scholarly journals Single and Cross-Disorder Detection for Autism and Schizophrenia

2021 ◽  
Author(s):  
Aleksander Wawer ◽  
Izabela Chojnicka ◽  
Lukasz Okruszek ◽  
Justyna Sarzynska-Wawer
Keyword(s):  
Deep Learning ◽  
Subjective Assessment ◽  
Autism Spectrum ◽  
Diagnostic Tools ◽  
Data Types ◽  
Learning Methods ◽  
Training Models ◽  
Machine Learning Model ◽  
Textual Data ◽  
Automated Methods

AbstractDetection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3) psychiatrists. Our article tests the effectiveness of several baseline approaches, such as bag of words and dictionary-based vectors, followed by a machine learning model. We employed two more refined Sentic text representations using affective features and concept-level analysis on texts. Further, we applied selected state-of-the-art deep learning methods for text representation and inference, as well as experimented with transfer and zero-shot learning. Finally, we also explored few-shot methods dedicated to low data size scenarios, which is a typical problem for the clinical setting. The best breed of automated methods outperformed human raters (psychiatrists). Cross-dataset approaches turned out to be useful (only from SCZ to ASD) despite different data types. The few-shot learning methods revealed promising results on the SCZ dataset. However, more effort is needed to explore the approaches to efficiently training models, given the very limited amounts of labeled clinical data. Psychiatry is one of the few medical fields in which the diagnosis of most disorders is based on the subjective assessment of a psychiatrist. Therefore, the introduction of objective tools supporting diagnostics seems to be pivotal. This paper is a step in this direction.

scholarly journals Identifying Condition-action Statements in Medical Guidelines: Three Studies using Machine Learning and Domain Adaptation

2021 ◽  
Author(s):  
Hossein Hematialam ◽  
Wlodek W. Zadrozny
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Guidelines ◽  
Domain Adaptation ◽  
Learning Methods ◽  
Text Understanding ◽  
Prior Art ◽  
Medical Guidelines ◽  
Conceptual Link ◽  
Automated Methods

Abstract Background: Medical guidelines provide the conceptual link between a diagnosis and a recommendation. They often disagree on their recommendations. There are over thirty five thousand guidelines indexed by PubMed, which creates a need for automated methods for analysis of recommendations, i.e., recommended actions, for similar conditions. Results: This article advances the state of the art in text understanding of medical guidelines by showing the applicability of transformer-based models and transfer learning (domain adaptation) to the problem of finding condition-action and other conditional sentences. We report results of three studies using syntactic, semantic and deep learning methods, with and without transformer-based models such as BioBERT and BERT. We perform in depth evaluation on a set of three annotated medical guidelines. Our experiments show that a combination of machine learning domain adaptation and transfer can improve the ability to automatically find conditional sentences in clinical guidelines. We show substantial improvements over prior art (up to 25%), and discuss several directions of extending this work, including addressing the problem of paucity of annotated data.Conclusion: Modern deep learning methods, when applied to the text of clinical guidelines, yield substantial improvements in our ability to find sentences expressing the relations of condition-consequence, condition-action and action.

scholarly journals ASD Diagnoses using Deep Learning and Neuroimaging as A Biomarker: A Review

2021 ◽  
Author(s):  
Sonali Beri ◽  
Arun Khosla
Keyword(s):  
Deep Learning ◽  
Neurodevelopmental Disorder ◽  
Repetitive Behavior ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Learning Methods ◽  
Developmental Factors ◽  
Learning Techniques ◽  
Autistic People ◽  
Neuro Imaging

Autism Spectrum Disorder (ASD) is a commonly occurring neurodevelopmental disorder characterized by problems occurring in social communication and the presence of restricted and repetitive behavior and interests. Up to now, ASD is being diagnosed considering clinical interview, behavior and developmental factors. Early diagnosis of it can help the autistic people to deal well in their lives. For this early detection different biomarker like Neuro-imaging data can be used which includes structural and functional magnetic resonance imaging. In order to explore the functional and structural differences in between TC and autistic group deep learning methods can be used. These deep learning methods will help in efficient classification and thus can help in autism diagnosis as well. In this paper studies related to various Deep Learning techniques used to diagnose autism are being looked at.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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