Evidence map›Paper›PMID 34957004›Full record

ArticleFrontiers in public health2021

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Tien Yun Yang, Pin-Yu Kuo, Yaoru Huang, Hsiao-Wei Lin, Shwetambara Malwade, Long-Sheng Lu, Lung-Wen Tsai, Shabbir Syed-Abdul, Chia-Wei Sun, Jeng-Fong Chiou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 17% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Tien Yun YangSchool of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Pin-Yu KuoBiomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yaoru HuangDepartment of Hospice and Palliative Care, Taipei Medical University Hospital, Taipei, Taiwan.
Hsiao-Wei LinDepartment of Hospice and Palliative Care, Taipei Medical University Hospital, Taipei, Taiwan.
Shwetambara MalwadeInternational Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Long-Sheng LuDepartment of Radiation Oncology, Taipei Medical University Hospital, Taipei, Taiwan.
Lung-Wen TsaiDepartment of Medical Research, Taipei Medical University Hospital, Taipei, Taiwan.
Shabbir Syed-AbdulInternational Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Chia-Wei SunBiomedical Optical Imaging Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Jeng-Fong ChiouDepartment of Hospice and Palliative Care, Taipei Medical University Hospital, Taipei, Taiwan.
Taipei Medical University · TWTaipei Medical University Hospital · TWNational Yang Ming Chiao Tung University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant component in survival prognostication. While current performance status evaluation tools are limited to their subjective nature, the advent of wearable technology enables continual recordings of patients' activity and has the potential to measure performance status objectively. We hypothesize that wristband actigraphy monitoring devices can predict in-hospital death of end-stage cancer patients during the time of their hospital admissions. The objective of this study was to train and validate a long short-term memory (LSTM) deep-learning prediction model based on activity data of wearable actigraphy devices. The study recruited 60 end-stage cancer patients in a hospice care unit, with 28 deaths and 32 discharged in stable condition at the end of their hospital stay. The standard Karnofsky Performance Status score had an overall prognostic accuracy of 0.83. The LSTM prediction model based on patients' continual actigraphy monitoring had an overall prognostic accuracy of 0.83. Furthermore, the model performance improved with longer input data length up to 48 h. In conclusion, our research suggests the potential feasibility of wristband actigraphy to predict end-of-life admission outcomes in palliative care for end-stage cancer patients.

Indexed as

Deep LearningNeoplasmsWearable Electronic DevicesActigraphyHospital MortalityHumansactigraphydeep learninglong short-term memory networkspalliative careperformance statusprognostic accuracysurvival predictionwearable technology

Identifiers

PMID34957004
PMCPMC8695752
OpenAlexW4200559760

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.