Collaborating Authors


The smart bracelet that tracks your blood pressure

Daily Mail - Science & tech

Mike Kisch, Aktiia CEO, told MailOnline that having constant blood pressure measurements in all settings was a'game changer' for doctors and patients That will be for doctors, allowing them to remotely gauge the progress of patients, even see what time of day medication should be taken. 'Right now, after they do the initial diagnosis and prescribe medication, they don't get a lot of data from the patient, so the likelihood that the first time it will work is low, so now they get ongoing data to see if they need to modify treatment. 'That is a game changer for the physician,' explains Mr Kisch. Data gathered by this device is fed into large scale cohort studies, with nine currently running or about to run around the world. One is about the way patient engagement in hypertension management programmes increase when using these products and how a doctors decision making process improves.

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation Artificial Intelligence

Patient monitoring is vital in all stages of care. We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm.

Multivariate Time Series Imputation with Variational Autoencoders Machine Learning

Time series are often associated with missing values, for instance due to faulty measurement devices, partially observed states, or costly measurement procedures [15]. These missing values impair the usefulness and interpretability of the data, leading to the problem of data imputation: estimating those missing values from the observed ones [38]. Multivariate time series, consisting of multiple correlated univariate time series or channels, give rise to two distinct ways of imputing missing information: (1) by exploiting temporal correlations within each channel, and (2) by exploiting correlations across channels, for example by using lowerdimensional representations of the data. For instance in a medical setting, if the blood pressure of a patient is unobserved, it can be informative that the heart rate at the current time is higher than normal and that the blood pressure was also elevated an hour ago. An ideal imputation model for multivariate time series should therefore take both of these sources of information into account.