Leveraging Latent Dirichlet Allocation in processing free-text personal goals among patients undergoing bladder cancer surgery

Purpose As we begin to leverage Big Data in health care settings and particularly in assessing patient-reported outcomes, there is a need for novel analytics to address unique challenges. One such challenge is in coding transcribed interview data, typically free-text entries of statements made durin...

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Bibliographic Details
Published in:Quality of life research Vol. 28; no. 6; pp. 1441 - 1455
Main Authors: Li, Yuelin, Rapkin, Bruce, Atkinson, Thomas M., Schofield, Elizabeth, Bochner, Bernard H.
Format: Journal Article
Language:English
Published: Cham Springer Science + Business Media 01.06.2019
Springer International Publishing
Springer Nature B.V
Subjects:
Big Data
Bladder cancer
Cancer surgery
CLINICAL AND POLICY APPLICATIONS
Medicine
Medicine & Public Health
Objectives
Public Health
Quality of Life Research
Sociology
Latent Dirichlet Allocation
Big Data analysis
Text analysis
Bladder cancer
Qualitative data
ISSN:0962-9343, 1573-2649, 1573-2649
Online Access:Get full text
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Summary:Purpose As we begin to leverage Big Data in health care settings and particularly in assessing patient-reported outcomes, there is a need for novel analytics to address unique challenges. One such challenge is in coding transcribed interview data, typically free-text entries of statements made during a face-to-face interview. Latent Dirichlet Allocation (LDA) offers statistical rigor and consistency in automating the interpretation of patients’ expressed concerns and coping strategies. Methods LDA was applied to interview data collected as part of a prospective, longitudinal study of QOL in N  = 211 patients undergoing radical cystectomy and urinary diversion for bladder cancer. LDA analyzed personal goal statements to extract the latent topics and themes, stratified by time, and on things patients wanted to accomplish and prevent. Model comparison metrics determined the number of topics to extract. Results LDA extracted seven latent topics. Prior to surgery, patients’ priorities were primarily in cancer surgery and recovery. Six months after the surgery, they were replaced by goals on regaining a sense of normalcy, to resume work, to enjoy life more fully, and to appreciate friends and family more. LDA model parameters showed changing priorities, e.g., immediate concerns on surgery and resuming employment decreased post-surgery and were replaced by concerns over cancer recurrence and a desire to remain healthy and strong. Conclusions Novel Big Data analytics such as LDA offer the possibility of summarizing personal goals without the need for conventional fixed-length measures and resource-intensive qualitative data coding.
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ISSN:0962-9343
1573-2649
1573-2649
DOI:10.1007/s11136-019-02132-w