In a trial of a new drug to cure cancer, 44 percent of 50 patients achieved remission after treatment. Without the drug, only 32 percent of previous patients did the same. The new treatment sounds promising, but is it better than the standard? That question is difficult, so statisticians tend to answer a different question. They look at their results and compute something called a p-value.
A 2015 Journal of Experimental Psychology study, involving 166 subjects, found that when people's phones beep or buzz while they're in the middle of a challenging task, their focus wavers, and their work gets sloppier--whether they check the phone or not. In an April article in the Journal of the Association for Consumer Research, Dr. Ward and his colleagues wrote that the "integration of smartphones into daily life" appears to cause a "brain drain" that can diminish such vital mental skills as "learning, logical reasoning, abstract thought, problem solving, and creativity." In a similar but smaller 2014 study (involving 47 subjects) in the journal Social Psychology, psychologists at the University of Southern Maine found that people who had their phones in view, albeit turned off, during two demanding tests of attention and cognition made significantly more errors than did a control group whose phones remained out of sight. In another study, published in Applied Cognitive Psychology in April, researchers examined how smartphones affected learning in a lecture class with 160 students at the University of Arkansas at Monticello.
And in an increasingly data-driven industry, medical education hasn't kept pace. Medical education does little to train doctors in the data science, statistics, or behavioral science required to develop, evaluate, and apply algorithms in clinical practice." But that's only possible if medical teams adopt new members: Clinicians well-versed in computer science that can interpret analytics designed to "systematically analyze every heartbeat" that can treat "tens of thousands of Americans who might otherwise drop dead unexpectedly in any given year." And while developers and computer scientists are often frustrated with the industry's slow adoption of technology, validated clinical trials are a critical part of preventing the use of potentially harmful tools.
Not everyone has experienced the same disillusionment when it comes to IBM's artificial intelligence capabilities. While acknowledging that the road ahead is long and that AI is a toddler with lots to learn, Mayo Clinic's CIO has publicly spoken about how the clinical trial matching capabilities of IBM Watson Health is an extremely useful tool. An IBM Watson Health spokeswoman who did not want to comment on the record pushed back on his comments saying that Watson for Drug Discovery is a commercialized product that Pfizer and other organizations are using. While radiology could be a relatively easy application for AI, counterintuitively, IBM Watson Health has no current radiology product in the marketplace.
The problem of high healthcare spending, in other words, is a problem of high labor cost. "Machine learning and artificial intelligence drive a lot more productivity with a smaller number of people and [lower] salaries," Makower said. Artificial intelligence (AI), of course, is the latest buzzword in healthcare, and in recent years companies ranging from IBM to Microsoft have dabbled in applying the concept to everything from imaging analysis to clinical trial matching. Like Makower, Ishrak shared his vision of how the future of AI in healthcare might play out.
From the acceleration of regulatory submissions - by identifying data gaps that have led to delays or rejections in the past - to the transformation of the conduct of clinical trials and patient safety monitoring, artificial intelligence (AI) has substantial potential to change the way life sciences organisations operate. Back-end technology already exists to facilitate more intelligent and proactive health monitoring by taking things forward as drug companies rely on finding the optimum ways for patients to interact with and use the tools. There is also important safety monitoring potential and drug feedback potential, as long as intelligent tools based on AI and machine learning are in the background offering companies what to look for and ways of deciphering what it all means. As more and more companies identify opportunities to turn AI-enabled insights into timely and beneficial outcomes - whether by accelerating market entry, successfully mining social media for potential adverse events and other patient feedback, discovering new indications, or improving the manufacturing and supply chain process - advanced automation through increased machine intelligence looks set to be the way forward.
First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses.
The primary focus of these initiatives is on health care providers, helping them develop treatment approaches that are most effective for individual patients. One consortium of hospitals, researchers, and a startup, for example, is conducting "Project Survival" to identify effective biomarkers for pancreatic cancer.3 In other firms, real-world data sources are being used to identify molecules that might be particularly effective (or ineffective) in clinical trials. Another long-term challenge to be addressed by the life sciences and health care industry is collaboration and integration of data. Project Survival, for example--an effort to find a pancreatic cancer biomarker--involves collaboration among a big data drug development startup (Berg Health), an academic medical center (Beth Israel Deaconess in Boston), a nonprofit (Cancer Research and Biostatistics), and a network of oncology clinicians and researchers (the Pancreatic Research Team).
IBM's supercomputer Watson has been applying machine learning to personalized cancer treatment for some time. After pouring over 600,000 medical reports and 1.5 million anonymized patient records and clinical trials, the data should help define the clearest path forward for doctors. Google's DeepMind is learning how to better apply radiotherapy to cancer patients. To transform this process, DeepMind will analyze 700 anonymized scans from former patients suffering from head and neck cancers.
A new study, in which IBM Watson took just 10 minutes to analyze a brain-cancer patient's genome and suggest a treatment plan, demonstrates the potential of artificially intelligent medicine to improve patient care. Both the NYGC clinicians and Watson identified mutations in genes that weren't checked in the panel test but which nonetheless suggested potentially beneficial drugs and clinical trials. Both Watson and the expert team received the patient's genome information and identified genes that showed mutations, went through the medical literature to see if those mutations had figured in other cancer cases, looked for reports of successful treatment with drugs, and checked for clinical trials that the patient might be eligible for. IBM's Parida notes that the cost of sequencing an entire genome has plummeted in recent years, opening up the possibility that whole-genome sequencing will soon be a routine part of cancer care.