Table 3 Characteristics of the studies included in the systematic review
Author | Study Design | Purpose | population | Comparator | Outcome | Solution category |
|---|---|---|---|---|---|---|
Huang et al 2023 [23] | Retrospective cross-sectional study | Identify major risk factors affecting male sperm count using machine learning predictive models | Male individuals underwent annual health screening | Traditional multiple linear regression | Identified major risk factors affecting male sperm count | Prediction tool |
Chen et al 2023 [24] | observational and retrospective. | Develop and evaluate machine learning models for the prediction of cervical cancer | Patients at risk of cervical cancer | Comparison with other machine learning models | performance metrics evaluation for predicting cervical cancer in patients at risk | Prediction tool |
Hariprasad et al 2023 [25] | retrospective observational study | Develop an efficient risk prediction model for cervical cancer using machine learning techniques | Female patients | Comparison with existing models | Improved performance metrics in predicting cervical cancer risk | Prediction tool |
Al Ghadban et al 2023 [26] | Prospective case-cohort study | Utilize machine learning models on metabolic data to predict spontaneous preterm birth in pregnant women | Pregnant women at risk of spontaneous preterm birth | Standard clinical assessment | Accuracy of predicting spontaneous preterm birth | Prediction tool |
Shivangi et al 2023 [27] | observational and analytical | Predict and analyze Polycystic Ovary Syndrome using machine learning techniques | Women with polycystic ovary syndrome | Traditional diagnostic methods | Identification of important indicators | Prediction tool |
Allen et al 2023 [28] | Observational study | Utilize natural language processing techniques to indirectly identify psychosocial risks | Pregnant and postpartum women | N/A | Identify psychosocial risks during the perinatal period through the analysis of language patterns | Screening tool |
Chen et al 2023 [29] | Retrospective, observational study | Develop and evaluate a machine learning-based prediction model for prostate cancer | Patients with suspected prostate cancer | Comparison with classical PSA predictors | Improved accuracy in predicting prostate cancer diagnosis | Prediction tool |
Cersonsky et al 2023 [30] | Observational study | Create and refine machine learning models for predicting stillbirth | Pregnant women | Previous predictive models using logistic regression | Accuracy and sensitivity in predicting stillbirth | Prediction tool |
Shen et al 2023 [31] | modelling cost-effectiveness analysis | Assess the cost-effectiveness of AI-assisted liquid-based cytology testing for cervical cancer screening | Women in China | Traditional screening methods | Cost-effectiveness for cervical cancer screening | Screening tool |
Reátegui et al 2022 [32] | Observational and comparative study | Identify patterns in sociodemographic and clinical information related to cervical cancer in women with HPV | Women with an active sexual life | Different cytology results, age groups, and marital statuses | Identification of patterns related to cervical cancer | Assessment tool |
Xu et al 2022 [33] | Prospective | Develop a web-based risk prediction tool for HIV and STIs using machine learning algorithms | Individuals seeking HIV and STI risk assessment | Traditional risk assessment | Personalized risk assessment for HIV and STIs | prediction tool |
Li et al 2022 [34] | Retrospective cohort study | Develop and validate an algorithm for predicting the risk of preeclampsia in pregnant women | Pregnant women | N/A | Improved prediction accuracy and identification of risk factors for preeclampsia | prediction tool |
Amitai et al 2023 [35] | Cross-Sectional Study | To predict the risk of first-trimester miscarriage in cleavage-stage embryos during IVF | Women Cleavage-stage embryos | Traditional assessment methods | Prediction of first-trimester miscarriage | prediction tool |
Blass et al 2022 [36] | Retrospective cohort | Endometriosis prediction using machine learning algorithms | Women with endometriosis | N/A | Prediction model performance | prediction tool |
Pawar et al 2022 [37] | Cross-sectional | Develop a robust machine learning predictive model for maternal health risk assessment | Pregnant women | Traditional machine learning algorithms | Maternal health risk prediction | prediction tool |
Xu et al 2022 [38] | Retrospective study | Develop and validate a machine-learning-based risk prediction tool for HIV and three common STIs acquisition over the next 12 months | Individuals at risk of HIV and STI acquisition | N/A | Prediction of HIV and STI acquisition | prediction tool |
