Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis

Motwani, Manish; Dey, Damini; Berman, Daniel S.; Germano, Guido; Achenbach, Stephan; Al‐Mallah, Mouaz H.; Andreini, Daniele; Budoff, Matthew J.; Cademartiri, Filippo; Callister, Tracy Q.; Chang, Hyuk Jae; Chinnaiyan, Kavitha M.; Chow, Benjamin J.W.; Cury, Ricardo C.; Delago, Augustin; Gomez, Millie; Gransar, Heidi; Hadamitzky, Martin; Hausleiter, Jöerg; Hindoyan, Niree; Feuchtner, Gudrun; Kaufmann, Philipp A.; Kim, Yong Jin; Leipsic, Jonathon; Lin, Fay Y.; Maffei, Erica; Marques, Hugo; Pontone, Gianluca; Raff, Gilbert; Rubinshtein, Ronen; Shaw, Leslee J.; Stehli, Julia; Villines, Todd C.; Dunning, Allison; Min, James K.; Slomka, Piotr J.

doi:10.1093/eurheartj/ehw188

Cited by 413 publications

(402 citation statements)

References 30 publications

Supporting

Mentioning

357

Contrasting

Unclassified

Order By: Relevance

“…All the tools described above, and those we reviewed [32] [33] [34] [35] [36], have at least one of the following limitations. They were either derived from small data sets (limited to specific studies or cohorts), or used too few variables (intentionally to make the model portable, or avoid overfitting), or the model was too simple to capture the complexities and subtleties of human health, or was limited to certain sub-populations (based on disease type, age etc.)…”

Section: Prognosis In the Age Of Big-datamentioning

confidence: 99%

Improving palliative care with deep learning

Avati

Jung

Harman

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

114

102

View full text Add to dashboard Cite

Abstract-Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life . We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.

show abstract

Section: Prognosis In the Age Of Big-datamentioning

confidence: 99%

Improving palliative care with deep learning

Avati

Jung

Harman

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

114

102

View full text Add to dashboard Cite

show abstract

“…Axes position error [59] Let's assume we have two objects having centroid ( 1 , 1 ) ( 2 , 2 ) and oriented on different angles …”

Section: B Axes Position Errormentioning

confidence: 99%

A Case Analysis on Different Registration Methods on Multi-modal Brain Images

Nathawat¹,

Mandot²,

Sharma³

2018

IJMECS

View full text Add to dashboard Cite

Abstract-Many applications of artificial vision need to compare or integrate images of the same object but obtained at different moments of time with different devices (cameras), from different positions, under different conditions, etc. These differences in capture give rise to images with important relative geometric differences that prevent these "Fit" with precision over each other.The registry eliminates these geometric differences so that located pixels in the same coordinates correspond to the same point of the object and, therefore, both images can easily be compared or integrated. The registration of images is essential in disciplines such as remote sensing, radiology, robotic vision, etc. ; Fields, all of them, that overlap images to study environmental phenomena, monitor tumours carcinogenic or to reconstruct the observed scene. This paper also study different measures of similarity used to measure their consistency and a novel procedure is proposed to improve the accuracy of the linear record by pieces. Specifically the elements that influence the estimation are analysed experimentally of probability distributions of the intensity levels of the images. These distributions are the basis for calculating measures of similarity based on entropy as mutual information (MI) or the Entropy correlation coefficient (ECC). Therefore, the effectiveness of these measures depends critically on their correct estimation.

show abstract

“…55 Furthermore, machine learning applications, integrating clinical, ECG, exercise, hemodynamic, defect quantification, and ancillary imaging data provide a patient-specific estimate of likelihood of early revascularization and allcause mortality, thus aiding in individualized decisionmaking in a way the human brain cannot do. 53,56 Machine learning algorithms are a natural complement to nuclear cardiology analyses packages and structured reporting software, from which multi-faceted data can be derived to generate risk estimates factored in DSTs and patient-centered decision guidance. …”

Section: Machine Learningmentioning

confidence: 99%