Towards more patient friendly clinical notes through language models and ontologies.

Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians time. We present a novel approach to automated simplification of medical text based on word frequencies and language modelling, grounded on medical ontologies enriched with layman terms. We release a new dataset of pairs of publicly available medical sentences and a version of them simplified by clinicians. Also, we define a novel text simplification metric and evaluation framework, which we use to conduct a large-scale human evaluation of our method against the state of the art. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.

Sentiment Analysis of Health Care Tweets: Review of the Methods Used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first.

Early and mid-term results of off-pump endarterectomy of the left anterior descending artery.

OBJECTIVES: Many patients referred for coronary artery bypass surgery (CABG) today have diffusely diseased coronary vessels, and some of them may require coronary endarterectomy to provide adequate revascularization. Most reports of coronary endarterectomy describe an on-pump procedure. As off-pump coronary artery bypass graft has become safer and more routine, there is renewed interest in off-pump coronary endarterectomy. We report on our series of patients who underwent off-pump coronary endarterectomy of the left anterior descending (LAD) artery using an open endarterectomy technique. METHODS: All patients undergoing open heart surgery at The Mount Sinai Medical Center are entered into a state-mandated, audited database. A retrospective review of this database revealed 12 patients between January 2008 and June 2012 who underwent off-pump endarterectomy of the LAD as part of their coronary revascularization. Additional data were collected from a review of the patients' charts. RESULTS: There were a total of 12 patients, with a mean age of 72 ± 4 years. Nine (75%) were male and 3 (25%) were female. Comorbidities included hypertension in 11 (92%) patients, dyslipidaemia in 10 (83%), diabetes in 8 (67%), renal failure in 6 (50%) and stroke in 1 (8%). The mean number of diseased coronary territories was 3 ± 0.4 (range 2-3), and the mean number of coronary bypass grafts performed was 4 ± 0.8 (range 2-5). Eight patients required transfusion with packed red blood cells (67%). One (8%) patient was converted from off-pump to on-pump. The mean intensive care unit stay was 3 ± 2.8 (range 1-8 days), and the mean hospital length of stay was 15 ± 13 (range 4-54 days). Postoperative follow-up (mean 24 ± 19 months, range 1-53) is complete, and no ischaemic events have occurred in the early and mid-term follow-up period. CONCLUSIONS: We conclude that off-pump endarterectomy of the LAD is a viable option for patients with diffuse LAD disease.