Jun et al 2022 [39] | Retrospective observational study | Identify the risk factors of cervical cancer in women | Women in a private hospital | Women without cervical cancer | Identification of risk factors for cervical cancer | prediction tool |
Bendifallah et al 2022 [40] | Retrospective cross-sectional study | Investigate the potential of machine learning algorithms as a screening approach for patients with endometriosis | Women with endometriosis | Traditional diagnostic methods | Accuracy of endometriosis diagnosis | Screening tool |
Nsugbe et al 2022 [41] | Retrospective study | To develop a predictive model for the early detection using machine learning | Women with endometrial cancer or atypical hyperplasia | Standard diagnostic methods | Early and accurate diagnosis of endometrial cancer | Diagnosis tool |
Sanderson et al 2020 [42] | Case-control study design | Predicting the risk of death by suicide following an emergency department visit for parasuicide | Individuals visited the emergency department for parasuicide | N/A | Quantification of suicide risk in a clinical setting | Prediction tool |
Bao et al 2021 [43] | Retrospective study | Predict the diagnosis of HIV and sexually transmitted infections among men who have sex with men | Men who have sex with men | N/A | Diagnosis of HIV and sexually transmitted infections | Prediction tool |
Metsker et al 2020 [44] | Retrospective, observational, data-driven | To develop a data-driven model using machine learning methods for the prediction of a labor due date based on pregnancy history | Female patient with underwent treatment | N/A | Prediction of a labor due date to allow proper resource planning | Prediction tool |
Petrozziello et al 2019 [45] | Observational, retrospective cohort study | To develop and evaluate the performance of MCNN for fetal compromise detection | Pregnant women in labor | Traditional visual examination of CTG | Improved accuracy in detecting fetal compromise during labor and delivery | Diagnosis tool |
Silva et al 2019 [46] | Experimental | To develop and evaluate the performance of LSTM networks for predicting cervical cancer | Women with suspected cervical cancer | performance comparison of different metaheuristic optimizers | Improved accuracy in cancer diagnosis | Prediction tool |
King et al 2018 [47] | Retrospective observational study | To develop and validate triage algorithms to predict STI diagnoses | MSM and young people | N/A | Identification of factors associated with new STI diagnoses | Prediction tool |
Bruno et al 2023 [48] | Cross-sectional Cohort study | Explore the potential of machine learning to predict changes in depressive symptoms | Patients with subclinical depression | Symptom changes over time | Forecasting of symptom changes using machine learning | Prediction tool |
Foltz et al 2022 [49] | Cross-Sectional Study | Reflect on the nature of measurement in language-based automated assessments of patients’ mental state and cognitive function | Patients undergoing automated assessment | Traditional assessment methods | Improved measurement accuracy and efficiency | Prediction tool |
Adeli et al 2023 [50] | Longitudinal study | Dynamically estimate the short-term risk of falls (within the next 4 weeks) using ambient gait monitoring and clinical data | Older adults with dementia | N/A | Estimation of fall risk within the next 4 weeks | Prediction tool |
Msosa et al 2023 [51] | Retrospective observational | Develop predictive models for mental health crisis prediction among individuals with depression | Mental health service users | N/A | Development and evaluation of predictive models for mental health crisis prediction | Assessment tool |
Ilias et al 2023 [52] | Experimental study | Propose and evaluate a solution for identifying stress and depression in social media using transformer-based models | Social media users | Performance of transformer-based models | Improved model performance to identify differences between stressful and nonstressful texts | Screening tool |
Biplob et al 2023 [53] | Predictive study | Use machine learning algorithms to predict suicidal ratios across different continents | Individuals at risk of suicide | Different machine learning algorithms performance | Prediction of suicidal ratio | Prediction tool |
Popat et al 2023 [54] | Computational and clinical research study | To explore the use of uncertainty-aware deep learning models for diagnosing mental health conditions using clinical patient health record data | Patients with mental health conditions | Traditional decision-making approaches meaning health care professionals | Improved clinical decision-making | Support tool |
