Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Snapshot: Trial Types in Precision Medicine.

Integrating precision diagnostics into personalized treatments requires understanding how biomarkers relate to clinical outcomes. Various clinical data collection methods exist, each with strengths and weaknesses. Interventional data are high quality but narrowly focused. Real-world data (RWD) provide broader information but with variable quality. Master protocols allow better efficiency in data collection. The master observational trial bridges the gap between interventional and retrospective RWD collection methods. To view this SnapShot, open or download the PDF

Precision Medicine

This colligated Special Issue of Pharmaceutics on Precision Medicine: Applied Concepts of Pharmacogenomics in Patients with Various Diseases and Polypharmacy offers to the reader a series of articles that describe the concept of Precision Medicine, discuss its implementation process and limitations, demonstrate its value by illustrating some clinical cases, and open the door to new and more sophisticated techniques and applications

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine

Empowering precision medicine through high performance computing clusters

The role of High Performance Computing (HPC) in Medicine is greatly increase in these last years, moving from basic research to the clinics. With the advent of Next Generation Sequencing (NGS) technologies, diverse areas of human health have been investigated through different omics techniques. The extensive use of these NGS platforms to high throughput profile human health issues in a cost-efficient manner, is generating huge amount of sequencing data pushing " (https://www.facebook.com/pages/Oatext/1439466783004774) # $ % (https://www.youtube.com/user/users/oatext) ○ ○ ○ Article Article Info Author Info Figures & Data bioinformatic research in the big-data field. Speed, accuracy and reproducibility of massively sequencing analysis have allowed to transfer molecular biology knowledge into precision medicine. Furthermore, Molecular Dynamics (MD) earned a great importance in aiding genome research. Sequencing studies of cancer have allowed to detect and characterize mutated genes that drive tumorigenesis. As a complementary approach, from a biophysical perspective, MD simulations, executed on HPC architectures, have permitted to investigate the role played by pathological mutations on the molecular mechanism of activation

Developing precision stroke imaging.

Stroke experts stand at the cusp of a unique opportunity to advance the care of patients with cerebrovascular disorders across the globe through improved imaging approaches. NIH initiatives including the Stroke Progress Review Group promotion of imaging in stroke research and the newly established NINDS Stroke Trials network converge with the rapidly evolving concept of precision medicine. Precision stroke imaging portends the coming shift to individualized approaches to cerebrovascular disorders where big data may be leveraged to identify and manage stroke risk with specific treatments utilizing an improved neuroimaging infrastructure, data collection, and analysis. We outline key aspects of the stroke imaging field where precision medicine may rapidly transform the care of stroke patients in the next few years