Big Data Analytics Platforms

So, early detection before disease onset is the optimal approach to complex non-communicational diseases (NCDs). The usage of big-data analysis platforms can offer the opportunity to trace individual health condition and release alert when some health parameter reaches the threshold of the normal range. And genomic analysis is much more related to personalized and precision medicine in current study. But translation information of big cohorts can guide significance for future diagnose of similar cohorts, like heritage family disease or another chronic disease.

For sensor information, wearable device, mobile application and real-time health monitor provide much more information of updated data related to the most recent health conditions of individuals. There are several factors that contribute to the rapid development of the wearable device, especially the rapid absorption of wearable devices.

These factors include improved data processing capabilities, faster wireless communications and higher bandwidth, and improved microelectronics and sensor device design. By providing a means to capture critical events and continue to simplify health information, implantable sensors can address the challenges of acute and chronic disease monitoring.

Not only are important symptom onset monitoring, but intelligent implantable sensors also offer promising techniques for monitoring postoperative complications such as tissue healing and slow infection. In addition, smart implants can also be used to treat chronic neurological diseases such as refractory epilepsy and Parkinson’s disease by providing drugs and brain stimulants for chronic pain.

The last one is about imaging information, before data-era, the value of medical images was not fully utilized. In recent years, the great success of big data in image recognition tasks has been related to the extensive use and sharing of electronic medical records and medical diagnostic imaging.

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The platform and method for big data analysis are applied to medical image analysis, focusing on convolutional neural networks, focusing on the clinical prediction and other applications of combining medical diagnostic images with big data. The advantage of the era of medical big data is that important hierarchical relationships and correlations in the data can be found algorithmically without the need for cumbersome and time-consuming manual segmentation.

Big data medical image analysis covers the key research areas and applications of medical image classification, positioning, detection, segmentation and registration. Even though the interpretation from professional physician can provide clinical diagnose to patients, after the interpretation, medical image has little value. With computer vision and data analysis, medical image like MRI or PET scan can be stored as huge datasets and be quantified to do further data analysis. For example, MRI of baby brain analysis, from Lab of Neuron Image (LONI), they are studying preterm baby cognitive development by using pre-term baby cross-sectional and longitudinal MRI scan at their gestation age and follow up longitudinal study. Medical image are unstructured data and the way to transform medical image to structured data that machine can read depends on the goal of ongoing study.

Normalization and registration and voxel analysis can make medical image provide more clinical significance with big data analysis. Big-data driven health plan management also contribute to personalized medicine. This is based on computational and analytical frameworks, extensive data collection and integration, similarity discovery and in-depth knowledge of important patient initiatives, and provides not only a personalized disease risk profile for each patient from the electronic medical record information, but also from several millions of other patients have similarities (Ker J, 2017). It is speculated that once all disease-associated mutations are cataloged, we will be able to predict individual susceptibility based on various molecular biomarkers.