title: Application of Text Mining in Cancer Symptom Management.
abstract: Fatigue continues to be one of the main symptoms that afflict ovarian cancer patients and negatively affects their functional status and quality of life. To manage fatigue effectively, the symptom must be understood from the perspective of patients. We utilized text mining to understand the symptom experiences and strategies that were associated with fatigue among ovarian cancer patients. Through text analysis, we determined that descriptors such as energetic, challenging, frustrating, struggling, unmanageable, and agony were associated with fatigue. Descriptors such as decadron, encourager, grocery, massage, relaxing, shower, sleep, zoloft, and church were associated with strategies to ameliorate fatigue. This study demonstrates the potential of applying text mining in cancer research to understand patients' perspective on symptom management. Future study will consider various factors to refine the results.
authors.affiliation: School of Nursing, University of Pittsburgh, Pittsburgh, USA.
authors.forename: Young Ji
authors.initials: YJ
authors.lastname: Lee
authors.affiliation: School of Nursing, University of Pittsburgh, Pittsburgh, USA.
authors.forename: Heidi
authors.initials: H
authors.lastname: Donovan
mesh_terms: D057225:Data Mining; D005221:Fatigue / Q000209:etiology / Q000628:therapy*; D005260:Female; D006801:Humans; D010051:Ovarian Neoplasms / Q000150:complications / Q000628:therapy*; D011788:Quality of Life