Deep Learning-based Time-to-event Analysis of Depression and Asthma using the All of Us Research Program.

Xueting Wang, Lucila Ohno-Machado, Jose L Gomez, Wen Gu, Rongyi Sun, Jihoon Kim

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xueting WangSection of Biomedical Informatics and Data Science.

Lucila Ohno-MachadoSection of Biomedical Informatics and Data Science.

Jose L GomezSection of Pulmonary, Critical Care Medicine and Sleep and Dept of Internal Medicine, Yale School of Medicine, New Haven, CT 06510.

Wen GuDepartment of Respiratory Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.

Rongyi SunNewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY.

Jihoon KimSection of Biomedical Informatics and Data Science.

Funding

Genetic & Social Determinants of Health: Center for Admixture Science and TechnologyRM1HG011558 · NHGRI · YALE UNIVERSITY · PI FRAZER, KELLY A, GYMREK, MELISSA · 2021 to 2025

$11.2M

NHGRI NIH HHS RM1 HG011558

6 · The paper itself

Abstract

While there is a growing recognition of the association between depression and asthma, few studies have leveraged deep learning-based (DL-based) models in a retrospective cohort study with a large sample size. We analyzed the association between depression and asthma among 239,161 participants of the All of Us Research Program through DL-based, logistic regression, and Cox Proportional Hazards (CoxPH) models. We used SHAP values to help interpret DL-based models and c-index to evaluate model performance. Results suggest a significant odds ratio for depression in asthma. The c-indices for the CoxPH, DeepSurv, and DeepHit models were 0.619, 0.625, and 0.596, respectively. SHAP indicated a different set of important variables when compared with the CoxPH model. In conclusion, we provide strong evidence of a positive relationship between depression and asthma. Also, DL-based models did not outperform the CoxPH model on the c-index. Sex at birth and income may play important roles in occurrence of depression in asthma patients.

Indexed as

AsthmaDeep LearningDepressionAdultFemaleHumansLogistic ModelsMaleMiddle AgedProportional Hazards ModelsRetrospective StudiesUnited States

Identifiers

PMID40417537

PMCPMC12099346

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.