraction and classification steps. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field to conduct and evaluate user studies and develop wearable systems