Akhlaghi et al 2023 [55] | Prospective observational study | Demonstrate deep Learning models to support clinical decision-making | Patients with mental health conditions | Traditional machine learning models and clinical diagnostic methods | Improved accuracy and reliability of clinical decision-making | Support tool |
Berge et al 2023 [56] | Qualitative co-design workshop | Design and evaluate AI-enhanced decision support during telephone triage | Mental health patient | Traditional telephone triage workflow | Improved clinical assessment and documentation | Support tool |
Lu et al 2022 [57] | Retrospective cohort study | Develop and evaluate ML models for the prediction of suicidal events | Inmates in a correctional setting | Comparison of the performance of AI algorithms | Improved prediction of suicidal and self-injurious events and enhanced sensitivity and specificity | Prediction tool |
Miles et al 2022 [58] | Cohort study | Develop and validate a risk prediction model to support ambulance clinical transport decisions | Conveyed ambulance patients | Standard transport decision-making | Avoidable ED attendances | Prediction tool |
Aulia et al 2022 [59] | Experimental study | Develop a machine learning solution for predicting depression | Indonesian Twitter users | Different data scenarios and pre-trained language models | Developed machine learning approaches for predicting depression | Prediction tool |
Skaik et al 2022 [60] | Computational research study | Develop and evaluate a predictive model for depression by filling out the Beck’s Depression Inventory questionnaire | Social media users in Canada | N/A | Predicted depression levels | Prediction tool |
Monreale et al 2022 [61] | Observational and analytical | Identify mental-health related patient from subreddits | Reddit users with addiction, anxiety, and depression | Performance of different models and subsets of LIWC features | Detection and classification accuracy | Prediction tool |
Shea et al 2022 [62] | Observational cohort study | Identify autistic adults enrolled in Medicaid programs | Autistic adults not enrolled in Medicaid | Gold standard clinical assessment | Developed and evaluated a PRE tool to identify Medicaid-enrolled autistic adults | Diagnosis tool |
Sun et al 2022 [63] | Mixed study | Improving user control for explainable online symptom checkers | Users of online symptom checkers | Static disclosure of all explanations | Perceived transparency and affective trust | Support tool |
McCosker et al 2023 [64] | Qualitative case study | Propose an integrated approach to managing the interaction between human and machine moderators | Mental health patient | Traditional moderation practices | Improved mental health services | Support tool |
Rozova et al 2022 [65] | Retrospective analysis | Develop an automated system for detecting self-harm presentations in ED triage notes using NLP | Emergency Department (ED) patients | Traditional machine learning and deep learning Model | Accurate identification of self-harm presentations | Diagnosis tool |
Martinez-Eguiluz et al 2023 [66] | Predictive study | To evaluate machine learning algorithms for the classification of Parkinson’s disease | Patients with Parkinson’s disease | Comparison of performance of machine learning algorithms | Prediction of Parkinson’s disease based on non-motor clinical features | Prediction tool |
Xu et al 2021 [67] | Secondary anonymous data analysis | suicide detection in online counseling systems | Users aged 11–35 | With BiLSTM deep learning model | Identified crisis cases and provided real-time feedback to counselors | Diagnosis tool |
Dharma et al 2021 [68] | Computational analysis | Develop a model for predicting mental health disorders among tech workers | Tech workers | Performance of SVM and FCNN algorithms in predicting mental health disorders | Accuracy rate of the algorithms in predicting mental health disorders | Diagnosis tool |
Annapureddy et al 2021 [69] | Retrospective observational study | Develop a personalized decision support system to predict the risk of persistent PTSD severity in veterans | Combat veterans suffering from PTSD | Standard care or other predictive models | Improved prediction and prevention of long-term crisis in veterans with PTSD | Assessment tool |
