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Riva Health wants to turn your smartphone into a blood pressure monitor – TechCrunch

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Riva Health, founded by scientist Tuhin Sinha and Siri co-founder Dag Kittlaus, wants to help people measure their blood pressure in a clinically approved way. Blood pressure can help indicate at-risk patients before they are actually at risk, showing early signs of heart disease. While other hardware solutions on the market promise the same end goal, Riva wants to be a purely software solution that integrates with hardware that it thinks its end user has anyway: their smartphone. The company, launching out of stealth today, has raised $15.5 million in seed funding in a round led by Menlo Ventures, with participation from True Ventures. Greg Yap of Menlo, who talked to Sinha for three years before investing, will be joining the board.


Remote Patient Monitoring -- RPM

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Americans will generate more clinical grade biological data like daily vital signs in the next 5 years than has previously been recorded in the past 20 years. The data will be more accurate since it won't be one snapshot in time, but many snapshots in someone's daily life. While most clinical grade vital signs are collected and recorded in a healthcare setting like a clinic, hospital, or ER, there are a number of factors changing that quickly. The combination of AI based software & medical devices that have cleared the FDA, payor reimbursement, clinical adoption, and patient adoption are all coming together to bring RPM mainstream. This is impactful for a number of reasons.


This Startup Wants to Take Your Blood Pressure With an iPhone

WIRED

In 1896, Italian physician Riva Rocci published the first of four papers on an invention that is still widely used. It was his take on the sphygmomanometer, a device to measure the pressure that a pumping heart exerts on the arteries. Rocci's basic approach of tying a cuff to the upper arm remains standard, and it is a vital tool because hypertension is one of the most serious medical ailments. The CDC reports that nearly half of all adults in the US have high blood pressure, and it is a primary or contributing factor in 500,000 deaths annually--it's like Covid-19 every year. Only a fourth of people with hypertension have it under control, in part because sphygmomanometers, whether used in a doctor's office or via clunky home units, don't supply a steady stream of readings, multiple times a day and in different settings, to help determine the proper treatment.


Wearables and AI Transforming Healthcare Diagnosis

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Artificial intelligence has a significant role to play in the increasing digitization and automation of various industries. Advanced technologies like AI, machine learning, NLP, etc., have increased the pace and quality of digital transformation. Life becomes convenient and easier with these technological advancements. Healthcare is an important industry that has been crippling with shortcomings and quality erosion. Although, many healthcare providers have invested in technology to improve the quality and pace of treatment.


The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards

arXiv.org Artificial Intelligence

Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.


An IoT Framework for Heart Disease Prediction based on MDCNN Classifier

arXiv.org Artificial Intelligence

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.


Using machine learning to improve patient care

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Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions. One team created a machine-learning approach called "ICU Intervene" that takes large amounts of intensive-care-unit (ICU) data, from vitals and labs to notes and demographics, to determine what kinds of treatments are needed for different symptoms. The system uses "deep learning" to make real-time predictions, learning from past ICU cases to make suggestions for critical care, while also explaining the reasoning behind these decisions.


Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

arXiv.org Machine Learning

AIMS. This study compared the performance of deep learning extensions of survival analysis models with traditional Cox proportional hazards (CPH) models for deriving cardiovascular disease (CVD) risk prediction equations in national health administrative datasets. METHODS. Using individual person linkage of multiple administrative datasets, we constructed a cohort of all New Zealand residents aged 30-74 years who interacted with publicly funded health services during 2012, and identified hospitalisations and deaths from CVD over five years of follow-up. After excluding people with prior CVD or heart failure, sex-specific deep learning and CPH models were developed to estimate the risk of fatal or non-fatal CVD events within five years. The proportion of explained time-to-event occurrence, calibration, and discrimination were compared between models across the whole study population and in specific risk groups. FINDINGS. First CVD events occurred in 61,927 of 2,164,872 people. Among diagnoses and procedures, the largest 'local' hazard ratios were associated by the deep learning models with tobacco use in women (2.04, 95%CI: 1.99-2.10) and with chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95%CI: 1.50-1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk predictors. The deep learning models significantly outperformed the CPH models on the basis of proportion of explained time-to-event occurrence (Royston and Sauerbrei's R-squared: 0.468 vs. 0.425 in women and 0.383 vs. 0.348 in men), calibration, and discrimination (all p<0.0001). INTERPRETATION. Deep learning extensions of survival analysis models can be applied to large health administrative databases to derive interpretable CVD risk prediction equations that are more accurate than traditional CPH models.


Predicting Heart Disease using Machine Learning? Don't!

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I was recently invited to judge a Data Science competition. The students were given the'heart disease prediction' dataset, perhaps an improvised version of the one available on Kaggle. I had seen this dataset before and often come across various self-proclaimed data science gurus teaching naïve people how to predict heart disease through machine learning. I believe the "Predicting Heart Disease using Machine Learning" is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required. Let me unpack the various problems in applying machine learning to this data set.


Predicting Heart Disease Using Machine Learning? Don't! - KDnuggets

#artificialintelligence

I was recently invited to judge a Data Science competition. The students were given the'heart disease prediction' dataset, perhaps an improvised version of the one available on Kaggle. I had seen this dataset before and often come across various self-proclaimed data science gurus teaching naïve people how to predict heart disease through machine learning. I believe the "Predicting Heart Disease using Machine Learning" is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required. Let me unpack the various problems in applying machine learning to this data set.