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 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

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

Precision oncology: the intention-to-treat analysis fallacy.

It has recently been suggested that precision oncology studies should be reanalysed using the intention-to-treat (ITT) methodology developed for randomized controlled clinical trials. This reanalysis dramatically decreases response rates in precision medicine studies. We contend that the ITT analysis of precision oncology trials is invalid. The ITT methodology was developed three decades ago to mitigate the problems of randomized trials, which try to ensure that both arms have an unselected patient population free from confounders. In contrast, precision oncology trials specifically select patients for confounders (that is biomarkers) that predict response. To demonstrate the issues inherent in an ITT reanalysis for precision cancer medicine studies, we take as an example the drug larotrectinib (TRK inhibitor) approved because of remarkable responses in malignancies harbouring NTRK fusions. Based on large-scale studies, NTRK fusions are found in ~0.31% of tumours. In a non-randomized pivotal study of larotrectinib, 75% of the 55 treated patients responded. Based upon the prevalence of NTRK fusions, ~18,000 patients would need to be screened to enrol the 55 treated patients. Utilizing the ITT methodology, the revised response rate to larotrectinib would be 0.23%. This is, of course, a dramatic underestimation of the efficacy of this now Food and Drug Administration (FDA)-approved drug. Similar issues can be shown for virtually any biomarker-based precision clinical trial. Therefore, retrofitting the ITT analysis developed for unselected patient populations in randomized trials yields misleading conclusions in precision medicine studies

Precision Pharmacotherapy Enables Precision Medicine

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138280/1/phar1998_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138280/2/phar1998.pd

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