Abstract
BackgroundWith connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.MethodsTo address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is Physician Assist Filters (PAF) that transform unwieldy multi-sensor time series data into summarized patient/disease specific trends in steps of progressive precision as demanded by the doctor for patient’s personalized condition at hand and help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient’s medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using SVM machine learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 min of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India.ResultsThe results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer.ConclusionThe RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps, thereby providing the advantages of three A’s: availability, affordability, and accessibility in the global health scenario.
Highlights
With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors
In a trial that was conducted at our hospital, we were able to achieve 99% precision in diagnosing sleep apnea from heart rate variability (HRV) using deep learning algorithm called Long short-term memory recurrent neural networks (LSTMRNN) and is reported in one of our previous works [16]
The second hypothesis evaluates the utility of RASPRO as a tool for predictive analytics in critical conditions; while the third hypothesis help us understand if there exists a case for personalization in disease discovery and prediction
Summary
With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors Transforming this flood of data into effective and efficient diagnosis is a major challenge. Precision medicine and personalized healthcare are fast gaining wide research interest as well as initial acceptance among the medical community This is facilitated by the availability of ubiquitous data sources such as wearable sensors, smartphones, and IoT (Internet of Things), along with machine learning and large-scale data analytics tools, resulting in promising outcomes in some of the Pathinarupothi et al BMC Medical Informatics and Decision Making (2018) 18:78 we found that, this promises to provide hitherto unavailable healthcare services to critically ill and aging population in the developing world, there are significant roadblocks in our expectation that doctors embrace this new paradigm in handling patients. The RASPRO framework was first introduced in [1]
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