The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

Secondary use of Structured Electronic Health Records Data: From Observational Studies to Deep Learning-based Predictive Modeling

With the wide adoption of electronic health records (EHRs), researchers, as well as large healthcare organizations, governmental institutions, insurance, and pharmaceutical companies have been interested in leveraging this rich clinical data source to extract clinical evidence and develop predictive algorithms. Large vendors have been able to compile structured EHR data from sites all over the United States, de-identify these data, and make them available to data science researchers in a more usable format. For this dissertation, we leveraged one of the earliest and largest secondary EHR data sources and conducted three studies of increasing scope. In the first study, which was of limited scope, we conducted a retrospective observational study to compare the effect of three drugs on a specific population of approximately 3,000 patients. Using a novel statistical method, we found evidence that the selection of phenylephrine as the primary vasopressor to induce hypertension for the management of nontraumatic subarachnoid hemorrhage is associated with better outcomes as compared to selecting norepinephrine or dopamine. In the second study, we widened our scope, using a cohort of more than 100,000 patients to train generalizable models for the risk prediction of specific clinical events, such as heart failure in diabetes patients or pancreatic cancer. In this study, we found that recurrent neural network-based predictive models trained on expressive terminologies, which preserve a high level of granularity, are associated with better prediction performance as compared with other baseline methods, such as logistic regression. Finally, we widened our scope again, to train Med-BERT, a foundation model, on more than 20 million patients’ diagnosis data. Med-BERT was found to improve the prediction performance of downstream tasks that have a small sample size, which otherwise would limit the ability of the model to learn good representation. In conclusion, we found that we can extract useful information and train helpful deep learning-based predictive models. Given the limitations of secondary EHR data and taking into consideration that the data were originally collected for administrative and not research purposes, however, the findings need clinical validation. Therefore, clinical trials are warranted to further validate any new evidence extracted from such data sources before updating clinical practice guidelines. The implementability of the developed predictive models, which are in an early development phase, also warrants further evaluation

