Dataset Information

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

ABSTRACT: Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

SUBMITTER: Lai JW

PROVIDER: S-EPMC8201065 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

Lai Joel Weijia JW Ang Candice Ke En CKE Acharya U Rajendra UR Cheong Kang Hao KH

International journal of environmental research and public health 20210605 11

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally ...[more]

PMID: 34198829

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Project description:BackgroundInterview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.AimsTo sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording.Settings and designData collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute.Materials and methodsData of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification.Statistical analysisMann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications.ResultsAccuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances.ConclusionsSVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.

Project description:AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Project description:BackgroundMore than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.ObjectiveThis study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.MethodsWe searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.ResultsWe identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.ConclusionsAI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.

Dataset Information

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

Publications

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification.

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