Analysis and Evaluation of Explainable Artificial Intelligence on
Suicide Risk Assessment
- URL: http://arxiv.org/abs/2303.06052v1
- Date: Thu, 9 Mar 2023 05:11:46 GMT
- Title: Analysis and Evaluation of Explainable Artificial Intelligence on
Suicide Risk Assessment
- Authors: Hao Tang, Aref Miri Rekavandi, Dharjinder Rooprai, Girish Dwivedi,
Frank Sanfilippo, Farid Boussaid, Mohammed Bennamoun
- Abstract summary: This study investigates the effectiveness of Explainable Artificial Intelligence (XAI) techniques in predicting suicide risks.
Data augmentation techniques and ML models are utilized to predict the associated risk.
Patients with good incomes, respected occupations, and university education have the least risk.
- Score: 32.04382293817763
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This study investigates the effectiveness of Explainable Artificial
Intelligence (XAI) techniques in predicting suicide risks and identifying the
dominant causes for such behaviours. Data augmentation techniques and ML models
are utilized to predict the associated risk. Furthermore, SHapley Additive
exPlanations (SHAP) and correlation analysis are used to rank the importance of
variables in predictions. Experimental results indicate that Decision Tree
(DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models achieve
the best results while DT has the best performance with an accuracy of 95:23%
and an Area Under Curve (AUC) of 0.95. As per SHAP results, anger problems,
depression, and social isolation are the leading variables in predicting the
risk of suicide, and patients with good incomes, respected occupations, and
university education have the least risk. Results demonstrate the effectiveness
of machine learning and XAI framework for suicide risk prediction, and they can
assist psychiatrists in understanding complex human behaviours and can also
assist in reliable clinical decision-making.
Related papers
- SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
Sepsis is the leading cause of in-hospital mortality in the USA.
Existing predictive models are usually trained on high-quality data with few missing information.
For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
arXiv Detail & Related papers (2024-07-24T04:47:36Z) - Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models.
Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
arXiv Detail & Related papers (2023-10-24T02:56:05Z) - MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data
Augmentation [58.93221876843639]
This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion.
It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space.
It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
arXiv Detail & Related papers (2023-10-04T01:36:30Z) - Improving Opioid Use Disorder Risk Modelling through Behavioral and Genetic Feature Integration [3.524972282521988]
Opioids are an effective analgesic for acute and chronic pain, but carry a risk of addiction leading to millions of opioid use disorder (OUD) cases and tens of thousands of premature deaths in the United States yearly.
We develop an experimental design and computational methods that combine genetic variants associated with OUD with behavioral features extracted from GPS and Wi-Fitemporal coordinates to assess OUD risk.
arXiv Detail & Related papers (2023-09-19T17:01:28Z) - Explainable AI for Malnutrition Risk Prediction from m-Health and
Clinical Data [3.093890460224435]
This paper presents a novel AI framework for early and explainable malnutrition risk detection based on heterogeneous m-health data.
We performed an extensive model evaluation including both subject-independent and personalised predictions.
We also investigated several benchmark XAI methods to extract global model explanations.
arXiv Detail & Related papers (2023-05-31T08:07:35Z) - Penalized Deep Partially Linear Cox Models with Application to CT Scans
of Lung Cancer Patients [42.09584755334577]
Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies.
The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients.
We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
arXiv Detail & Related papers (2023-03-09T15:38:16Z) - A New Approach for Interpretability and Reliability in Clinical Risk
Prediction: Acute Coronary Syndrome Scenario [0.33927193323747895]
We intend to create a new risk assessment methodology that combines the best characteristics of both risk score and machine learning models.
The proposed approach achieved testing results identical to the standard LR, but offers superior interpretability and personalization.
The reliability estimation of individual predictions presented a great correlation with the misclassifications rate.
arXiv Detail & Related papers (2021-10-15T19:33:46Z) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced
Data [81.00385374948125]
We present UNcertaInTy-based hEalth risk prediction (UNITE) model.
UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data.
We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD)
UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
arXiv Detail & Related papers (2020-10-22T02:28:11Z) - Gradient Boosting on Decision Trees for Mortality Prediction in
Transcatheter Aortic Valve Implantation [5.050648346154715]
Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning.
This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
arXiv Detail & Related papers (2020-01-08T10:04:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.