딥러닝 기반 생존분석이 적용된 심혈관질환 위험 평가 모델 성능 향상을 위한 콕스 모형과 결합된 하이브리드 접근법: 헬스케어-환경 연계 데이터 활용 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 박상민 .Background and aims: The contribution of different cardiovascular disease (CVD) risk factors for the risk evaluation and predictive modeling for incident CVD is often debated. Also, to what extent data on CVD risk factors from multiple data categories should be collected for comprehensive risk assessment and predictive modeling for CVD risk using survival analysis is uncertain despite the increasing availability of the relevant data sources. This study aimed to evaluate the contribution of different data categories derived from integrated data on healthcare and environmental exposure to the risk evaluation and prediction models for CVD risk using deep learning based survival analysis in combination with Cox proportional hazards regression and Cox proportional hazards regression. Methods: Information on the comprehensive list of CVD risk factors were collected from systematic reviews of variables included in the conventional CVD risk assessment tools and observational studies from medical literature database (PubMed and Embase). Each risk factor was screened for availability in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) linked to environmental exposure data on cumulative particulate matter and urban green space using residential area code. Individual records of 137,249 patients more than 40 years of age who underwent the biennial national health screening between 2009 and 2010 without previous history of CVD were followed up for incident CVD event from January 1, 2011 to December 31, 2013 in the NHIS-NSC with data linkage to environmental exposure. Statistics-based variable selection methods were implemented as follows: statistical significance, subset with the minimum (best) Akaike Information Criteria (AIC), variables selected from the regularized Cox proportional hazards regression with elastic net penalty, and finally a variable set that commonly meets all the criteria from the abovementioned statistical methods. Prediction models using Cox proportional hazards deep neural network (DeepSurv) and Cox proportional hazards regression were constructed in the training set (80% of the total sample) using input feature sets selected from the abovementioned strategies and progressively adding input features by data categories to examine the relative contribution of each data type to the predictive performance for CVD risk. Performance evaluations of the DeepSurv and Cox proportional hazards regression models for CVD risk were conducted in the test set (20% of the total sample) with Unos concordance statistics (C-index), which is the most up-to-date evaluation metrics for the survival models with right censored data. Results: After the comprehensive review, data synthesis, and availability check, a total of 31 risk factors in the categories of sociodemographic, clinical laboratory test and measurement, lifestyle behavior, family history, underlying medical conditions, dental health, medication, and environmental exposure were identified in the NHIS-NSC linked to environmental exposure data. Among the models constructed with different variable selection methods, using statistically significant variables for DeepSurv (Unos C-index: 0.7069) and all of the variables for Cox proportional hazards regression (Unos C-index: 0.7052) showed improved predictive performance for CVD risk, which was a statistically significant increase (p-value for difference in Unos C-index: <0.0001 for both comparisons) compared to the models with basic clinical factors (age, sex, and body mass index), respectively. When all and statistically significant variables in each data category from sociodemographic to environmental exposure were progressively added as input features into DeepSurv and Cox proportional hazards regression for predictive modeling for CVD risk, the DeepSurv model with statistically significant variables pertaining to the sociodemographic factors, clinical laboratory test and measurement, and lifestyle behavior data showed the notable performance that outperformed Cox proportional hazards regression model with statistically significant variables added up to the medication category. Extensive data linkage to environmental exposure on cumulative particulate matter and urban green space offered only marginal improvement for the predictive performance of DeepSurv and Cox proportional hazards regression models for CVD risk. Conclusion: To obtain the best predictive performance of DeepSurv model for CVD risk with minimum number of input features, information on sociodemographic, clinical laboratory test and measurement, and lifestyle behavior should be primarily collected and used as