Artificial intelligence based personalized predictive survival among colorectal cancer patients

Article Health Care Sciences & Services

iCOVID: interpretable deep learning framework for early recovery-time prediction of COVID-19 patients

Jun Wang et al.

Summary: The study reveals that treatment schemes, age, symptoms, comorbidities, and biomarkers are highly related to the prediction of recovery time for COVID-19 patients.

NPJ DIGITAL MEDICINE (2021)

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Review Biochemistry & Molecular Biology

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Antonio Jesus Banegas-Luna et al.

Summary: Artificial intelligence has made astonishing progress in medicine, especially in cancer diagnosis and therapies. Machine learning models and in-silico tools are widely used, yet the interpretability of machine learning predictions for doctors is still an issue that needs improvement.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2021)

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Article Multidisciplinary Sciences

Long-term cancer survival prediction using multimodal deep learning

Luis A. Vale-Silva et al.

Summary: MultiSurv is a multimodal deep learning method for long-term pan-cancer survival prediction that can handle missing data and achieve accurate results, as well as provide insights on cancer characteristics and heterogeneity through visualizations of learned multimodal representations.

SCIENTIFIC REPORTS (2021)

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Article Medicine, General & Internal

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer

Qianyu Yuan et al.

Summary: This study utilized electronic health records (EHRs) to construct a lung cancer cohort, providing a powerful platform for clinical outcome studies. The results demonstrate that EHR data can be used for lung cancer prognosis research, with reliable and accurate outcomes.

JAMA NETWORK OPEN (2021)

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Article Medicine, General & Internal

Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer

Lauren J. Beesley et al.

Summary: Recent insights into oropharyngeal cancer have led to the development of a bayesian multistate model for individualized predictions of survival and recurrence outcomes. The model, incorporating clinical, oncologic, and imaging data, demonstrated good discrimination in predicting 5-year overall survival. It also showed promising results in estimating and discriminating locoregional recurrence from distant metastases, providing personalized predictions for patients and clinicians.

JAMA NETWORK OPEN (2021)

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Article Health Care Sciences & Services

Interpretable survival prediction for colorectal cancer using deep learning

Ellery Wulczyn et al.

Summary: A deep learning system was developed for predicting disease-specific survival in stage II and III colorectal cancer, showing good performance on validation sets. By clustering embeddings, human-interpretable histologic features were generated to better explain the predictive changes of the model.

NPJ DIGITAL MEDICINE (2021)

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Article Gastroenterology & Hepatology