AIMC Topic: Cancer Survivors
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Development and Validation of a Novel Prediction Model for Hearing Loss From Cisplatin Chemotherapy.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
May 5, 2025
PURPOSE: Cisplatin treats many common tumors but causes permanent and debilitating hearing loss (HL). The objective of this study was to develop and externally validate a predictive model of HL in cisplatin-treated children and adolescent cancer surv...
Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis.
JMIR cancer
Apr 7, 2025
BACKGROUND: Cancer survivors and their caregivers, particularly those from disadvantaged backgrounds with limited health literacy or racial and ethnic minorities facing language barriers, are at a disproportionately higher risk of experiencing sympto...
A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.
JMIR cancer
Jan 27, 2025
BACKGROUND: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily li...
Analyzing Secondary Cancer Risk: A Machine Learning Approach.
Asian Pacific journal of cancer prevention : APJCP
Jan 1, 2025
OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors incl...
A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health.
International journal of environmental research and public health
Dec 19, 2024
BACKGROUND: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily function, and mortality. GI health screening is crucial for preventing and managing this distress. ...
Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review.
JCO clinical cancer informatics
Dec 2, 2024
PURPOSE: The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer survivors.
Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey.
International journal of medical informatics
Nov 14, 2024
OBJECTIVE: A major concern for cancer survivors after treatment is the Fear of Cancer Recurrence (FCR), which is the fear that cancer will reappear or progress. This fear can be exacerbated by medical uncertainty about the future, leading to harmful ...
Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.
International journal of medical informatics
Sep 14, 2024
BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhanceme...
Factors influencing psychological distress among breast cancer survivors using machine learning techniques.
Scientific reports
Jul 1, 2024
Breast cancer is the most commonly diagnosed cancer among women worldwide. Breast cancer patients experience significant distress relating to their diagnosis and treatment. Managing this distress is critical for improving the lifespan and quality of ...
Survival prediction in second primary breast cancer patients with machine learning: An analysis of SEER database.
Computer methods and programs in biomedicine
Jun 25, 2024
BACKGROUND: Studies have found that first primary cancer (FPC) survivors are at high risk of developing second primary breast cancer (SPBC). However, there is a lack of prognostic studies specifically focusing on patients with SPBC.
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