AIMC Topic: Proportional Hazards Models
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Interpretable machine learning models for survival prediction in prostate cancer bone metastases.
Scientific reports
Jul 6, 2025
Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing these typically incurable patients. However, existing clinical models lack precision. This study seeks t...
Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.
Scientific reports
Jul 2, 2025
The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. However, whether HRR can predict the mortality for pulmonary embolism (PE) patients remains ambiguous. A...
Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.
Scientific reports
Jul 2, 2025
Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific su...
AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.
Scientific reports
Jul 1, 2025
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...
Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.
Scientific reports
Jul 1, 2025
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...
Improving lung cancer diagnosis and survival prediction with deep learning and CT imaging.
PloS one
Jun 11, 2025
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model the non-linear relationship bet...
Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical features.
EBioMedicine
May 28, 2025
BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most ...
Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model.
BMC cancer
May 22, 2025
BACKGROUND: Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia ...
The establishment of machine learning prognostic prediction models for pineal region tumors based on SEER-A multicenter real-world study.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Apr 22, 2025
BACKGROUND: Pineal region tumors (PRT) are rare intracranial neoplasms with diverse pathological types and growth characteristics, leading to varied clinical manifestations. This study aims to develop machine learning (ML) models for survival predict...
Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.
BMC medical informatics and decision making
Apr 22, 2025
OBJECTIVE: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.
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