AIMC Topic: Precision Medicine

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Utilizing Big Data in Cancer Care.

Surgical oncology clinics of North America
Clinical research has boomed over the past decade, with the development of multiple clinical datasets that are available for retrospective review. However, data remain incomplete based on fragmented reporting, provider change, and loss of follow-up. ...

Artificial intelligence and machine learning in emergency medicine.

Emergency medicine Australasia : EMA
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in ...

Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms.

Statistics in medicine
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 with chronic dis...

Chemical-induced disease relation extraction with dependency information and prior knowledge.

Journal of biomedical informatics
Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have ...

Enabling Precision Medicine through Integrative Network Models.

Journal of molecular biology
A key challenge in precision medicine lies in understanding molecular-level underpinnings of complex human disease. Biological networks in multicellular organisms can generate hypotheses about disease genes, pathways, and their behavior in disease-re...

Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension.

Current hypertension reports
PURPOSE OF REVIEW: Evidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hyperte...

Information-Based Medicine in Glioma Patients: A Clinical Perspective.

Computational and mathematical methods in medicine
Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical managemen...

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature.

Scientific reports
In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a cha...

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care.

IEEE journal of biomedical and health informatics
The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal...

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Statistics in medicine
Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning...