Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
Data science is the big draw in business schools
579 days ago
7 Effective Methods for Fitting a Liner
589 days ago
3 Thoughts on Why Deep Learning Works So Well
589 days ago
3 million at risk from the rise of robots
589 days ago
Top 10 Hot Artificial Intelligence (AI) Technologies
How Machine Learning Is Helping Us Predict Heart Disease and Diabetes
While debate drags on about legislation, regulations, and other measures to improve the U.S. health care system, a new wave of analytics and technology could help dramatically cut costly and unnecessary hospitalizations while improving outcomes for patients. For example, by preventing hospitalizations in cases of just two widespread chronic illnesses - heart disease and diabetes - the United States could save billions of dollars a year.
Toward this end, my colleagues and I at Boston University's Center for Information and Systems Engineering have been striving to bring the power of machine-learning algorithms to this critical problem. In an ongoing effort with Boston-area hospitals, including the Boston Medical Center and the Brigham and Women's Hospital, we found that we could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82%. This will give care providers the chance to intervene much earlier and head off hospitalizations. Our team is also working with the Department of Surgery at the Boston Medical Center and can predict readmissions within 30 days of general surgery; the hope is to guide postoperative care in order to prevent them.
The hospitals provide patients' anonymized electronic health records (EHRs) that contain all of the information the hospital has about each patient, including demographics, diagnoses, admissions, procedures, vital signs taken at doctor visits, medications prescribed, and lab results. We then unleash our algorithms to predict who might have to be hospitalized. This gives the hospital opportunities to intervene, treat the disease more aggressively in an outpatient setting, and avoid a costly hospitalization while improving the patient's condition.
The accuracy rates of these predictions surpass what is possible with well-accepted risk scoring systems such as the one that emerged from the famous Framingham Heart Study, the ongoing long-term cardiovascular cohort study that is now in its third generation of participants. Using that system, a doctor assesses the patient's age, cholesterol, weight, blood pressure, and several other factors to arrive at the individual's chances of developing cardiovascular disease over the next 10 years. Using the Framingham Study 10-year cardiovascular risk score, one can predict hospitalizations with an accuracy of about 56%, which is substantially lower than the 82% rate we achieved.
In fact, we found that feeding the factors used in the Framingham 10-year risk score into more sophisticated machine-learning methods still leads to results inferior to ours (an accuracy rate of about 69%). This suggests that using the entirety of a patient's EHR (which can contain as much as 200 factors) instead of just a few key factors leads to superior prediction results. What's more, an algorithmic approach can easily be scaled so it can be applied to a very large number of patients - something that is impossible with human monitors only.
The potential benefits from applying machine-learning analytics in health care are enormous. Based on a study of a year's worth of hospital admissions, the U.S. Agency for Healthcare Research and Quality (AHRQ) estimated that 4.4 million of those admissions in the United States, totaling $30.8 billion in costs, could have been prevented. Of that $30.8 billion, $9 billion was for patients with heart diseases and $5.8 billion for patients with complications from diabetes. That's half of all unnecessary hospitalizations.
Just 5% of Medicaid's 70 million beneficiaries account for 54% of Medicaid annual expenditures of more than $500 billion, and 1% account for 25% of the total. Of this 1%, 83% have at least three chronic conditions. Approaches like ours could reduce their use of hospital services and save Medicaid a large amount of money.
Ongoing U.S. reforms in health care that link payments with outcomes are forcing hospitals to assume more financial risks. In response, hospitals are increasingly making analytics and new technologies an integral part of hospital operations. Business analytics widely used in the transportation industry by airlines and shipping companies are beginning to be employed to schedule operating rooms and staffing. Other algorithms are being developed to assist physicians in making diagnoses. My team has developed methods to automatically titrate medications in intensive care units in response to the patient's condition.
These advances are only the tip of the iceberg. We are on the cusp of major changes in health monitoring and care. Google and other companies with lots of experience in collecting and learning from data appear ready to step into this domain. A myriad of technologies, from implantable medical devices (such as defibrillators and pacemakers) to fit trackers, smart watches, and smartphones already capture our health data and lifestyle choices. Our credit-card and electronic-payment systems know our purchase history and the type of food we consume. The result is the emergence of a rich personal health record we carry in our pockets.
If we can now predict future hospitalizations with more than 80% accuracy using medical records alone, imagine what is possible if we can tap into this trove of personal data. Recommender systems could be used to nudge us to adopt healthier eating habits and behaviors. The holy grail of heading off the emergence of conditions by keeping people well could be realized.
Yes, analytics and data-driven personalized medicine and health monitoring present risks. Do we want our employers and health insurers to know the status of our health and the risks we face? Privacy, security, and reliability of new systems and methods are also critical concerns. But rather than retreating from this new era, we should be working on how to strengthen our methods, institutions, laws, and regulatory framework to avoid those unintended consequences. Algorithms - the foundation of encryption methods, privacy-preserving data processing, and intrusion- and fraud detection systems - could help. Read More