high cholesterol
Everyday spice may be key to fighting fat and high cholesterol, scientists say
New research shows that black cumin seed powder significantly reduced LDL cholesterol and triglycerides, while increasing HDL cholesterol, in an eight-week study.
Health care bots are only as good as the data and doctors they learn from
The number of tech companies pursuing health care seems to have reached an all-time high: Google, Amazon, Apple, and IBM's Watson all want to change health care using artificial intelligence. IBM has even rebranded its health offering as "Watson Health -- Cognitive Healthcare Solutions." Although technologies from these giants show great promise, the question of whether effective health care AI already exists or whether it is still a dream remains. As a physician, I believe that in order to understand what is artificially intelligent in health care, you have to first define what it means to be intelligent in health care. Consider the Turing test, a point when a machine becomes indistinguishable from a human. Joshua Batson, a writer for Wired magazine, has mused whether there is an alternative measurement to the Turing test, one where the machine doesn't just seem like a person, but an intelligent person.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Ballinger, Brandon (Cardiogram) | Hsieh, Johnson (Cardiogram) | Singh, Avesh (Cardiogram) | Sohoni, Nimit (Cardiogram, Stanford) | Wang, Jack (Cardiogram, University of Waterloo) | Tison, Geoffrey H. (University of California, California (UCSF)) | Marcus, Gregory M. (University of California, California (UCSF)) | Sanchez, Jose M. (University of California, California (UCSF)) | Maguire, Carol (University of California, California (UCSF)) | Olgin, Jeffrey E. (University of California, California (UCSF)) | Pletcher, Mark J. (University of California, California (UCSF))
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised training methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Ballinger, Brandon, Hsieh, Johnson, Singh, Avesh, Sohoni, Nimit, Wang, Jack, Tison, Geoffrey H., Marcus, Gregory M., Sanchez, Jose M., Maguire, Carol, Olgin, Jeffrey E., Pletcher, Mark J.
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear.