Automated Prediction Model Research Articles

Attention-Enhanced Guided Multimodal and Semi-Supervised Networks for Visual Acuity (VA) Prediction after Anti-VEGF Therapy

The development of telemedicine technology has provided new avenues for the diagnosis and treatment of patients with DME, especially after anti-vascular endothelial growth factor (VEGF) therapy, and accurate prediction of patients’ visual acuity (VA) is important for optimizing follow-up treatment plans. However, current automated prediction methods often require human intervention and have poor interpretability, making it difficult to be widely applied in telemedicine scenarios. Therefore, an efficient, automated prediction model with good interpretability is urgently needed to improve the treatment outcomes of DME patients in telemedicine settings. In this study, we propose a multimodal algorithm based on a semi-supervised learning framework, which aims to combine optical coherence tomography (OCT) images and clinical data to automatically predict the VA values of patients after anti-VEGF treatment. Our approach first performs retinal segmentation of OCT images via a semi-supervised learning framework, which in turn extracts key biomarkers such as central retinal thickness (CST). Subsequently, these features are combined with the patient’s clinical data and fed into a multimodal learning algorithm for VA prediction. Our model performed well in the Asia Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition, earning fifth place in the overall score and third place in VA prediction accuracy. Retinal segmentation achieved an accuracy of 99.03 ± 0.19% on the HZO dataset. This multimodal algorithmic framework is important in the context of telemedicine, especially for the treatment of DME patients.

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

Implementing an Automated Prediction Model to Improve Prescribing of HIV Preexposure Prophylaxis

Summary Antiretroviral preexposure prophylaxis (PrEP) is nearly 100% effective at decreasing HIV acquisition but is underused in priority populations. Primary care clinicians need tools to help them identify persons likely to benefit from PrEP use and prescribe it when appropriate. The researchers developed and validated an automated decision support tool with interactive alerts in the electronic health record to increase PrEP discussions and prescribing in primary care. They piloted the tool at three federally qualified health centers and assessed feasibility, acceptance by clinicians, and preliminary impact on PrEP care. Of 33,803 patients who visited the pilot clinics from July 2022 through January 2023, providers received PrEP alerts at the point of care for 2.2% of patients, demonstrating feasibility. Although numbers of PrEP prescriptions remained low, the proportion of all patients with new PrEP prescriptions was 4.5 times higher at pilot clinics compared with matched control clinics (0.09% vs. 0.02%). Implementation of the decision support tool was associated with a statistically nonsignificant 5.5% increase in HIV tests per 100 patients. In qualitative interviews, providers said the tool facilitated PrEP discussions with patients, particularly for those patients who would not have initiated discussions because of stigma. The researchers found that acceptance, use, and impact of machine-learning models for PrEP depends on collaborating with and building trust among providers, including blending a data-driven approach to identifying patients at increased risk for HIV acquisition with providers’ traditional decision-making framework. These approaches could be useful for health care organizations seeking to implement automated prediction models across all areas of medicine.

An Optimal Modified Faster Region CNN Model for Diagnosis of Liver Diseases from Ultrasound Images

In recent years, the rate of mortality and morbidity increased due to uncontrolled conditions of liver diseases. Liver disorders are mainly caused because of inhalation of toxic gases, consumption of contaminated food, alcohol usage, etc. If the progression of liver disease is not detected at an early stage, it seriously threatens human life. Early diagnosis and timely treatment are the only ways to prevent such life-threatening diseases. This paper mainly aims to design an automated prediction model using ultrasound liver images. Due to non-invasive, cost-effective, and real-time imaging abilities, the ultrasound becomes most preferable modality. Therefore, the main purpose of the paper is at predicting liver diseases using the proposed modified faster region-based convolutional neural network-based hybrid grasshopper tunicate swarm (MFRCNN-HGTS) approach. The distortions in the raw ultrasound liver images are removed and the number of training images is increased by the augmentation process. The augmented data samples are then allowed to pass through a modified faster region-based convolutional neural network (MFRCNN) classifier to predict normal and abnormal instances from the images. To improve classification accuracy, the weight factors are assigned with appropriate values using the hybrid grasshopper tunicate swarm (HGTS) algorithm. The proposed MFRCNN-HGTS approach predicts liver diseases accurately and classifies them based on texture features as normal, cirrhosis, or hepatitis. The experimental setup is performed using MATLAB R2016a software. The proposed MFRCNN-HGTS method is evaluated by comparing it with other traditional methods. The analytic result illustrates that the proposed MFRCNN-HGTS techniques achieve 98.45% accuracy over other techniques.

Enhancing Accuracy and Explainability of Recidivism Prediction Models

Predicting recidivism is a challenging task, but it helps support courts in their decision-making process. Automated prediction models suffer from low accuracy and are associated with criticism for biased and unexplainable decision-making. In this poster, we present different machine-learning models with just a few selected features that achieve accuracies as good as models that use larger sets of features. In addition, we investigate the influencing features that contribute to recidivism prediction, which can enhance the explainability of the learned models.

A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.

An automated heart disease prediction approach using linearly support vector regression and stacked linear swarm optimization

In today’s world, mining and learning applications play an essential role in healthcare sectors and intend to transform all the data into an understandable form. However, the healthcare sectors require an automated disease prediction system for better medical analysis and emphasize better prediction accuracy for evaluation purposes. In this paper, a new automated prediction model based on Linearly Support Vector Regression and Stacked Linear Swarm Optimization (LSVR-SLSO) has been proposed to predict heart disease accurately. Primarily, the features are analyzed in a linear and non-linear manner using LSVR feature learning approaches. The extracted features are then fed into the SLSO model in order to extract the global optimal solutions. These global solutions will reduce the data dimensionality and computational complexity during the evaluation phase. Moreover, the optimal solution facilitates the proposed model to predict heart disease appropriately. The simulation can be carried out through the MATLAB environment by utilizing a publicly available benchmark heart disease dataset. The performance results evident that the proposed LSVR-SLSO model can efficiently predict heart disease with superior accuracy of 98%, precision of 98.76%, and recall of 99.7% when compared with conventional approaches. The better performance of the proposed model will pave the way to act as an effective clinical decision support tool for physicians during an emergency.

The automation of the development of classification models and improvement of model quality using feature engineering techniques

Recently pipelines of machine learning-based classification models have become important to codify, orchestrate, and automate the workflow to produce an effective machine learning model. In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. The proposed framework is extendable and configurable by adding algorithms supported by the CARET package implemented in the R programming language. This framework can generate different machine learning models, which provide comparable results compared to other studies. The framework allows practitioners and researchers to automatically generate different classification models. This research used High-Resolution Orbitrap-based Mass Spectrometers (HRMS) data to create automated prediction models for the first time in literature. We demonstrated the applicability of feature engineering techniques such as data imputation, transformation (e.g., scaling, centering, etc.), and data balancing using several case studies and the proposed semi-automated framework. We showed how the initial prediction models can be improved using the proposed framework.

Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features

Early automated prediction model for the diagnosis and detection of children with autism spectrum disorders based on effective sociodemographic and family characteristic features

O-122 Reporting on the value of Artificial Intelligence in predicting the optimal embryo for transfer: A systematic review and meta-analysis

Abstract Study question Are Artificial Intelligence (AI) based models effective in robustly predicting in vitro fertilization (IVF) outcome by assessing embryo quality? Summary answer The majority of the AI-based models could provide an accurate prediction regarding live birth, clinical pregnancy, clinical pregnancy with fetal heartbeat and embryo ploidy status. What is known already Precision and consistency in embryo quality evaluation are of paramount importance regarding the outcome of an IVF cycle. Numerous embryo grading and evaluation systems, employing morphological and morphokinetical assessment, have been proposed but without reaching a consensus yet. The main limitation of the aforementioned assessment systems is that they depend on human evaluation, which may be subject to subjectivity and interobserver variation. Thus, automated prediction models may be essential to optimize objectivity and reliability of embryo grading. Artificial neural network models may process microscopy images or time-lapse videos as input to predict the embryos’ potential competency. Study design, size, duration A systematic review and meta-analysis including 18 published studies. The population consists of preimplantation embryos suitable for embryo transfer in IVF/ICSI cycles following employment of an AI-based prediction model. The outcome measures are prediction of live birth, clinical pregnancy, clinical pregnancy with heartbeat and ploidy status. Participants/materials, setting, methods A systematic search of the literature was performed in the databases of Pubmed/Medline, Embase, and Cochrane Central Library limited to articles published in English up to August 2021. The initial search yielded a total of 694 studies with 97 of them being duplicates and other 579 being excluded on the grounds of not fulfilling inclusion criteria. Following full-text screening and citation mining a total of 18 studies were identified to be eligible for inclusion. Main results and the role of chance Four studies reported on prediction of live birth. The sensitivity was 70.6% (95%C.I.: 38.1-90.4%) and specificity was 90.6% (95%C.I.:79.3-96.1%). The Area Under the Curve (AUC) of the Summary Receiver Operating Characteristics (SROC) curve was 0.905, while the partial AUC (pAUC) was 0.755. Employing the Bayesian approach, the total Observed:Expected ratio (O:E) was 1.12 (95%CI: 0.26–2.37; 95%PI:0.02-6.54). Ten studies reported on prediction of clinical pregnancy. The sensitivity and the specificity were 71% (95%C.I.: 58.1-81.2%) and 62.5% (95%C.I.: 47.4-75.5%) respectively. The AUC was 0.716, while pAUC was 0.693. Moreover, the total O:E ratio was 0.92 (95%CI: 0.61–1.28; 95%PI:0.13-2.43). Eight studies reported on prediction of clinical pregnancy with fetal heartbeat the sensitivity was 75.2% (95%C.I.: 66.8-82%) and the specificity was 55.3% (95%C.I.: 41.2-68.7%). The AUC was 0.722, while the pAUC was 0.774. The O:E ratio was 0.77 (95%CI: 0.54 – 1.05; 95%PI: 0.21-1.62). Four studies reported on the ploidy status of the embryo. The sensitivity and specificity were 59.4% (95%C.I.: 45.0-73.1%) and 79.2% (95%C.I.: 70.1-86.1%) respectively. The AUC was 0.751 and the pAUC was 0.585. The total O:E ratio was 0.86 (95%CI: 0.42 – 1.27; 95%PI: 0.03-1.83). Limitations, reasons for caution The limited number of studies fulfilling inclusion criteria, along with the different designs applied when developing AI models which may lead to increased heterogeneity, stand as limitations. Inclusion of women regardless of their age presents as another limitation, as advanced maternal age has been associated with diminished IVF outcomes. Wider implications of the findings Albeit, our findings support that AI is a highly promising tool in the era of personalized medicine providing precise predictions it does not appear to considerably surpass human prediction capabilities. More studies and more collaborations between the developers are of paramount importance prior to AI becoming the gold standard. Trial registration number Not applicable

Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data.

Colon and rectal cancers are the most common kinds of cancer globally. Colon cancer is more prevalent in men than in women. Early detection increases the likelihood of survival, and treatment significantly increases the likelihood of eradicating the disease. The Surveillance, Epidemiology, and End Results (SEER) programme is an excellent source of domestic cancer statistics. SEER includes nearly 30% of the United States population, covering various races and geographic locations. The data are made public via the SEER website when a SEER limited-use data agreement form is submitted and approved. We investigate data from the SEER programme, specifically colon cancer statistics, in this study. Our objective is to create reliable colon cancer survival and conditional survival prediction algorithms. In this study, we have presented an overview of cancer diagnosis methods and the treatments used to cure cancer. This paper presents an analysis of prediction performance of multiple deep learning approaches. The performance of multiple deep learning models is thoroughly examined to discover which algorithm surpasses the others, followed by an investigation of the network's prediction accuracy. The simulation outcomes indicate that automated prediction models can predict colon cancer patient survival. Deep autoencoders displayed the best performance outcomes attaining 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC).

Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.

Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.

An automated clinical decision support system for predicting cardiovascular disease using ensemble learning approach

AbstractWith the vast advancements in the medical domain, earlier prediction of disease plays a substantial role in enhancing the healthcare quality and assists in taking better decisions making during emergency times. Most of the existing research concentrates on modeling an automated prediction model for heart disease and the risk factors. Nevertheless, accurate classification is a vital challenge in heart disease diagnosis where the managing of high‐dimensional data increases the execution time of existing classifiers. In this paper, a new ensemble model has been proposed with the aid of random subspace and K‐nearest neighbor (RSS‐KNN) scheme for earlier prediction of heart disease. Primarily, the proposed scheme implements an isolation‐based outlier removal mechanism to eradicate the noises and outliers in the distributed data. Subsequently, the essential features are identified using RSS by varying the testing and training errors in the evaluation phase. The extracted features are then fed into KNN for the accurate classification of heart disease. Finally, an enhanced squirrel optimizer has been employed in the proposed scheme to obtain the global results which balance the exploration as well as exploitation issues and eliminate the over‐fitting problems. The simulation results manifest that the accuracy (without features) of the proposed ensemble RSS‐KNN scheme in the UCI ML dataset is 97.65%, accuracy (with features) is 98.56%, and specificity is 98.10% when compared with existing state‐of‐the‐art classifiers.

Прогнозирование развития реактивного артрита у детей с дисплазией соединительной ткани

Objective. To develop a model for automated prediction of the development of recurring reactive arthritis in children with symptoms of connective tissue dysplasia (CTD). Patients and methods. The work included two stages. At the first stage, in order to identify the predisposing factors to the development of reactive arthritis (ReA), two age- and sex-representative groups of children aged 3-17 years were formed: group 1 consisted of children with ReA (50 patients), group 2 – comparison group (25 conditionally healthy children from the general population). All children underwent a standard clinical examination with determination of CTD symptoms, and microbiological examination, including evaluation of the state of microbiocenosis of the distal colon by degrees; the ability of isolated microorganisms to biofilm formation (BF); levels of lactoferrin (LF) and lysozyme (LZC) in coprofiltrates of children. At the second stage, to create an automated prediction model, two additional groups of children were formed: 30 conditionally healthy children with CTD symptoms without arthritis and 30 conditionally healthy children without CTD symptoms. Stepwise discriminant analysis (DA) and the method of resultants mapping were used. Results. The factors determining the risk of ReA development in children with connective tissue dysplasia were identified. Highly informative markers associated with the development and course of ReA were established: the presence of external phenotypic symptoms and dysplasia of internal organs; dysbiotic disorders of the distal large intestine; high rates of microbial contamination and BF of intestinal microsymbionts; increased levels of LF and LZC in coprofiltrates of children. Conclusion. The diagnostic algorithm for automated prediction of the development of arthritis recurrence was developed, which allows primary care physicians to solve the problem of secondary prevention of ReA. Key words: reactive arthritis, children, connective tissue dysplasia, intestinal microbiota, prediction method

