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 duri...
| Publicado en: | Quality of Life Research Vol. 28; no. 6; pp. 1441 - 1456 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136504684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136504684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09629343 GPQ jtl: Quality of Life Research issn: 09629343 maglogo: N pubinfo: dt: Jun2019 vid: 28 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136504684 136504684 NLM30798421 10.1007/s11136-019-02132-w NLM30798421 136504684 ppf: 1441 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging Latent Dirichlet Allocation in processing free-text personal goals among patients undergoing bladder cancer surgery. aug: au: Li, Yuelin Rapkin, Bruce Atkinson, Thomas M. Schofield, Elizabeth Bochner, Bernard H. affil: Department of Psychiatry & Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA sug: subj: Algorithms Urinary Diversion Psychosocial Factors Bladder Neoplasms Psychosocial Factors Cystectomy Psychosocial Factors Quality of Life Psychosocial Factors Bladder Neoplasms Surgery Adult Neoplasm Recurrence, Local Pathology Aged Treatment Outcomes Prospective Studies Female Goals and Objectives Aged, 80 and Over Bladder Pathology Middle Age Coping Strategies Questionnaire Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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