The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40%.
Objective Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to develop a generalizable model capable of leveraging clinical notes to predict healthcare-associated diseases 24–96 hours in advance. Methods We developed a reCurrent Additive Network for Temporal RIsk Prediction (CANTRIP) to predict the risk of hospital acquired (occurring ≥ 48 hours after admission) acute kidney injury, pressure injury, or anemia ≥ 24 hours before it is implicated by the patient’s chart, labs, or notes. We rely on the MIMIC III critical care database and extract distinct positive and negative cohorts for each disease. We retrospectively determine the date-of-event using structured and unstructured criteria and use it as a form of indirect supervision to train and evaluate CANTRIP to predict disease risk using clinical notes. Results Our experiments indicate that CANTRIP, operating on text alone, obtains 74%–87% area under the curve and 77%–85% Specificity. Baseline shallow models showed lower performance on all metrics, while bidirectional long short-term memory obtained the highest Sensitivity at the cost of significantly lower Specificity and Precision. Discussion Proper model architecture allows clinical text to be successfully harnessed to predict nosocomial disease, outperforming shallow models and obtaining similar performance to disease-specific models reported in the literature. Conclusion Clinical text on its own can provide a competitive alternative to traditional structured features (eg, lab values, vital signs). CANTRIP is able to generalize across nosocomial diseases without disease-specific feature extraction and is available at https://github.com/h4ste/cantrip.
A Means for Expressing Location Information in the Domain Name System Status of this MemoThis memo defines an Experimental Protocol for the Internet community. This memo does not specify an Internet standard of any kind. Discussion and suggestions for improvement are requested. Distribution of this memo is unlimited. AbstractThis memo defines a new DNS RR type for experimental purposes. This RFC describes a mechanism to allow the DNS to carry location information about hosts, networks, and subnets. Such information for a small subset of hosts is currently contained in the flat-file UUCP maps. However, just as the DNS replaced the use of HOSTS.TXT to carry host and network address information, it is possible to replace the UUCP maps as carriers of location information.This RFC defines the format of a new Resource Record (RR) for the Domain Name System (DNS), and reserves a corresponding DNS type mnemonic (LOC) and numerical code (29).This RFC assumes that the reader is familiar with the DNS [RFC 1034, RFC 1035. The data shown in our examples is for pedagogical use and does not necessarily reflect the real Internet.
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