TL-Lite: Temporal Visualization and Learning for Clinical Forecasting

Jeremy C. Weiss
Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 136:397-414, 2020.

Abstract

Clinical data extraction is a necessary step for quantitative analysis in clinical research. Whereas most machine learning algorithms learn from fixed length or regularly-collected panel data, health records data are neither. To facilitate the development of transparent and reproducible machine learning models from such data, we introduce TL-Lite, a clinical data ingestion, transformation, and visualization tool for conducting temporal machine learning. The central principle behind TL-Lite is to provide visual responsiveness at the individual level alongside management of the desired transformations behind the scenes that go on to be applied throughout the cohort and that result in cohort-level summaries, statistics, models and predictions. Characterization of the tool, discussion of design choices, and examples of use demonstrate its added value. A demo is provided at \url{https://www.andrew.cmu.edu/user/jweiss2/viz.html}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v136-weiss20a, title = {TL-Lite: Temporal Visualization and Learning for Clinical Forecasting}, author = {Weiss, Jeremy C.}, booktitle = {Proceedings of the Machine Learning for Health NeurIPS Workshop}, pages = {397--414}, year = {2020}, editor = {Alsentzer, Emily and McDermott, Matthew B. A. and Falck, Fabian and Sarkar, Suproteem K. and Roy, Subhrajit and Hyland, Stephanie L.}, volume = {136}, series = {Proceedings of Machine Learning Research}, month = {11 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v136/weiss20a/weiss20a.pdf}, url = {https://proceedings.mlr.press/v136/weiss20a.html}, abstract = {Clinical data extraction is a necessary step for quantitative analysis in clinical research. Whereas most machine learning algorithms learn from fixed length or regularly-collected panel data, health records data are neither. To facilitate the development of transparent and reproducible machine learning models from such data, we introduce TL-Lite, a clinical data ingestion, transformation, and visualization tool for conducting temporal machine learning. The central principle behind TL-Lite is to provide visual responsiveness at the individual level alongside management of the desired transformations behind the scenes that go on to be applied throughout the cohort and that result in cohort-level summaries, statistics, models and predictions. Characterization of the tool, discussion of design choices, and examples of use demonstrate its added value. A demo is provided at \url{https://www.andrew.cmu.edu/user/jweiss2/viz.html}. } }
Endnote
%0 Conference Paper %T TL-Lite: Temporal Visualization and Learning for Clinical Forecasting %A Jeremy C. Weiss %B Proceedings of the Machine Learning for Health NeurIPS Workshop %C Proceedings of Machine Learning Research %D 2020 %E Emily Alsentzer %E Matthew B. A. McDermott %E Fabian Falck %E Suproteem K. Sarkar %E Subhrajit Roy %E Stephanie L. Hyland %F pmlr-v136-weiss20a %I PMLR %P 397--414 %U https://proceedings.mlr.press/v136/weiss20a.html %V 136 %X Clinical data extraction is a necessary step for quantitative analysis in clinical research. Whereas most machine learning algorithms learn from fixed length or regularly-collected panel data, health records data are neither. To facilitate the development of transparent and reproducible machine learning models from such data, we introduce TL-Lite, a clinical data ingestion, transformation, and visualization tool for conducting temporal machine learning. The central principle behind TL-Lite is to provide visual responsiveness at the individual level alongside management of the desired transformations behind the scenes that go on to be applied throughout the cohort and that result in cohort-level summaries, statistics, models and predictions. Characterization of the tool, discussion of design choices, and examples of use demonstrate its added value. A demo is provided at \url{https://www.andrew.cmu.edu/user/jweiss2/viz.html}.
APA
Weiss, J.C.. (2020). TL-Lite: Temporal Visualization and Learning for Clinical Forecasting. Proceedings of the Machine Learning for Health NeurIPS Workshop, in Proceedings of Machine Learning Research 136:397-414 Available from https://proceedings.mlr.press/v136/weiss20a.html.

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