NDLI: Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning

Content Provider	IEEE Xplore Digital Library
Author	Mehrabi, S. Sunghwan Sohn Dingheng Li Pankratz, J.J. Therneau, T. St. Sauver, J.L. Hongfang Liu Palakal, M.
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. & Comput., Indiana Univ., Indianapolis, IN, USA (Palakal, M.) \|\| Dept. of Health Sci. Res., Mayo Clinic, Rochester, MN, USA (Mehrabi, S.; Sunghwan Sohn; Dingheng Li; Therneau, T.; St. Sauver, J.L.; Hongfang Liu) \|\| Div. of Inf. Manage. & Analytics, Mayo Clinic, Rochester, MN, USA (Pankratz, J.J.)
Abstract	Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.
Starting Page	408
Ending Page	416
File Size	1060340
Page Count	9
File Format	PDF
e-ISBN	9781467395489
DOI	10.1109/ICHI.2015.58
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temporal Pattern Discovery Rochester Epidemiology Project Sociology Machine learning Statistics Medical diagnostic imaging Deep Learning Cascading style sheets Diseases
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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