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<p>Cancer survivors often experience multiple cooccurring symptoms such as insomnia, pain, fatigue, and anxiety; yet conventional analyses in symptom science typically analyze symptoms one at a time and thus overlook putative clusters of shared symptom experiences. We applied a novel machine learning approach to supporting tailored symptom management to cooccurring symptoms. Bayesian nonparametric (BNP) clustering was applied to discover unique subgroups of symptom profiles in cancer survivors diagnosed with insomnia (<i>N</i> = 160) and with cooccurring pain, fatigue, and anxiety, using secondary symptom data from a clinical trial (clinicaltrials.gov: NCT02356575) comparing cognitive-behavioral therapy for insomnia (CBT-I) and acupuncture. BNP identified survivor subgroups by recognizing shared features in symptoms that contributed to heterogeneous treatment responses at 8 weeks. Simulations evaluated sensitivity to model assumptions. BNP identified three patient subgroups: (1) “insomnia-predominant” (<i>N</i> = 84) with high severity insomnia alone; (2) “insomnia &amp; pain” (<i>n</i> = 21) with high severity of both insomnia and pain; and (3) “high symptom burden” (<i>n</i> = 54) with high severity across all symptoms. CBT-I produced greater insomnia reduction among “insomnia-predominant” patients (posterior mean=-2.45, 95% Bayesian Highest Density Interval: −&#xa0;4.38, −&#xa0;0.35) and among “insomnia &amp; pain” patients (−&#xa0;2.66, 80% HDI: −&#xa0;4.50, −&#xa0;0.50). However, acupuncture produced greater pain reduction among “insomnia &amp; pain” patients (−&#xa0;1.47, 95% HDI: −&#xa0;2.79, −&#xa0;0.18). CBT-I and acupuncture were equally effective for all symptoms among the “high symptom burden” patients. Simulations showed that our main BNP settings accurately identified these subgroups. Unsupervised BNP learning supports interventions tailored to patients’ symptom burden and their main concerns. If further validated, BNP learning provides a roadmap for precision symptom management for cancer survivors, and broadly applicable in behavioral medicine data analysis.</p>

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Novel bayesian nonparametric unsupervised learning approach to precision symptom management in cancer survivors: a re-analysis of a comparative effectiveness trial

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Yuelin Li,
  • Kevin T. Liou,
  • Elizabeth Schofield,
  • Thomas M. Atkinson,
  • Jun J. Mao

摘要

Cancer survivors often experience multiple cooccurring symptoms such as insomnia, pain, fatigue, and anxiety; yet conventional analyses in symptom science typically analyze symptoms one at a time and thus overlook putative clusters of shared symptom experiences. We applied a novel machine learning approach to supporting tailored symptom management to cooccurring symptoms. Bayesian nonparametric (BNP) clustering was applied to discover unique subgroups of symptom profiles in cancer survivors diagnosed with insomnia (N = 160) and with cooccurring pain, fatigue, and anxiety, using secondary symptom data from a clinical trial (clinicaltrials.gov: NCT02356575) comparing cognitive-behavioral therapy for insomnia (CBT-I) and acupuncture. BNP identified survivor subgroups by recognizing shared features in symptoms that contributed to heterogeneous treatment responses at 8 weeks. Simulations evaluated sensitivity to model assumptions. BNP identified three patient subgroups: (1) “insomnia-predominant” (N = 84) with high severity insomnia alone; (2) “insomnia & pain” (n = 21) with high severity of both insomnia and pain; and (3) “high symptom burden” (n = 54) with high severity across all symptoms. CBT-I produced greater insomnia reduction among “insomnia-predominant” patients (posterior mean=-2.45, 95% Bayesian Highest Density Interval: − 4.38, − 0.35) and among “insomnia & pain” patients (− 2.66, 80% HDI: − 4.50, − 0.50). However, acupuncture produced greater pain reduction among “insomnia & pain” patients (− 1.47, 95% HDI: − 2.79, − 0.18). CBT-I and acupuncture were equally effective for all symptoms among the “high symptom burden” patients. Simulations showed that our main BNP settings accurately identified these subgroups. Unsupervised BNP learning supports interventions tailored to patients’ symptom burden and their main concerns. If further validated, BNP learning provides a roadmap for precision symptom management for cancer survivors, and broadly applicable in behavioral medicine data analysis.