CSV is a common format for storing and exchanging data, and can be read by most data analysis and visualization software. There's an important difference between gender and eye color, called "categorical" variables, and height and weight, termed "continuous." Creating frequency counts from categorical data creates a new continuous variable -- what has changed is the level of analysis. In this example, the original data would consist of a huge table with a record for each person, noting their racial/ethnic identity as categorical variables; in creating the frequency table shown here, the level of analysis has shifted from the individual to the racial/ethnic group.
At Memorial Sloan Kettering Cancer Center, physicians are training a new kind of colleague: the Watson for Oncology program. "Just like we would teach a trainee in medical oncology or surgical oncology, we're teaching the MSK Watson system," explained Dr. Mark Kris, a lead physician at Memorial Sloan Kettering. Doctors feed information into Watson for Oncology, allowing it to learn appropriate treatment options. These recommendations are doctor-vetted since, as with Watson for Oncology, the system is'taught' by actual physicians who feed data and information in.
New data presented this week at the American Society of Clinical Oncology's annual meeting show that IBM's Watson for Oncology suggests cancer treatments that are often in-line with what physicians recommend. In a handful of studies being presented at ASCO, researchers show that Watson for Oncology is pretty dang good at recommending treatments for a variety of different cancers. From research done in India, Watson's treatment recommendations were in agreement with those of physicians 96 percent of the time for lung cancer, 93 percent of the time for rectal cancer, and 81 percent of the time for colon cancer. Additionally, Watson was able to screen breast and lung cancer patients for clinical trial eligibility 78 percent faster than a human, reducing screening time from 110 minutes down to just 24.
Healthcare AI expert Peter Borden, managing director at consulting and services firm Sapient Health, helps healthcare organizations apply innovative AI technologies to their ecosystems. In this Q&A with SearchHealthIT, Borden talks about how such AI in healthcare applications helps with clinical trials, customizing post-discharge instructions using patients' personal characteristics and population health. How will new forms of AI in healthcare affect transitional care when patients leave the hospital for other settings? How could emotional intelligence help AI in healthcare applications?
Here's a more realistic prediction: Self-correcting machine learning models and auto-generated code will change the way statisticians, programmers, and data scientists work. On the front end of the process, every machine learning model requires training data. All of these very different kinds of data have one thing in common: Human beings worked to gather the data, structure the data, and provide access to it. The reason data exist is because human beings decided to gather the data, often at significant cost, if not in terms of cash then in terms of time and effort.
To stop there, though, would be to deny one of the central goals of statistical science. . We can shout till our throats are sore that rejection of the null should not imply the acceptance of the alternative, but acceptance of the alternative is what many people want to hear. Also, remember that most effects can't be zero (at least in social science and public health), and that an "effect" is usually a mean in a population (or something similar such as a regression coefficient)--a fact that seems to be lost from consciousness when researchers slip into binary statements about there being "an effect" or "no effect" as if they are writing about constants of nature.
Since his appearance on the game show in 2011, IBM has expanded Watson's talents, building on the algorithms that allow him to read and derive meaning from natural language. Toronto Western, part of the University Health Network, is the first hospital in Canada to use Watson for research in Parkinson's, a neurological disorder. The centre has a track record of running clinical trials for off-label drug use, which means taking a drug approved for treatment of one condition and repurposing it for another. Visanji, 39, is a scientist at the hospital's Morton and Gloria Shulman Movement Disorders Centre, the country's biggest Parkinson's clinic.
One is multivariate analysis, in which potential confounding variables are included as covariates (independent variables) to reduce group differences post hoc. Broadly speaking, propensity score analysis can be performed in a number of ways: propensity score matching, propensity score stratification, propensity score weighting and covariate adjustment. With inverse propensity weighting each subject's weight is the inverse of the probability of belonging to the group to which they belong, probability being represented by their propensity score. The easiest way (though not always the best) is a regression model relating the outcome (dependent variable) to treatment group status – usually a dummy-coded (0/1) variable – after having first included subjects' propensity scores in the equation as a control variable.
In oncology, Watson draws upon "600,000 medical evidence reports, 1.5 million patient records and clinical trials, and two million pages of text from medical journals to help doctors develop treatment plans" tailored based on a patient's symptoms, genetics and history. In that scenario, Watson has also done its homework, looking at 25 million Medline abstracts, over one million medical journal articles, data from four million patients, and every drug patent since 1861. The Financial Times' technology correspondent Madhumita Murgia wrote an excellent analysis of digital mobile healthcare in the future, featuring Babylon Health. Your phone will be a hub of your medical records, including personal health history, diet and fitness.
Such capabilities effectively reduce physician time, enabling them to focus on the most critical patients and streamline the care process." Cognitive techniques such as machine learning and deep learning are needed to process structured and unstructured data at scale and automatically discover patterns and anomalies to augment human intelligence with machine intelligence and deliver value across the healthcare value chain. Recent technological developments enable us to automatically apply mathematical calculations to vast amounts of medical data -- taking into consideration health conditions, genetic factors and lifestyle to identify complex trends, patterns and interrelationships over time. In a recent Ambra Health webinar event on automated medical image analysis and AI, 50 percent of polled audience attendees believe that in three years, using deep learning in radiology could help reduce imaging errors, and 30 percent believe artificial intelligence can work to automate workflows such as patient matching.