Ameratunga et al 2022 [70] | Cross-sectional | To investigate the prevalence and predictors of post-traumatic stress symptoms in injured | Injured New Zealanders | Hospitalised vs. non-hospitalised participants | Prevalence and predictors of post-traumatic stress symptoms | Prediction tool |
Simpson et al 2021 [71] | Retrospective cohort study | Evaluate the performance of the (C-SSRS) Screener in predicting suicide risk and self-harm after ED discharge | Adult patients in the emergency department (ED) | N/A | Prediction of suicide risk and ED visits for self-harm | Prediction tool |
van der Schyff et al 2023 [72] | Interventional Study | To explore the potential of AI-based chatbots to provide mental health support | Mental health chatbot user | Traditional mental health support | Effectiveness and accessibility | Support tool |
Haines-Delmont et al 2020 [73] | Retrospective analysis | Explore the feasibility of using digital phenotyping and machine learning to predict suicide risk using phone measurements | Patients in acute mental health | N/A | Prediction of suicide risk using data collected from the SWiM app | Prediction tool |
Lin et al 2020 [74] | Historical cohort study | To utilize machine learning techniques to predict the presence of suicide ideation in military personnel | Military men and women aged 18–50 years | N/A | Prediction of suicide ideation in military personnel based on psychological stress domains | Prediction tool |
Baek et al 2020 [75] | Predictive modeling study | Accurately predict the risk of depression based on various contextual factors | Individuals at risk of depression | Traditional regression analysis | Accurate prediction of depression risk based on various factors | Prediction tool |
Howard et al 2020 [76] | Predictive modeling study | Benchmark multiple methods of text feature representation and compare their downstream | social media users | N/A | Prediction of risk classification for social media posts | Prediction tool |
Ferraro et al 2020 [77] | Predictive and data-driven study | To develop and evaluate automated text classification methods for triaging from online group texts | Internet support groups for mental health | Traditional manual moderation | Identification of crisis posts | Screening tool |
Chen et al 2020 [78] | Retrospective observational study | To develop and evaluate natural language processing-based models for predicting short-term emergency department | Adult non-trauma outpatients | Reference model without NLP methods | Improvement in performance metrics (MSE or MAE) for predicting short-term emergency department length of stay | Prediction tool |
Si et al 2019 [79] | Cross-sectional study | To distinguish between the speech of patients who suffer from mental disorders causing psychosis | Participant with psychosis | Utilization of word embeddings and CNN | Prediction rate in distinguishing between the speech of psychosis patients and healthy individuals | Prediction tool |
Wang et al 2019 [80] | Computational linguistic analysis | To develop a reliable and efficient method for identifying depression risk in Chinese microblogs | Users of Chinese microblogs | N/A | Assessment of depression risk based on microblog content | Assessment tool |
Shrestha et al 2019 [81] | Observational study | Address the problem of detecting depressed users in online forums | Users of the ReachOut.com online forum | Comparison of linguistic features to evaluate their effectiveness | Detection of depressed users and identification of at-risk individuals for appropriate support and intervention | Prediction tool |
McCoy et al 2019 [82] | Retrospective cohort study | To determine the length of stay in ED | Children and adolescents in ED | Comparison of dimensional psychopathology scores | Length of stay and probability of hospital admission | Prediction tool |
Yang et al 2020 [83] | Cross-sectional study | To enhance understanding of the underlying psychosocial factors associated with dementia in older adults to improve diagnosis | Older adults aged 50 years | N/A | Identification of potential risk factors for dementia | Prediction tool |
Milne et al 2019 [84] | Retrospective | To evaluate the effectiveness of an automated triage system in improving moderator responsiveness in online peer support | Moderators of online peer support forums | Moderator behaviour before the introduction of the triage system | Evaluation of the accuracy of the triage systems | Support tool |
Singh et al 2018 [85] | Observational study | To explore the potential of using mobile phone metadata to automatically assess an individual’s mental health | People with mental health problem | Traditional assessment methods | Automated and accurate mental health assessment | Assessment tool |