We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

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We reviewed the literature on home telecare for elderly patients suffering from chronic diseases. Articles published between 1990 and 2007 were identified via the PubMed database. The literature search yielded 485 papers. After reviewing the title and abstract from each, 54 were selected for closer examination. They were published in 37 different journals. The number of papers increased from one in 1997 to 14 in 2006. The diseases in which home telecare had been used were diabetes (14 studies), heart failure (13 studies), cognitive impairment (dementia and/or Alzheimer's disease, 10 studies), chronic obstructive pulmonary disease (5 studies), chronic wounds (4 studies) and mobility disabilities (4 studies). Patients were generally satisfied with home telecare, but they preferred a combination of home telecare with conventional health-care delivery. Health-care professionals were positive about telecare. Users felt that on many occasions telecare led to a reduction in costs due to time savings and avoidance of travelling. Even though there were important benefits from home telecare, there are organizational, ethical, legal, design, usability and other matters that need to be resolved before widespread implementation can occur.

Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.

Background Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the differenc...

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There are many home telecare technologies which have been developed specifically for chronic diseases and there are some more generic technologies that could be used as well. For home telecare, the equipment must be certified, the operational routines must be reformed, the infrastructure must be in place, the market must be prepared, the health authorities must be convinced that the system will work and the cost-effectiveness must be evaluated. Organizational and societal changes, such as cost reduction policies and an aging population, are the main driving forces for the development of home telecare, especially for elderly patients. At the moment there is no holistic model for scientific evaluation from different perspectives (e.g. clinical, legal, technical). We suggest that more research on home telecare and its effects needs to be conducted, in order to provide evidence for optimizing the use of this promising technique.