scholarly journals Using wearable technology to predict health outcomes: a literature review

2018 ◽  
Vol 25 (9) ◽  
pp. 1221-1227 ◽  
Author(s):  
Jason P Burnham ◽  
Chenyang Lu ◽  
Lauren H Yaeger ◽  
Thomas C Bailey ◽  
Marin H Kollef
Keyword(s):  
Clinical Outcome ◽  
Health Outcomes ◽  
Clinical Outcomes ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Research Question ◽  
Wearable Technology ◽  
Cochrane Library ◽  
Moderate Quality ◽  
Clinical Outcome Prediction

Abstract Objective To review and analyze the literature to determine whether wearable technologies can predict health outcomes. Materials and methods We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits. Results Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome. Discussion Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes. Conclusion Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology.

The PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort: Leveraging the potential of electronic health records in the treatment of mental disorders

2021 ◽  
pp. 1-27
Author(s):  
Lasse Hansen ◽  
Kenneth C. Enevoldsen ◽  
Martin Bernstorff ◽  
Kristoffer L. Nielbo ◽  
Andreas A. Danielsen ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Mental Illness ◽  
Clinical Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Health Records ◽  
Machine Learning Methods ◽  
Clinical Outcome Prediction

Abstract Background The quality of life and lifespan are greatly reduced among individuals with mental illness. To improve prognosis, the nascent field of precision psychiatry aims to provide personalized predictions for the course of illness and response to treatment. Unfortunately, the results of precision psychiatry studies are rarely externally validated, almost never implemented in clinical practice, and tend to focus on a few selected outcomes. To overcome these challenges, we have established the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, which will form the basis for extensive studies in the upcoming years. Methods PSYCOP is a retrospective cohort study that includes all patients with at least one contact with the psychiatric services of the Central Denmark Region in the period from January 1, 2011 to October 28, 2020 (n=119,291). All data from the electronic health records (EHR) are included, spanning diagnoses, information on treatments, clinical notes, discharge summaries, laboratory tests etc. Based on these data, machine learning methods will be used to make prediction models for a range of clinical outcomes, such as diagnostic shifts, treatment response, medical comorbidity, and premature mortality, with an explicit focus on clinical feasibility and implementation. Discussion We expect that studies based on the PSYCOP cohort will advance the field of precision psychiatry through the use of state-of-the-art machine learning methods on a large and representative dataset. Implementation of prediction models in clinical psychiatry will likely improve treatment and, hopefully, increase the quality of life and lifespan of those with mental illness.

scholarly journals Mutational monitoring of EGFR T790M in cfDNA for clinical outcome prediction in EGFR-mutant lung adenocarcinoma

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207001 ◽  
Author(s):  
Kang-Yi Su ◽  
Jeng-Sen Tseng ◽  
Keng-Mao Liao ◽  
Tsung-Ying Yang ◽  
Kun-Chieh Chen ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Lung Adenocarcinoma ◽  
Outcome Prediction ◽  
Clinical Outcome Prediction ◽  
Egfr Mutant ◽  
Egfr T790m

Training radiomics-based CNNs for clinical outcome prediction: Challenges, strategies and findings

2022 ◽  
Vol 123 ◽  
pp. 102230
Author(s):  
Shuchao Pang ◽  
Matthew Field ◽  
Jason Dowling ◽  
Shalini Vinod ◽  
Lois Holloway ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Outcome Prediction ◽  
Clinical Outcome Prediction

Bayesian clinical classification from high-dimensional data: Signatures versus variability

2016 ◽  
Vol 27 (2) ◽  
pp. 336-351 ◽  
Author(s):  
Akram Shalabi ◽  
Masato Inoue ◽  
Johnathan Watkins ◽  
Emanuele De Rinaldis ◽  
Anthony CC Coolen
Keyword(s):  
Clinical Outcome ◽  
Bayesian Methods ◽  
Outcome Prediction ◽  
High Dimensional Data ◽  
Genomic Data ◽  
Bayesian Prediction ◽  
High Dimensional ◽  
Clinical Classification ◽  
Numerical Integrations ◽  
Clinical Outcome Prediction

When data exhibit imbalance between a large number d of covariates and a small number n of samples, clinical outcome prediction is impaired by overfitting and prohibitive computation demands. Here we study two simple Bayesian prediction protocols that can be applied to data of any dimension and any number of outcome classes. Calculating Bayesian integrals and optimal hyperparameters analytically leaves only a small number of numerical integrations, and CPU demands scale as O(nd). We compare their performance on synthetic and genomic data to the mclustDA method of Fraley and Raftery. For small d they perform as well as mclustDA or better. For d = 10,000 or more mclustDA breaks down computationally, while the Bayesian methods remain efficient. This allows us to explore phenomena typical of classification in high-dimensional spaces, such as overfitting and the reduced discriminative effectiveness of signatures compared to intra-class variability.

scholarly journals A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction

2015 ◽  
Vol 61 (1) ◽  
pp. 227-242 ◽  
Author(s):  
Arman Rahmim ◽  
C Ross Schmidtlein ◽  
Andrew Jackson ◽  
Sara Sheikhbahaei ◽  
Charles Marcus ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Outcome Prediction ◽  
Clinical Outcome Prediction
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