Every year, nearly one out of eight U.S deaths is caused due to heart failure. One of acute heart failure's most common causes is the presence of excess fluid in the lungs. This condition is known as'pulmonary edema.' A patient's excess fluid level often indicates the doctor's course of action, but such determination requires clinicians to rely on subtle features in X-rays that sometimes lead to inconsistent diagnoses and treatment plans. A group of researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can analyse the X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 means healthy to 3 which is very bad.

Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning. Learning better representations is critical to improve the performance of downstream tasks. Due to the advances in machine learning, we now can learn better and meaningful representations from EHR through disentangling the underlying factors inside data and distilling large amounts of information and knowledge from heterogeneous EHR sources. In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1. Next, we explain the commonly-used machine learning and evaluation methods for representation learning using a deep learning approach in Section 2. Following that, we review recent related studies of learning patient state representation from EHR for clinical machine learning tasks in Section 3. Finally, in Section 4 we discuss more techniques, studies, and challenges for learning natural language representations when free texts, such as clinical notes, examination reports, or biomedical literature are used. W e also discuss challenges and opportunities in these rapidly growing research fields.

In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning experiments for clinical prediction tasks. The objectives of this chapter are to (1) understand the basics of machine learning techniques and the reasons behind why they are useful for solving clinical prediction problems, (2) understand the intuition behind some machine learning models, including regression, decision trees, and support vector machines, and (3) understand how to apply these models to clinical prediction problems using publicly available datasets via case studies.

De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic de-identification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.

When a clogged artery landed Peter Szolovits in the hospital for a coronary bypass operation in mid-October, he noticed a few incongruities other patients might not have. Machines that performed intertwined functions--dosing and delivering medication, for example--did not communicate with one another, and patient statistics detailed on paper were not in the hospital's electronic medical records. As head of the Massachusetts Institute of Technology's Clinical Decision Making Group, which works to apply artificial intelligence (AI) to medicine, Szolovits knew that intelligent systems could optimize care by working together better to eliminate errors as well as avoid repetition of medical tests. Indeed, in the midst of the U.S. health care debate, some experts say that AI could lift some of the burden on physicians by helping them diagnose conditions and choose treatments. Of course, the same claim echoed in the 1970s and 1980s, when a media blitz put medical AI on the cover of newsweeklies.

The last seven years have seen the field of artificial intelligence (AI) transformed. This transformation is not simple, nor has it yet run its course. The transformation has been generated by the emergence of expert systems. Whatever exactly these are or turn out to be, they first arose during the 1970s, with a triple claim: to be AI systems that used large bodies of heuristic knowledge, to be AI systems that could be applied, and to be the wave of the future. The exact status of these claims (or even whether my statement of them is anywhere close to the mark) is not important. The thrust of these systems was strong enough and the surface evidence impressive enough to initiate the transformation. This transformation has at least two components.

July Conference Highlights m An AI Art Exhibition will showcase robot weighing in at 300 lbs., from the use of AI in serious works SRI; Bert and Ernie, midget-sized Again this year, AAAI is staging the of art. AIon-Line, five audience-interactive Technologies, untethered and battery with the National Conference on Artificial AI user panels, will offer practical powered, from NASA-JSC; William, a Intelligence, with 19 deployed learning on key business and feel-its-way robot from MIT and JPL; applications selected for presentation organization issues, based on case Flash, a Denning mobile platform from entries from around the world. A series of invited speakers and guidance from MITRE; Flash dimension to the conference, panels will complement the refereed Zorton, a walking machine designed promising give-and-take discussions papers and introduce areas of to compete in the robotic decathlon, about AI in operation. AI research that have unusual from Ecole Polytechnique of Montreal; AAAI-92 offers a series of technical interest and application. The of Southern California/Information The AAAI Robot Rules capture the National Conference is the year's Sciences Institute, and Peter spirit of the competition, indicating, largest meeting ground for those Szolovits, Associate Professor of Computer "It will not be slick, polished...there interested in AI, from scientific, academic, Science at the Massachusetts will be a certain amount of chaos, and business communities. This year's program is particularly There is a serious purpose, diverse, with concentration on research AAAI To Include New Dean noted, "to bring together areas results that bridge the gaps between AI Robotics Competition of AI including those working in perception, the different AI technologies and the AAAI will have its first AI Robotics Highlights, including AAAI-92 National Conference in San facilitate this and to make 34 focused technical sessions, with Jose, California July 12-16, 1992.