SciCombinator

Discover the most talked about and latest scientific content & concepts.

Concept: Prophecy

278

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Concepts: Forecasting, Medicine, Prophecy, Electronic health record, Scientific method, Futurology, Future, Prediction

193

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

Concepts: Prophecy, Futurology, Prediction, Scientific method

188

Why do certain group members end up liking each other more than others? How does affective reciprocity arise in human groups? The prediction of interpersonal sentiment has been a long-standing pursuit in the social sciences. We combined fMRI and longitudinal social network data to test whether newly acquainted group members' reward-related neural responses to images of one another’s faces predict their future interpersonal sentiment, even many months later. Specifically, we analyze associations between relationship-specific valuation activity and relationship-specific future liking. We found that one’s own future (T2) liking of a particular group member is predicted jointly by actor’s initial (T1) neural valuation of partner and by that partner’s initial (T1) neural valuation of actor. These actor and partner effects exhibited equivalent predictive strength and were robust when statistically controlling for each other, both individuals' initial liking, and other potential drivers of liking. Behavioral findings indicated that liking was initially unreciprocated at T1 yet became strongly reciprocated by T2. The emergence of affective reciprocity was partly explained by the reciprocal pathways linking dyad members' T1 neural data both to their own and to each other’s T2 liking outcomes. These findings elucidate interpersonal brain mechanisms that define how we ultimately end up liking particular interaction partners, how group members' initially idiosyncratic sentiments become reciprocated, and more broadly, how dyads evolve. This study advances a flexible framework for researching the neural foundations of interpersonal sentiments and social relations that-conceptually, methodologically, and statistically-emphasizes group members' neural interdependence.

Concepts: Reciprocal, Prophecy, Future, Scientific method, Psychology, Sociology, Prediction, Futurology

143

Measuring and predicting the success of junior faculty is of considerable interest to faculty, academic institutions, funding agencies and faculty development and mentoring programs. Various metrics have been proposed to evaluate and predict research success and impact, such as the h-index, and modifications of this index, but they have not been evaluated and validated side-by-side in a rigorous empirical study. Our study provides a retrospective analysis of how well bibliographic metrics and formulas (numbers of total, first- and co-authored papers in the PubMed database, numbers of papers in high-impact journals) would have predicted the success of biomedical investigators (n = 40) affiliated with the University of Nevada, Reno, prior to, and after completion of significant mentoring and research support (through funded Centers of Biomedical Research Excellence, COBREs), or lack thereof (unfunded COBREs), in 2000-2014. The h-index and similar indices had little prognostic value. Publishing as mid- or even first author in only one high-impact journal was poorly correlated with future success. Remarkably, junior investigators with >6 first-author papers within 10 years were significantly (p < 0.0001) more likely (93%) to succeed than those with ≤6 first-author papers (4%), regardless of the journal's impact factor. The benefit of COBRE-support increased the success rate of junior faculty approximately 3-fold, from 15% to 47%. Our work defines a previously neglected set of metrics that predicted the success of junior faculty with high fidelity-thus defining the pool of faculty that will benefit the most from faculty development programs such as COBREs.

Concepts: Divination, Impact factor, National Health and Medical Research Council, Prophecy, Future, Futurology, Prediction, Scientific method

118

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.

Concepts: United States Constitution, Prophecy, Jury, Divination, Future, Scientific method, Supreme Court of the United States, United States

65

Affective forecasting is an ability that allows the prediction of the hedonic outcome of never-before experienced situations, by mentally recombining elements of prior experiences into possible scenarios, and pre-experiencing what these might feel like. It has been hypothesised that this ability is uniquely human. For example, given prior experience with the ingredients, but in the absence of direct experience with the mixture, only humans are said to be able to predict that lemonade tastes better with sugar than without it. Non-human animals, on the other hand, are claimed to be confined to predicting-exclusively and inflexibly-the outcome of previously experienced situations. Relying on gustatory stimuli, we devised a non-verbal method for assessing affective forecasting and tested comparatively one Sumatran orangutan and ten human participants. Administered as binary choices, the test required the participants to mentally construct novel juice blends from familiar ingredients and to make hedonic predictions concerning the ensuing mixes. The orangutan’s performance was within the range of that shown by the humans. Both species made consistent choices that reflected independently measured taste preferences for the stimuli. Statistical models fitted to the data confirmed the predictive accuracy of such a relationship. The orangutan, just like humans, thus seems to have been able to make hedonic predictions concerning never-before experienced events.

Concepts: Hominidae, Divination, Forecasting, Scientific method, Prophecy, Future, Futurology, Prediction

53

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high-low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

Concepts: Psychology, Prophecy, Affect measures, Affective neuroscience, Affect display, Affect, Neuroscience, Emotion

46

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

Concepts: Survey sampling, Prophecy, Futurology, Prediction, Machine learning, Cancer, Scientific method, Future

46

We hypothesized that individuals may differ in the dispositional tendency to have positive vs. negative attitudes, a trait termed the dispositional attitude. Across 4 studies, we developed a 16-item Dispositional Attitude Measure (DAM) and investigated its internal consistency, test-retest reliability, factor structure, convergent validity, discriminant validity, and predictive validity. DAM scores were (a) positively correlated with positive affect traits, curiosity-related traits, and individual preexisting attitudes; (b) negatively correlated with negative affect traits; and © uncorrelated with theoretically unrelated traits. Dispositional attitudes also significantly predicted the valence of novel attitudes while controlling for theoretically relevant traits (such as the Big 5 and optimism). The dispositional attitude construct represents a new perspective in which attitudes are not simply a function of the properties of the stimuli under consideration, but are also a function of the properties of the evaluator. We discuss the intriguing implications of dispositional attitudes for many areas of research, including attitude formation, persuasion, and behavior prediction. (PsycINFO Database Record © 2013 APA, all rights reserved).

Concepts: Prophecy, All rights reserved, Futurology, Prediction, Scientific method, Psychometrics, Validity, Reliability

39

Lice are socially-transmitted ectoparasites. Transmission depends upon their host’s degree of contact with conspecifics. While grooming facilitates ectoparasite transmission via body contact, it also constrains their spread through parasite removal. We investigated relations between parasite burden and sociality in female Japanese macaques following two opposing predictions: i) central females in contact/grooming networks harbour more lice, related to their numerous contacts; ii) central females harbour fewer lice, related to receiving more grooming. We estimated lice load non-invasively using the conspicuous louse egg-picking behaviour performed by macaques during grooming. We tested for covariation in several centrality measures and lice load, controlling for season, female reproductive state and dominance rank. Results show that the interaction between degree centrality (number of partners) and seasonality predicted lice load: females interacting with more partners had fewer lice than those interacting with fewer partners in winter and summer, whereas there was no relationship between lice load and centrality in spring and fall. This is counter to the prediction that increased contact leads to greater louse burden but fits the prediction that social grooming limits louse burden. Interactions between environmental seasonality and both parasite and host biology appeared to mediate the role of social processes in louse burden.

Concepts: Primate, Interaction, Parasitism, Prophecy, Futurology, Centrality, Prediction