input features in the NHIS-NSC. Also, the overall performance of DeepSurv for CVD risk assessment was improved with a hybrid approach using statistically significant variables from Cox proportional hazards regression as input features. When all the data categories in the NHIS-NSC linked to environmental exposure data are available, progressively adding variables in each data category could incrementally increase the predictive performance of DeepSurv model for CVD risk with the hybrid approach. Data linkage to the environmental exposure with residential area code in the NHIS-NSC offered marginally improved performance for CVD risk in both DeepSurv model with the hybrid approach and Cox proportional hazards regression model.배경 및 목적: 심혈관질환 위험평가 및 예측모델링에서 다양한 심혈관질환 위험인자들의 모델 성능향상에 대한 기여도는 논란의 요지로 보고되어왔다. 또한, 지속적으로 증가하는 활용 가능한 심혈관질환 관련 데이터의 종류와 양에도 불구하고 포괄적인 심혈관질환 위험평가와 최적의 예측 모형 개발을 위해 데이터를 어느 범위와 수준까지 수집해야 하는지에 대한 근거는 부족한 현황이다. 본 연구에서는 콕스 모형과 결합된 딥러닝 기반 생존분석 접근법 및 콕스 모형을 활용한 심혈관질환 위험평가와 예측모델링에서 헬스케어-환경 연계 데이터 활용방법 및 범주에 따른 모델 성능향상에 대한 기여도를 평가하고자 하였다. 연구 방법: 전통적 심혈관질환 위험 평가 도구 및 관찰 연구들에 포함 된 심혈관질환 위험요인 관련 변수들을 체계적 문헌고찰 방법론을 활용하여 의학연구 문헌데이터베이스 (PubMed and Embase)에서 포괄적으로 정보를 수집하였다. 미세먼지 누적장기노출 및 도시녹지면적에 대한 환경 노출 데이터와 연계 된 국민건강보험공단 표본코호트, (National Health Insurance Service-National Sample Cohort, NHIS-NSC)에서 각 심혈관질환 위험인자들의 데이터 확보 가능성을 검토하였다. NHIS-NSC를 기준으로 2009년에서 2010년 사이에 국가건강검진을 받은 40세 이상 대상자 중 과거 심혈관질환 병력이 없는 대상자 137,249명의 환자에 대한 정보를 수집하여 2011 년 1 월 1 일부터 2013 년 12 월 31 일까지 신규 발생한 심혈관질환에 대해 시간 경과에 따라 추적 조사하였다. 통계 기반 변수선택 방법은 콕스비례위험모형에서 통계적 유의성, 최소 (최상의) Akaike Information Criteria (AIC)의 하위 집합, elastic net penalty로 정규화 된 콕스비례위험모형에서 선택된 변수 및 위에 언급된 모든 기준을 충족하는 변수 세트로 선정하였다. 위에 명시된 통계적 방법 외 모든 데이터 범주에 속한 변수 및 콕스비례위험모형에서 통계적으로 유의미한 변수 (하이브리드 접근법)를 점진적으로 입력 피쳐로 추가하는 전략으로 딥러닝 기반 생존분석 (Cox proportional hazards deep neural network, DeepSurv) 및 콕스비례위험모형에서 예측 모델들을 훈련 세트 (전체 샘플의 80 %)를 기반으로 개발하였다. DeepSurv 및 콕스비례 위험모형을 활용한 심혈관질환 예측 모델의 성능평가는 생존분석을 활용한 예측 모델링에 가장 적합한 평가지표로 알려진 Unos concordance statistics (C-index)를 사용하여 테스트 세트 (총 샘플의 20 %)에서 수행하였다. 결과: 체계적 문헌고찰, 데이터 취합 및 추출 가능성 검토 후, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관, 가족력, 건강상태, 구강건강, 약물 및 환경 노출 데이터 범주에서 총 31 개의 심혈관질환 위험인자가 지역환경 자료와 연계된 NHIS-NSC에서 확인되었다. 통계 기반 변수선택 방법으로 개발한 심혈관질환 예측 모델 중 콕스비례위험모형에서 통계적으로 유의미한 변수를 DeepSurv에 적용한 하이브리드 접근법이 Uno 's C-index 값 0.7069, 모든 변수를 콕스비례위험모형에 적용한 콕스비례위험모형이 Uno 's C-index 값 0.7052로 나타나 기본 임상 요인 (연령, 성별 및 체질량지수)이 포함된 예측 모델과 비교하여 통계적으로 유의미한 모델 예측력 증가를 보였다 (두 모델 모두 Unos C-index 차이에 대한 p-value : <0.0001). 인구사회학적 특성에서 환경 노출에 이르기까지 각 데이터 범주에서 모두 통계적으로 유의미한 변수들이 심혈관질환 예측 모델링을위한 DeepSurv 및 Cox 비례 위험 회귀에 입력 피쳐로 점진적으로 추가 된 경우, 인구사회학적 요인, 건강검진 및 측정 결과, 생활습관 요인 중 통계적으로 유의미한 변수들로 구성된 DeepSurv 모델이 의약품 사용까지 고려한 Cox 비례 위험 회귀를 기반으로 한 모델 보다 뛰어난 성능을 나타냈다. 미세먼지 및 도시녹지면적에 대한 환경 노출 데이터를 거주지를 기반으로 NHIS-NSC와 연계 후 점진적으로 입력 피쳐로 추가 시 DeepSurv 및 콕스비례위험모형을 활용한 심혈관질환 예측 모델링 성능을 통계적으로 유의미한 수준으로 개선하지 못했다. 결론: 최소 입력 피쳐를 갖춘 생존 분석 기반 심혈관질환 예측 모델에서 최상의 성능을 얻으려면 인구사회학적, 건강검진 및 측정 결과, 및 생활습관에 대한 정보를 NHIS-NSC에서 수집하여 DeepSurv의 입력 피쳐로 활용해야한다. 지역환경 자료와 연계된 NHIS-NSC에서 모든 데이터 범주를 사용할 수 있을 때 점진적으로 각 데이터 범주 중 콕스비례위험모형에서 통계적으로 유의미한 심혈관질환 위험인자를 점진적으로 입력 피쳐로 DeepSurv 모델에 추가하는 하이브리드 접근법에서 심혈관질환 예측 모델링 성능이 점차 향상 될 것으로 기대할 수 있다. 주거 지역 코드를 사용한 NHIS-NSC와 환경 노출 데이터 연계는 DeepSurv 및 콕스비례위험모형 모두에서 심혈관질환 예측 모델링 성능이 향상되었지만 통계적으로 유의미한 증가 수준은 아닌 것으로 나타나 환경 노출 데이터 연계 및 적용 시 검토가 필요할 것으로 추정된다.I. Introduction 1 1. Background 1 2. Research problem 4 3. Hypothesis and objective 6 3.1. Hypothesis 6 3.2. Objective 6 II. Materials and methods 8 1. Comprehensive review and identification of cardiovascular disease (CVD) risk factors 8 1.1. Systematic review on variables included in conventional CVD risk assessment tools 8 1.2. Systematic review on traditional and emerging CVD risk factors from observational studies 9 1.3. Integration of the comprehensive list of CVD risk factors 11 1.4. Screening for data availability 11 2. Cohort analysis for measuring strength of association between risk factors and incident cardiovascular disease 11 2.1 Study population and linkage to environmental exposure data 11 2.2. Variable selection and data processing 15 2.3. Population-based cohort analysis 17 3. Predictive modeling using survival analysis: DeepSurv and Cox proportional hazards regression 17 3.1. Model development 17 3.2. Evaluation of the predictive performance of the models 20 III. Results 21 1. Identification and categorization of cardiovascular disease risk factors 21 2. Magnitude of association between selected risk factors with cardiovascular disease 43 3. Model performance evaluation 56 VI. Discussion 68 1. Key findings and contributions 68 2. Comparison to other studies 69 3. Strengths and limitations 73 4. Implications 74 5. Future perspectives 75 V. Conclusion 77 Reference 78 국문초록 88Docto

