scholarly journals Can precision medicine advance psychiatry?

2020 ◽  
pp. 1-6
Author(s):  
Dónal Roche ◽  
Vincent Russell
Keyword(s):  
Precision Medicine ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
New Approach ◽  
Considerable Potential ◽  
Traditional Research ◽  
Learning Techniques ◽  
Insight Into

Precision medicine is a new approach that considers differences in genes, environment, and lifestyle in an attempt to tailor treatments for individual patients. Psychiatry, as a discipline, has historically relied on clinical judgement and phenomenology-based diagnostic guidelines and has yet to take full advantage. This editorial provides an insight into the expanding role of precision medicine in psychiatry, both in research and clinical practice. It discusses the application of genetics and subgroup stratification in increasing response rates to therapeutic interventions, mainly focusing on major depressive disorder and schizophrenia. It presents an overview of machine learning techniques and how they are being integrated with traditional research methods within the field. In the context of these developments, while emphasizing the considerable potential for moving toward precision psychiatry, we also acknowledge the inherent challenges.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

scholarly journals Mitochondrial Function, Biology, and Role in Disease

2016 ◽  
Vol 118 (12) ◽  
pp. 1960-1991 ◽  
Author(s):  
Elizabeth Murphy ◽  
Hossein Ardehali ◽  
Robert S. Balaban ◽  
Fabio DiLisa ◽  
Gerald W. Dorn ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Mitochondrial Function ◽  
The United States ◽  
Therapeutic Interventions ◽  
Therapeutic Approaches ◽  
Mitochondrial Defects ◽  
Exciting Time ◽  
Insight Into ◽  
Novel Therapeutic

Cardiovascular disease is a major leading cause of morbidity and mortality in the United States and elsewhere. Alterations in mitochondrial function are increasingly being recognized as a contributing factor in myocardial infarction and in patients presenting with cardiomyopathy. Recent understanding of the complex interaction of the mitochondria in regulating metabolism and cell death can provide novel insight and therapeutic targets. The purpose of this statement is to better define the potential role of mitochondria in the genesis of cardiovascular disease such as ischemia and heart failure. To accomplish this, we will define the key mitochondrial processes that play a role in cardiovascular disease that are potential targets for novel therapeutic interventions. This is an exciting time in mitochondrial research. The past decade has provided novel insight into the role of mitochondria function and their importance in complex diseases. This statement will define the key roles that mitochondria play in cardiovascular physiology and disease and provide insight into how mitochondrial defects can contribute to cardiovascular disease; it will also discuss potential biomarkers of mitochondrial disease and suggest potential novel therapeutic approaches.

scholarly journals Research on threat detection in cyber security based on machine learning

2021 ◽  
Vol 2113 (1) ◽  
pp. 012074
Author(s):  
Qiwei Ke
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Information Privacy ◽  
Machine Learning Techniques ◽  
Threat Detection ◽  
Vulnerability Detection ◽  
New Information ◽  
Learning Techniques ◽  
Potential Problems ◽  
Insight Into

Abstract The volume of the data has been rocketed since the new information era arrives. How to protect information privacy and detect the threat whenever the intrusion happens has become a hot topic. In this essay, we are going to look into the latest machine learning techniques (including deep learning) which are applicable in intrusion detection, malware detection, and vulnerability detection. And the comparison between the traditional methods and novel methods will be demonstrated in detail. Specially, we would examine the whole experiment process of representative examples from recent research projects to give a better insight into how the models function and cooperate. In addition, some potential problems and improvements would be illustrated at the end of each section.

A new approach for the prediction of partition functions using machine learning techniques

2018 ◽  
Vol 149 (4) ◽  
pp. 044118 ◽  
Author(s):  
Caroline Desgranges ◽  
Jerome Delhommelle
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Partition Functions ◽  
New Approach ◽  
Learning Techniques

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

Role of data mining and machine learning techniques in medical imaging

2020 ◽  
Vol 15 (3) ◽  
pp. 340
Author(s):  
Abhishek Agnihotri ◽  
Vikash Yadav ◽  
Vandana Dixit Kaushik
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Medical Imaging ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Quantitative Method to Explore the Assimilation Effects

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 217-217
Author(s):  
E Kroon ◽  
M J H Puts ◽  
C M M de Weert
Keyword(s):  
Test Stimulus ◽  
Stimulus Object ◽  
Object Segmentation ◽  
Matching Task ◽  
New Approach ◽  
Assimilation Effect ◽  
Black And White ◽  
Central Processing ◽  
Insight Into

The role of central processes in the assimilation effect can easily be shown qualitatively (de Weert and Spillmann, 1995 Vision Research35 1413 – 1419), but it is difficult to measure quantitatively because of the subtlety of the effect. In most experimental designs, the match stimulus differs greatly in appearance from the test stimulus, eg in size or configuration, and because these differences are far more striking than the assimilation effect, matching is difficult. Central processing, eg object segmentation, influences colour spreading. It is this property that we explored with a new approach: a matching task in which the match stimulus has the same properties (eg size and configuration) as the test stimulus. Object segmentation is forced by stereopsis-induced depth. The test stimulus consists of two depth planes, one with black dots and the other with white dots, on a homogeneous gray background. The match stimulus has the same configuration of black and white dots, but now squeezed into a single depth plane. The basic idea behind this stimulus is that assimilation mainly acts on the back plane of a scene (as can be shown experimentally). So, while keeping the appearance of the stimulus the same, subjects can focus on the assimilation effect itself. This new approach allows us to explore more aspects of the assimilation effect and gain insight into the processes involved.

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