The Relationship Between Reductionism and Prediction in Psychiatry: A Survey

2021 ◽  
Author(s):  
Eren Asena ◽  
Henk Cremers
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Literature Review ◽  
Significant Role ◽  
Biological Psychiatry ◽  
Learning Models ◽  
Different Types ◽  
Survey Results ◽  
The Relationship ◽  
Machine Learning Models

Introduction. Biological psychiatry has yet to find clinically useful biomarkers despite mucheffort. Is this because the field needs better methods and more data, or are current conceptualizations of mental disorders too reductionistic? Although this is an important question, there seems to be no consensus on what it means to be a “reductionist”. Aims. This paper aims to; a) to clarify the views of researchers on different types of reductionism; b) to examine the relationship between these views and the degree to which researchers believe mental disorders can be predicted from biomarkers; c) to compare these predictability estimates with the performance of machine learning models that have used biomarkers to distinguish cases from controls. Methods. We created a survey on reductionism and the predictability of mental disorders from biomarkers, and shared it with researchers in biological psychiatry. Furthermore, a literature review was conducted on the performance of machine learning models in predicting mental disorders from biomarkers. Results. The survey results showed that 9% of the sample were dualists and 57% were explanatory reductionists. There was no relationship between reductionism and perceived predictability. The estimated predictability of 11 mental disorders using currently available methods ranged between 65-80%, which was comparable to the results from the literature review. However, the participants were highly optimistic about the ability of future methods in distinguishing cases from controls. Moreover, although behavioral data were rated as the most effective data type in predicting mental disorders, the participants expected biomarkers to play a significant role in not just predicting, but also defining mental disorders in the future.

scholarly journals Federated Learning in a Medical Context: A Systematic Literature Review

2021 ◽  
Vol 21 (2) ◽  
pp. 1-31
Author(s):  
Bjarne Pfitzner ◽  
Nico Steckhan ◽  
Bert Arnrich
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Privacy ◽  
Research Area ◽  
Learning Models ◽  
Related Data ◽  
Private Data ◽  
Large Databases ◽  
Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

scholarly journals Detecting Depression Through Gait Data: Examining the Contribution of Gait Features in Recognizing Depression

2021 ◽  
Vol 12 ◽  
Author(s):  
Yameng Wang ◽  
Jingying Wang ◽  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Frequency Domain ◽  
Common Mental Disorders ◽  
Microsoft Kinect ◽  
Training Data ◽  
Support Vector ◽  
Learning Models ◽  
Gait Features ◽  
Machine Learning Models

While depression is one of the most common mental disorders affecting more than 300 million people across the world, it is often left undiagnosed. This paper investigated the association between depression and gait characteristics with the aim to assist in diagnosing depression. Our dataset consisted of 121 healthy people and 126 patients with depression who diagnosed by psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders. Spatiotemporal, temporal-domain, and frequency-domain features were extracted based on the walking data of 247 participants recorded by Microsoft Kinect (Version 2). Multiple logistic regression was used to analyze the variance of spatiotemporal (12.55%), time-domain (58.36%), and frequency-domain features (60.71%) on recognizing depression based on Nagelkerke's R2 measure, respectively. The contributions of the different types of features were further explored by building machine learning models by using support vector machine algorithm. All the combinations of the three types of gait features were used as training data of machine learning models, respectively. The results showed that the model trained using only time- and frequency-domain features demonstrated the same best performance compared to the model trained using all the features (sensitivity = 0.94, specificity = 0.91, and AUC = 0.93). These results indicated that depression could be effectively recognized through gait analysis. This approach is a step forward toward developing low-cost, non-intrusive solutions for real-time depression recognition.

scholarly journals Optimization of Machine Learning in Various Situations Using ICT-Based TVOC Sensors

Micromachines ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1092
Author(s):  
Jae Hyuk Cho ◽  
Hayoun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Ground Truth ◽  
Information And Communications Technology ◽  
Learning Models ◽  
Computational Framework ◽  
Sensory Data ◽  
Total Volatile Organic Compounds ◽  
The Relationship ◽  
Machine Learning Models

A computational framework using artificial intelligence (AI) has been suggested in numerous fields, such as medicine, robotics, meteorology, and chemistry. The specificity of each AI model and the relationship between data characteristics and ground truth, allowing their guidance according to each situation, has not been given. Since TVOCs (total volatile organic compounds) cause serious harm to human health and plants, the prevention of such damages with a reduction in their occurrence frequency becomes not an optional process but an essential one in manufacturing, as well as for chemical industries and laboratories. In this study, with consideration of the characteristics of the machine learning technique and ICT (information and communications technology), TVOC sensors are explored as a function of grounded data analysis and the selection of machine learning models, determining their performance in real situations. For representative scenarios, considering features from an ICT semiconductor sensor and one targeting TVOC gas, we investigated suitable analysis methods and machine learning models such as LSTM (long short-term memory), GRU (gated recurrent unit), and RNN (recurrent neural network). Detailed factors for these machine learning models with respect to the concentration of TVOC gas in the atmosphere are compared with original sensory data to obtain their accuracy. From this work, we expect to significantly minimize risk in empirical applications, i.e., maintaining homeostasis or predicting abnormal situations to construct an opportune response.

scholarly journals What if Social Robots Look for Productive Engagement?