푸리에 변환 기반 스케줄 분석 알고리즘을 적용한 건물 에너지 소비량 예측 모델

Building energy demand currently accounts for 40% of the total energy demand, which has a great influence on the planning and operation of the energy market by energy suppliers, and its importance has increased significantly with the advent of the smart grid. Variables affecting the use of building energy include the identification of environmental conditions, historical conditions, and schedule conditions, and these factors have a sophisticated effect on buildings. One of the most influential variables is the building schedule. Because each building has its own schedule, standardized schedules cannot be applied to various buildings, and it is difficult for non-experts to analyze or predict schedules in these cases. The aim of this paper is to propose a high-precision building energy demand prediction model by using a Fourier transform-based time series prediction model, that automatically analyzes and predicts the schedule to be applied when predicting building energy demand. In order to compare with the existing method, the six buildings are divided into schedules when they are not scheduled, weekdays/weekends, days of week and when schedule analysis algorithm are applied. Machine learning is performed using the LSTM model, and prediction accuracy is verified through the CvRMSE and MBE. There was an average difference of 15.37% based on the CvRMSE, and all predictions were predictable when the automated prediction model was applied. This study can be used as a building energy operation plan for the creation and implementation of a future energy-efficient smart grid system.

Data-Driven Bandwidth Prediction Models and Automated Model Selection for Low Latency

Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate when lower latency is desired due to the shorter time available to react to bandwidth changes, and when mobile networks are involved due to their inherently more frequent and potentially larger bandwidth fluctuations. Any inaccuracy in bandwidth prediction results in flawed adaptation decisions, which will in turn translate into a diminished viewer experience. We propose an Automated Model for Prediction (AMP) that encompasses techniques for bandwidth prediction and model auto-selection specifically designed for low-latency live steaming with chunked transfer encoding. We first study statistical and computational intelligence techniques to implement a suite of bandwidth prediction models that can work accurately under a broad range of network conditions, and second, we introduce an automated prediction model selection method. We confirm the effectiveness of our solution through trace-driven live streaming experiments.

ThalPred: a web-based prediction tool for discriminating thalassemia trait and iron deficiency anemia

BackgroundThe hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT.MethodsWe retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k-nearest neighbor (k-NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices.ResultsThe data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively.ConclusionThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

Bearing Fault Diagnosis Using Empirical Mode Decomposition, Entropy Based Features And Data Mining Techniques

Rolling bearing is an important part of rotating machinery. Working condition of bearing will directly affect the operation and function of equipment. Therefore, the testing and fault diagnosis of the running state of bearing is significant. A common method for extracting the mechanical fault is assuming that vibration signals are stable. But the real measured signals often are non- stationary. For this nonlinear phenomenon, it is difficult to solve the problem fundamentally by the traditional power spectrum analysis .In this present work aims to formulate an automated prediction model using vibration signals of various bearing conditions by using EMD (empirical mode decomposition) and entropy based features and different classification algorithms.

Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

In this present work aims to formulate an automated prediction model using vibration signals of various gear operating conditions by using EMD (empirical mode decomposition) and spectral features and different classification algorithms. In this present work empirical mode decomposition (EMD) is a signal processing technique used to extract more useful fault information from the vibration signals. The proposed method described in following parts gear test rig, data acquisition system, signal processing, feature extraction and classification algorithms and finally identification. Meanwhile, in order to remove the redundant and irrelevant spectral features and classification algorithms, data mining is implemented and it showed promising prediction results.

Communications-based early detection of gambling-related problems in online gambling

Most algorithms developed for the early identification of gambling-related problems rely on predictors aggregated out of transactional gambling data. However, as a notable extension, one algorithm uses predictors derived from written correspondence with players and thereby opens up a so far unused resource for the early detection of gambling-related problems. In this article, a sample of 1008 emails from self-excluders and controls to the customer services of an online gambling operator was reanalysed to explore the possibility of using automated text analysis software to extract quantitative markers from written player correspondence. For this purpose a text analysis tool, using psychometrically validated English and German dictionaries, was applied. While the classification results that were based solely on automated text analysis were nearly on a level with those attained by human assessment, the application of an automated prediction model can even add incremental validity to human judgements. A combined model, relying on human rating as well as the scales Anger, Time and Causation, derived from automated text analysis, displayed improved validity and classification rate. Discussed in the light of practical application, the results indicate that automated text analysis can be deployed as an expert system to prioritize cases and to support human judgement.