Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Jieyi Li; Ognjen Arandelovic

doi:10.1109/EMBC.2017.8037782

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Jieyi Li, Ognjen Arandelovic

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

Original language	English
Title of host publication	2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Publisher	IEEE
Pages	4199-4202
DOIs	https://doi.org/10.1109/EMBC.2017.8037782
Publication status	Published - 11 Jul 2017
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - International Conference Centre (ICC), Jeju Island, Korea, Democratic People's Republic of Duration: 11 Jul 2017 → 15 Jul 2017 Conference number: 38 https://embc.embs.org/2017/

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Abbreviated title	EMBC
Country/Territory	Korea, Democratic People's Republic of
City	Jeju Island
Period	11/07/17 → 15/07/17
Internet address	https://embc.embs.org/2017/

Access to Document

10.1109/EMBC.2017.8037782

2017_EMBC_paper3
© 2017, IEEE. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/EMBC.2017.8037782
Accepted author manuscript, 939 KB

Cite this

@inproceedings{93dcdd4bc6d044a4a07c4c1379739c24,

title = "Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk",

abstract = "Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.",

author = "Jieyi Li and Ognjen Arandelovic",

year = "2017",

month = jul,

day = "11",

doi = "10.1109/EMBC.2017.8037782",

language = "English",

pages = "4199--4202",

booktitle = "2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)",

publisher = "IEEE",

note = "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, EMBC ; Conference date: 11-07-2017 Through 15-07-2017",

url = "https://embc.embs.org/2017/",

}

Li, J & Arandelovic, O 2017, Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk. in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)., 8037782, IEEE, pp. 4199-4202, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Democratic People's Republic of, 11/07/17. https://doi.org/10.1109/EMBC.2017.8037782

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk. / Li, Jieyi; Arandelovic, Ognjen.
2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. p. 4199-4202 8037782.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

AU - Li, Jieyi

AU - Arandelovic, Ognjen

N1 - Conference code: 38

PY - 2017/7/11

Y1 - 2017/7/11

N2 - Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

AB - Computer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.

U2 - 10.1109/EMBC.2017.8037782

DO - 10.1109/EMBC.2017.8037782

M3 - Conference contribution

SP - 4199

EP - 4202

BT - 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)

PB - IEEE

T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

Y2 - 11 July 2017 through 15 July 2017

ER -

Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Abstract

Conference

Access to Document

Fingerprint

Cite this