Mountains of data are constantly being accumulated, including in the form of medical records of doctor visits and treatments. The question is what actionable information can be gleaned from it beyond a one-time record of a specific medical examination. Arguably, if one were to combine the data in a large corpus of many patients suffering from the same condition, then overall patterns that apply beyond a specific instance of a specific doctor visit might be observed. Such patterns might reveal how medical conditions are related to one another over a broad set of patients, as well as how these conditions might be related to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) of the Centers for Disease Control and Prevention (CDC) Classification of Diseases, Functioning, and Disability codes (henceforth, ICD codesa). Conceivably, applying such a method to a large dataset could even suggest new avenues of medical and public health research by identifying new associations, along with the relative strength of the associations compared to other associations.
Technology helps us live better and for longer; in fact it has been doing so since the birth of modern medicine. And as each new technology comes into use, it turns out to have medical uses, even though these are not always the ones that are sold hardest: in the 1920s the American press was full of advertisements for the health benefits of radium, which was then a mysterious and powerful substance just as artificial intelligence (AI) is today. AI won't work miracles or make death unnecessary by letting people upload their minds into silicon, but it might catch cancers earlier. The prime minister on Monday said that 30,000 lives a year would be saved by 2030, mostly through earlier and more accurate diagnosis. This is about 10% of the annual cancer death rate in Britain.
This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.
How many doctors does it take to tell you how you're doing? The answer could soon be, none. Scientists and researchers across India are exploring the applications of artificial intelligence in health care -- from helping diagnoses illnesses to monitoring critical care. "Artificial intelligence -- or cyber-physical systems, as I like to call them -- can collect digitised data or generate data, analyse and make decisions based on it," says professor Ashutosh Sharma, secretary of the union government's department of science and technology. "A big advantage of AI in healthcare is that it can help where there is a scarcity of human resources, which is the case in many rural areas," adds Dr P Anandan, CEO at the Wadhwani Institute for Artificial Intelligence (Wadhwani AI).
In this paper we propose an efficient algorithm ProtoDash for selecting prototypical examples from complex datasets. Our generalizes the learn to criticize (L2C) work by Kim et al. (2016) to not only select prototypes for a given sparsity level $m$ but also to associate non-negative (for interpretability) weights with each of them indicative of the importance of each prototype. This extension provides a single coherent framework under which both prototypes and criticisms can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016). Our additional requirement of learning non-negative weights no longer maintains submodularity of the objective as in the previous work, however, we show that the problem is weakly submodular and derive approximation guarantees for our fast ProtoDash algorithm. We demonstrate the efficacy of our method on diverse domains such as retail, digit recognition (MNIST) and on publicly available 40 health questionnaires obtained from the Center for Disease Control (CDC) website maintained by the US Dept. of Health. We validate the results quantitatively as well as qualitatively based on expert feedback and recently published scientific studies on public health, thus showcasing the power of our method in providing actionability (for retail), utility (for MNIST) and insight (on CDC datasets), which presumably are the hallmark of an effective interpretable method.
Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. We summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural Language Processing (NLP) at this moment.
IBM scientists Thomas Brunschwiler and Rahel Straessle are developing machine learning algorithms to interpret the IoT data. COPD, is a progressive lung disease which causes breathlessness and is often caused by cigarette smoke and air pollution. By 2030, it is expected to be the third leading cause of death worldwide, with 90% occurring in low and middle-income countries, according to the World Health Organization. The Centers for Disease Control and Prevention reports that by 2020 the expected cost of medical care for adults in the US with COPD will be more than $90 billion, mainly due to complications and multiple hospitalizations, many of which are preventable with better healthcare management and more personalized and frequent patient support. IBM researchers in Zurich are collaborating with Swiss start-up docdok.health to develop a set of sensor and machine learning technologies that aim to improve the life quality of COPD patients, facilitate patient-physician communication and simultaneously reduce the financial burden on healthcare systems.
Artificial intelligence (AI) seems to know no boundaries. Innovation in this area has been exciting, and developments over the next five years will be nothing short of astonishing. Even while you're sleeping, it's possible to have new AI technologies hard at work to ensure you get a good night of quality, uninterrupted sleep. As Americans, we're getting a failing grade when it comes to sleep. This is just a small sample of data.