2021 ◽  
Author(s):  
Jauwairia Nasir ◽  
Barbara Bruno ◽  
Mohamed Chetouani ◽  
Pierre Dillenbourg
Keyword(s):  
Machine Learning ◽  
Learning Outcomes ◽  
State Of The Art ◽  
Learning Models ◽  
Collaborative Activity ◽  
Data Set ◽  
Performance Metric ◽  
The Relationship ◽  
Machine Learning Models ◽  
Productive Engagement

AbstractIn educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human–human–robot setup with 68 students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will (1) distinguish teams based on engagement that is conducive of learning; and (2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labelling based on the data.

scholarly journals Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences

2019 ◽  
Author(s):  
David B. Sauer ◽  
Da-Neng Wang
Keyword(s):  
Machine Learning ◽  
Nucleic Acid ◽  
Molecular Mechanisms ◽  
Mean Squared Error ◽  
Fluorescent Proteins ◽  
Optimal Growth ◽  
Multilayer Perceptrons ◽  
Learning Models ◽  
The Relationship ◽  
Machine Learning Models

AbstractThe link between sequence and phenotype is essential to understanding the molecular mechanisms of evolution, and the design of proteins and genes with specific properties. However, it is difficult to describe the relationship between sequence and protein or organismal phenotypes, due to the complex relationship between sequence, protein folding and activity, and organismal physiology. Here, we use machine learning models trained on individual families of proteins or nucleic acids to predict the originating species’ optimal growth temperatures or other quantitative phenotypes. Trained multilayer perceptrons (MLPs) outperformed linear regressions in predicting the originating species growth temperature from protein sequences, achieving a root mean squared error of 3.6 °C. Similar machine learning models were able to predict the binding affinity of mutant WW domain sequences, brightness of fluorescent proteins, and enzymatic activity of ribozymes. Notably, the trained models are protein or nucleic acid family specific and therefore useful in the design of biopolymers with particular properties. This method provides a new tool for the in silico prediction of quantitative biophysical and organismal phenotypes directly from sequence.

scholarly journals Tracking Major Sources of Water Contamination Using Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Jianyong Wu ◽  
Conghe Song ◽  
Eric A. Dubinsky ◽  
Jill R. Stewart
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Land Cover ◽  
Microbial Contamination ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning Models ◽  
The Relationship ◽  
Microbial Sources ◽  
Machine Learning Models

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.

scholarly journals Exploring the Relationship Between Mandatory Helmet Use Regulations and Adult Cyclists’ Behavior in California Using Hybrid Machine Learning Models

2021 ◽  
Author(s):  
Fatemeh Davoudi Kakhki ◽  
Maria Chierichetti
Keyword(s):  
Machine Learning ◽  
Sociodemographic Characteristics ◽  
Bicycle Helmet ◽  
Learning Models ◽  
Helmet Use ◽  
Bicycle Helmets ◽  
Hybrid Machine ◽  
The Impact ◽  
The Relationship ◽  
Machine Learning Models

In California, bike fatalities increased by 8.1% from 2015 to 2016. Even though the benefits of wearing helmets in protecting cyclists against trauma in cycling crash has been determined, the use of helmets is still limited, and there is opposition against mandatory helmet use, particularly for adults. Therefore, exploring perceptions of adult cyclists regarding mandatory helmet use is a key element in understanding cyclists’ behavior, and determining the impact of mandatory helmet use on their cycling rate. The goal of this research is to identify sociodemographic characteristics and cycling behaviors that are associated with the use and non-use of bicycle helmets among adults, and to assess if the enforcement of a bicycle helmet law will result in a change in cycling rates. This research develops hybrid machine learning models to pinpoint the driving factors that explain adult cyclists’ behavior regarding helmet use laws.

scholarly journals Machine Learning Based Prediction of Complex Bugs in Source Code

2019 ◽  
pp. 26-37
Author(s):  
Ishrat-Un-Nisa Uqaili ◽  
Syed Nadeem Ahsan
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Source Code ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Software Module ◽  
Different Types ◽  
Software Modules ◽  
Highly Correlated ◽  
Machine Learning Models

During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis. Several research works have been performed using software metrics and predict fault-prone software module. In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data. Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules. Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.

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