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources

딥 뉴럴 네트워크를 활용한 의학 개념 및 환자 표현 학습과 의료 문제에의 응용

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 정교민.본 학위 논문은 전국민 의료 보험데이터인 표본코호트DB를 활용하여 딥 뉴럴 네트워크 기반의 의학 개념 및 환자 표현 학습 방법과 의료 문제 해결 방법을 제안한다. 먼저 순차적인 환자 의료 기록과 개인 프로파일 정보를 기반으로 환자 표현을 학습하고 향후 질병 진단 가능성을 예측하는 재귀신경망 모델을 제안하였다. 우리는 다양한 성격의 환자 정보를 효율적으로 혼합하는 구조를 도입하여 큰 성능 향상을 얻었다. 또한 환자의 의료 기록을 이루는 의료 코드들을 분산 표현으로 나타내 추가 성능 개선을 이루었다. 이를 통해 의료 코드의 분산 표현이 중요한 시간적 정보를 담고 있음을 확인하였고, 이어지는 연구에서는 이러한 시간적 정보가 강화될 수 있도록 그래프 구조를 도입하였다. 우리는 의료 코드의 분산 표현 간의 유사도와 통계적 정보를 가지고 그래프를 구축하였고 그래프 뉴럴 네트워크를 활용, 시간/통계적 정보가 강화된 의료 코드의 표현 벡터를 얻었다. 획득한 의료 코드 벡터를 통해 시판 약물의 잠재적인 부작용 신호를 탐지하는 모델을 제안한 결과, 기존의 부작용 데이터베이스에 존재하지 않는 사례까지도 예측할 수 있음을 보였다. 마지막으로 분량에 비해 주요 정보가 희소하다는 의료 기록의 한계를 극복하기 위해 지식그래프를 활용하여 사전 의학 지식을 보강하였다. 이때 환자의 의료 기록을 구성하는 지식그래프의 부분만을 추출하여 개인화된 지식그래프를 만들고 그래프 뉴럴 네트워크를 통해 그래프의 표현 벡터를 획득하였다. 최종적으로 순차적인 의료 기록을 함축한 환자 표현과 더불어 개인화된 의학 지식을 함축한 표현을 함께 사용하여 향후 질병 및 진단 예측 문제에 활용하였다.This dissertation proposes a deep neural network-based medical concept and patient representation learning methods using medical claims data to solve two healthcare tasks, i.e., clinical outcome prediction and post-marketing adverse drug reaction (ADR) signal detection. First, we propose SAF-RNN, a Recurrent Neural Network (RNN)-based model that learns a deep patient representation based on the clinical sequences and patient characteristics. Our proposed model fuses different types of patient records using feature-based gating and self-attention. We demonstrate that high-level associations between two heterogeneous records are effectively extracted by our model, thus achieving state-of-the-art performances for predicting the risk probability of cardiovascular disease. Secondly, based on the observation that the distributed medical code embeddings represent temporal proximity between the medical codes, we introduce a graph structure to enhance the code embeddings with such temporal information. We construct a graph using the distributed code embeddings and the statistical information from the claims data. We then propose the Graph Neural Network(GNN)-based representation learning for post-marketing ADR detection. Our model shows competitive performances and provides valid ADR candidates. Finally, rather than using patient records alone, we utilize a knowledge graph to augment the patient representation with prior medical knowledge. Using SAF-RNN and GNN, the deep patient representation is learned from the clinical sequences and the personalized medical knowledge. It is then used to predict clinical outcomes, i.e., next diagnosis prediction and CVD risk prediction, resulting in state-of-the-art performances.1 Introduction 1 2 Background 8 2.1 Medical Concept Embedding 8 2.2 Encoding Sequential Information in Clinical Records 11 3 Deep Patient Representation with Heterogeneous Information 14 3.1 Related Work 16 3.2 Problem Statement 19 3.3 Method 20 3.3.1 RNN-based Disease Prediction Model 20 3.3.2 Self-Attentive Fusion (SAF) Encoder 23 3.4 Dataset and Experimental Setup 24 3.4.1 Dataset 24 3.4.2 Experimental Design 26 ii 3.4.3 Implementation Details 27 3.5 Experimental Results 28 3.5.1 Evaluation of CVD Prediction 28 3.5.2 Sensitivity Analysis 28 3.5.3 Ablation Studies 31 3.6 Further Investigation 32 3.6.1 Case Study: Patient-Centered Analysis 32 3.6.2 Data-Driven CVD Risk Factors 32 3.7 Conclusion 33 4 Graph-Enhanced Medical Concept Embedding 40 4.1 Related Work 42 4.2 Problem Statement 43 4.3 Method 44 4.3.1 Code Embedding Learning with Skip-gram Model 44 4.3.2 Drug-disease Graph Construction 45 4.3.3 A GNN-based Method for Learning Graph Structure 47 4.4 Dataset and Experimental Setup 49 4.4.1 Dataset 49 4.4.2 Experimental Design 50 4.4.3 Implementation Details 52 4.5 Experimental Results 53 4.5.1 Evaluation of ADR Detection 53 4.5.2 Newly-Described ADR Candidates 54 4.6 Conclusion 55 5 Knowledge-Augmented Deep Patient Representation 57 5.1 Related Work 60 5.1.1 Incorporating Prior Medical Knowledge for Clinical Outcome Prediction 60 5.1.2 Inductive KGC based on Subgraph Learning 61 5.2 Method 61 5.2.1 Extracting Personalized KG 61 5.2.2 KA-SAF: Knowledge-Augmented Self-Attentive Fusion Encoder 64 5.2.3 KGC as a Pre-training Task 68 5.2.4 Subgraph Infomax: SGI 69 5.3 Dataset and Experimental Setup 72 5.3.1 Clinical Outcome Prediction 72 5.3.2 Next Diagnosis Prediction 72 5.4 Experimental Results 73 5.4.1 Cardiovascular Disease Prediction 73 5.4.2 Next Diagnosis Prediction 73 5.4.3 KGC on SemMed KG 73 5.5 Conclusion 74 6 Conclusion 77 Abstract (In Korean) 90 Acknowlegement 92박

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction

Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

غالبًا ما تكون السكتة الدماغية المتكررة مدمرة وقادرة على التسبب في إعاقة شديدة أو الوفاة. ومع ذلك ، فإن ما يقرب من 90 ٪ من أسباب السكتة الدماغية المتكررة قابلة للتغير ، مما يعني أنه يمكن تجنب السكتات الدماغية المتكررة عن طريق التحكم في عوامل الخطر ، والتي هي في الأساس سلوكية واستقلابية بطبيعتها. وبالتالي ، يتضح من الأعمال السابقة أن نموذج التنبؤ بالسكتة الدماغية المتكررة يمكن أن يساعد في تقليل احتمالية الإصابة بسكتة دماغية متكررة. أظهرت الأعمال السابقة نتائج واعدة في التنبؤ بحالات السكتة الدماغية لأول مرة باستخدام أساليب التعلم الآلي. ومع ذلك ، هناك أعمال محدودة للتنبؤ بالسكتة الدماغية المتكررة باستخدام أساليب التعلم الآلي. ومن ثم ، تم اقتراح هذا العمل لإجراء تحليل تجريبي والتحقيق في خوارزميات التعلم الآلي المطبقة في نماذج التنبؤ بالسكتة الدماغية المتكررة. يهدف هذا البحث إلى التحقيق في أداء خوارزميات التعلم الآلي ومقارنتها باستخدام مجموعات البيانات السريرية العامة للسكتة الدماغية المتكررة. في هذه الدراسة ، تم استخدام الشبكة العصبية الاصطناعية (ANN) وآلة المتجهات الداعمة (SVM) وقائمة قواعد بايزي (BRL) ومقارنة أدائها في مجال نموذج التنبؤ بالسكتة الدماغية المتكررة. تظهر نتيجة التجارب التجريبية أن ANN سجلت أعلى دقة عند 80.00٪ ، تليها BRL بنسبة 75.91٪ و SVM بنسبة 60.45٪.Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders