MOTIVATION: The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. RESULTS: We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. AVAILABILITY AND IMPLEMENTATION: http://www.newdrugtargets.org. CONTACT: [email protected].

Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

OBJECTIVE: The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR). DESIGN: The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms. MEASUREMENT: We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms. RESULTS: The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate. CONCLUSIONS: Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.

OBJECTIVE: To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. RESULTS: The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. DISCUSSION: With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. CONCLUSIONS: Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

OBJECTIVE: The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge. DESIGN: The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes. MEASUREMENTS: Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge. RESULTS AND DISCUSSION: Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge.

Table 1 Potential drugs that prolong QT interval with significance level of 0.001 Drug Chi-square Evidence Amiodarone 39.21 Known reaction Potassium supplements 24.78 Treatmenttypically given to people with long QT intervals to keep it normal Procainamide 22.11 Known reaction Sotalol 21.62 Known reaction Warfarin 18.42 No evidence found Meperidine 18.13 No evidence found Oxycodone 17.08 No evidence found Promethazine 12.90 No evidence found

Charles Friedman recently proposed a fundamental theorem of biomedical informatics (henceforth called the fundamental theorem) which states that ‘a person working in partnership with an information resource is better than that same person unassisted’.1 The person could be a clinician, researcher, student, patient or administrator interacting with the resource to perform some specific task at hand. Without loss of generality we can be agnostic about the various roles of the person and assume that the person is interacting with the information resource for decision making. Using the framework of decision removes from consideration pedestrian uses of a computational resource such as watching a movie or checking news. Hastie and Dawes2 formulate decision making as a response to a situation consisting of three parts: (1) availability of a set of actions to perform; (2) the decision maker has some prior notion regarding possible outcomes for each action; and (3) consequences for the outcomes. An example of a decision in a healthcare setting would be whether to manage a condition such as shoulder pain conservatively or by surgery. Cognitive scientists and researchers in artificial intelligence have used games such as tic tac toe, checkers and chess to study decision making. A chess contest described by Garry Kasparov,3 the former world chess champion, has important implications for the fundamental theorem under consideration. After his loss to the IBM chess supercomputer Deep Blue, Kasparov became interested in partnership play with computers instead of the traditional human versus machine contest in what came to be known as ‘advanced chess’. He reports that the online chess site http://Playchess.com organized an advanced chess competition in which players could participate in teams by partnering with other people and/or computers. Attracted by the prize money many grandmasters working in partnership with one or more computers joined the contest. The team of humans partnering with machines convincingly defeated even the strongest chess-playing computers such as Hydra, a chess supercomputer. Human strategic strengths combined with the tactical analytical powers of the machine became almost invincible. This was the predictable part. What was surprising was that the winner of the contest turned out to be a pair of amateur chess players who were using three computers simultaneously and not a strong grandmaster with a dedicated chess supercomputer. Kasparov makes the following observation: ‘Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants. Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.’ It is clear from the foregoing that the process of interaction with a computer is important for improved human decision making partnering with a computer. Recently, Hunter4 suggested a modification to the fundamental theorem by incorporating a scientific method to state that a person working in partnership with an information resource and using a scientific method is better than that same person unassisted. In Hunter's setting there is an implicit assumption of the user being a clinician or researcher. I argue that the enhancement is needed assuming the person in the theorem is a decision maker and suggest the following. A person working in partnership with an information resource and using a correct process is better than that same person unassisted. None. Not commissioned; not externally peer reviewed.