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Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's…

Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in…

With the advancement in drug development, multiple treatments are available for a single disease. Patients can often benefit from taking multiple treatments simultaneously. For example, patients in Clinical Practice Research Datalink (CPRD)…

In this paper, machine learning models are used to predict outcomes for patients with persistent post-concussion syndrome (PCS). Patients had sustained a concussion at an average of two to three months before the study. By utilizing…

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms,…

In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's…

Optimal treatment rules can improve health outcomes on average by assigning a treatment associated with the most desirable outcome to each individual. Due to an unknown data generation mechanism, it is appealing to use flexible models to…

Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used…

The diagnosed cases of Breast cancer is increasing annually and unfortunately getting converted into a high mortality rate. Cancer, at the early stages, is hard to detect because the malicious cells show similar properties (density) as…

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer…

Brain tumors analysis is important in timely diagnosis and effective treatment to cure patients. Tumor analysis is challenging because of tumor morphology like size, location, texture, and heteromorphic appearance in the medical images. In…

The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). Articles were searched for…

We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm. Semi-supervised learning allows a smaller labelled data-set…

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature based machine learning method for breast cancer detection to improve the performance beyond a…

In the realm of medical science, the pursuit of enhancing treatment efficacy and patient outcomes continues to drive innovation. This study delves into the integration of biomimicry principles within the domain of Radiation Therapy (RT) to…

The Precision Medicine Initiative states that treatments for a patient should take into account not only the patient's disease, but his/her specific genetic variation as well. The vast biomedical literature holds the potential for…

In this paper, a long-term survival model under competing risks is considered. The unobserved number of competing risks is assumed to follow a negative binomial distribution that can capture both over- and under-dispersion. Considering the…

Acute lymphoblastic leukemia is the most frequent pediatric cancer. Approximately two third of survivors develop one or more health complications known as late adverse effects following their treatments. The existing measures offered to…

The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the…

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective…