Call for Pragmatic Computational Psychiatry

2016 ◽  
Author(s):  
Martin P. Paulus ◽  
Crane Huang ◽  
Katia M. Harlé
Keyword(s):  
Risk Prediction ◽  
Psychiatric Patient ◽  
Biological Psychiatry ◽  
Biological Parameters ◽  
Pragmatic Approach ◽  
Future Directions ◽  
Computational Approaches ◽  
Computational Psychiatry ◽  
Intense Research

Biological psychiatry is at an impasse. Despite several decades of intense research, few, if any, biological parameters have contributed to a significant improvement in the life of a psychiatric patient. It is argued that this impasse may be a consequence of an obsessive focus on mechanisms. Alternatively, a risk prediction framework provides a more pragmatic approach, because it aims to develop tests and measures which generate clinically useful information. Computational approaches may have an important role to play here. This chapter presents an example of a risk-prediction framework, which shows that computational approaches provide a significant predictive advantage. Future directions and challenges are highlighted.

scholarly journals A Comparative Study of Target Tracking Approaches in Wireless Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Asmaa Ez-Zaidi ◽  
Said Rakrak
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Directions ◽  
Wide Range ◽  
Comprehensive Survey ◽  
The Subject ◽  
Intense Research

Wireless sensor networks have been the subject of intense research in recent years. Sensor nodes are used in wide range of applications such as security, military, and environmental monitoring. One of the most interesting applications in wireless sensor networks is target tracking, which mainly consists in detecting and monitoring the motion of mobile targets. In this paper, we present a comprehensive survey of target tracking approaches. We then analyze them according to several metrics. We also discuss some of the challenges that influence the performance of tracking schemes. In the end, we conduct detailed analysis and comparison between these algorithms and we conclude with some future directions.

scholarly journals Structure Determination of Microtubules and Pili: Past, Present, and Future Directions

2022 ◽  
Vol 8 ◽  
Author(s):  
James A. Garnett ◽  
Joseph Atherton
Keyword(s):  
Ex Vivo ◽  
Structural Heterogeneity ◽  
Resonance Spectroscopy ◽  
Future Directions ◽  
Computational Approaches ◽  
The Past ◽  
Major Shift ◽  
Technical Innovations

Historically proteins that form highly polymeric and filamentous assemblies have been notoriously difficult to study using high resolution structural techniques. This has been due to several factors that include structural heterogeneity, their large molecular mass, and available yields. However, over the past decade we are now seeing a major shift towards atomic resolution insight and the study of more complex heterogenous samples and in situ/ex vivo examination of multi-subunit complexes. Although supported by developments in solid state nuclear magnetic resonance spectroscopy (ssNMR) and computational approaches, this has primarily been due to advances in cryogenic electron microscopy (cryo-EM). The study of eukaryotic microtubules and bacterial pili are good examples, and in this review, we will give an overview of the technical innovations that have enabled this transition and highlight the advancements that have been made for these two systems. Looking to the future we will also describe systems that remain difficult to study and where further technical breakthroughs are required.

scholarly journals Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

2017 ◽  
Vol 4 (4) ◽  
pp. 307-320 ◽  
Author(s):  
Lori C. Sakoda ◽  
Louise M. Henderson ◽  
Tanner J. Caverly ◽  
Karen J. Wernli ◽  
Hormuzd A. Katki
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Risk Prediction ◽  
Prediction Models ◽  
Current Knowledge ◽  
Lung Cancer Screening ◽  
Future Directions ◽  
Risk Prediction Models

scholarly journals Computational Psychiatry Research Map (CPSYMAP): a New Database for Visualizing Research Papers

2020 ◽  
Author(s):  
Ayaka Kato ◽  
Yoshihiko Kunisato ◽  
Kentaro Katahira ◽  
Tsukasa Okimura ◽  
Yuichi Yamashita
Keyword(s):  
Machine Learning ◽  
Computational Neuroscience ◽  
Scientific Evidence ◽  
High Dimensional ◽  
Two Dimensional ◽  
Research Papers ◽  
Treatment Protocols ◽  
Computational Approaches ◽  
Computational Psychiatry ◽  
Multidisciplinary Nature

AbstractThe field of computational psychiatry is growing in prominence along with the recent advances in computational neuroscience, machine learning, and the cumulative scientific evidence of psychiatric disorders. Computational approaches provide an understanding of disorders using psychological and neuroscience terms and help to determine treatment protocols based on high-dimensional data. However, the multidisciplinary nature of this field seems to limit the development of computational psychiatry studies. Computational psychiatry combines knowledge from neuroscience, psychiatry, and computation; thus, there is an emerging need for a platform to integrate and coordinate these perspectives. In this study, we developed a new database for visualizing research papers as a two-dimensional “map” called the Computational Psychiatry Research Map (CPSYMAP). This map shows the distribution of papers along neuroscientific, psychiatric, and computational dimensions to enable anyone to find niche research and deepen their understanding of the field.

Computational Approaches for Studying Mechanisms of Psychiatric Disorders

2016 ◽  
Author(s):  
Zeb Kurth-Nelson ◽  
John P. O’Doherty ◽  
Deanna M. Barch ◽  
Sophie Denève ◽  
Daniel Durstewitz ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Patient Outcomes ◽  
Computational Neuroscience ◽  
Psychiatric Illness ◽  
Psychological Processes ◽  
Computational Approaches ◽  
Computational Psychiatry ◽  
Mechanistic Understanding ◽  
The Many

Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding these processes. This chapter begins by organizing the many ways in which computational neuroscience may provide insight to the mechanisms of psychiatric illness. It then contextualizes the quest for deep mechanistic understanding through the perspective that even partial or nonmechanistic understanding can be applied productively. Finally, it questions the standards by which these approaches should be evaluated. If computational psychiatry hopes to go beyond traditional psychiatry, it cannot be judged solely on the basis of how closely it reproduces the diagnoses and prognoses of traditional psychiatry, but must also be judged against more fundamental measures such as patient outcomes.

scholarly journals Recent advances in processing negation

2020 ◽  
pp. 1-10
Author(s):  
Roser Morante ◽  
Eduardo Blanco
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Future Directions ◽  
Rule Based ◽  
Computational Approaches ◽  
Recent Advances ◽  
Linguistic Phenomenon

Abstract Negation is a complex linguistic phenomenon present in all human languages. It can be seen as an operator that transforms an expression into another expression whose meaning is in some way opposed to the original expression. In this article, we survey previous work on negation with an emphasis on computational approaches. We start defining negation and two important concepts: scope and focus of negation. Then, we survey work in natural language processing that considers negation primarily as a means to improve the results in some task. We also provide information about corpora containing negation annotations in English and other languages, which usually include a combination of annotations of negation cues, scopes, foci, and negated events. We continue the survey with a description of automated approaches to process negation, ranging from early rule-based systems to systems built with traditional machine learning and neural networks. Finally, we conclude with some reflections on current progress and future directions.

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