An Introduction to Personalized Medicine

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

BACKGROUND

Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors.

OBJECTIVE

This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data.

METHODS

This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks.

RESULTS

The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094).

CONCLUSIONS

By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

Platform technologies for molecular diagnostics near the patient's bedside

It is believed Lab-on-Chip systems will become a mainstream technology within the next centuries. Especially because of new findings in molecular medicine and global trends such as the changing global population in third world countries and an ageing population in industrial countries, the need for fast and reliable diagnostics is rising tremendously. Hence, diagnostics have to become more frequently and more easily available. In this regard, technologies have to be found that enable the cost-effective production and hence an affordable price. In a joint-project between seven Fraunhofer institutes a Lab-on-Chip system was developed which consists of a credit-card-sized cartridge and a base station. The cartridges contain besides the reagents necessary for a specific assay also functionalities such as pumping or heating enabling a self-contained system without any fluidic interfaces, which tend to be error-prone. Because of the modularity of the system it is possible to integrate an optical sensor as well an electrochemical sensor into the cartridge. Hence, the system can be customized to serve the needs of the particular assays. This chapter will describe the system including generic design rules for such Lab-on-Chip systems, the development of these rules into a modular Lab-on-Chip system, the integration of biomedical assays, and the production possibility of this system.