Applying machine learning to continuously monitored physiological data

Abstract

The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for monitoring within the hospital environment. We present use cases as well as several questions regarding the application of ML to the analysis of the vast amount of complex data that clinicians must interpret in the realm of continuous physiological monitoring. ML, especially employed in bidirectional conjunction with electronic health record data, has the potential to extract much more useful information out of this currently under-analyzed data source from a population level. As a data driven entity, ML is dependent on copious, high quality input data so that error can be introduced by low quality data sources. At present, while ML is being studied in hybrid formulations along with static expert systems for monitoring applications, it is not yet actively incorporated in the formal artificial learning sense of an algorithm constantly learning and updating its rules without external intervention. Finally, innovations in monitoring, including those supported by ML, will pose regulatory and medico-legal challenges, as well as questions regarding precisely how to incorporate these features into clinical care and medical education. Rigorous evaluation of ML techniques compared to traditional methods or other AI methods will be required to validate the algorithms developed with consideration of database limitations and potential learning errors. Demonstration of value on processes and outcomes will be necessary to support the use of ML as a feature in monitoring system development: Future research is needed to evaluate all AI based programs before clinical implementation in non-research settings.

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

“…However, this approach has not been validated in a new population, so the performance of this algorithm in another group of people with PD remains unclear. Evaluation of overfitting and validation on a different cohort is of paramount importance when using machine learning approaches that can fit a specific set of data very well, but may fail on a new set . An additional short report measured the amount of FOG and its variability using a mixed approach (frequency of right and left foot accelerations and correlation of right and left foor angular velocities) with two wearable sensors on the feet worn for 7 days at home .…”

Journal of Clinical Monitoring and Computing end of year summary 2019: hemodynamic monitoring and management

“…For one of the original papers [40], an accompanying editorial was published highlighting important aspects of the reporting of such papers [45]. Finally, general and future aspects of applying machine learning to continuously monitored physiological data was highlighted in an excellent narrative review [46], where Rush et al stated in their introduction that "Machine learning is a term likely spoken of more than understood. Machine learning is most simply defined as the use of various statistical techniques that can be employed to make predictions and decisions based on similarities in what is being analyzed to what has previously been observed."…”

“…The authors discussed the role of machine learning and also acknowledge other artificial intelligence algorithms such as physiologic modelling. But, no matter the algorithm, human clinical knowledge will always remain necessary in the care of patients [46]. The authors reviewed specific clinical monitoring problems, where machine learning may play a role such as identification of sepsis, delirium, and ventilator dyssynchrony or reduction of false alarms and sedation management.…”

“…The field is still new but exciting for authors, editors, and reviewers and we look very much forward to seeing which impact all these efforts provide in future, preferably prospective, studies. Studies need sufficient reporting [45] as well as discussion of various bias types [46], particularly if studies are retrospective, which the vast majority of published papers still are. Potential biases in the underlying (retrospective) data are difficult to identify and evaluate when advanced and hard-to-interpret machine learning algorithms are applied to them.…”

An awakening in medicine: the partnership of humanity and intelligent machines

“…4 AI is already being built into the development of physiological monitors and has been proposed as an aid to decisions such as the treatment of sepsis, assessment of readmission risk, and recognition of consciousness in unresponsive patients. [5][6][7][8] One can envision the possibilities of AI guidance and support in the care of patients with complex conditions, such as those with multiple, chronic medical problems, or the decision to proceed to major surgery in fragile, complicated patients. The question is not whether computers can outperform humans in specific tasks, but how humanity will embrace and adopt these capabilities into the practice of medicine.…”