Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population.

Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

Association between laboratory markers and Covid-19 disease severity and outcome: a retrospective cohort study in Saudi Arabia.

Introduction: In Saudi Arabia, limited studies have evaluated factors including epidemiologic, clinical, and laboratory findings that are associated with COVID-19 disease. The aim of this paper was to identify laboratory parameters used in King Abdulaziz University Hospital which show an association with disease severity and patient outcome in the form of mortality. Methods: Age, gender, medical history, and laboratory parameters were all retrospectively assessed concerning disease severity and disease outcome in a total of 111 COVID-19 patients at King Abdulaziz University Hospital between July 2020 and August 2020. Patients were categorized into mild disease if they did not require ward admission, moderate if they met the Ministry of Health criteria for isolation ward admition, and severe if they were admitted to the ICU. Results: Age but not gender was associated with the disease severity X2 (4, N = 110) = 27.2, p <0.001. Of all laboratory parameters on admission, only the levels of Albumin appeared to be significantly associated X2 (2, N =70) = 6.6, p <0.05 with disease severity. Age but not gender was also significantly associated with disease outcome X2 (2, N = 110) = 12.8, p < 0.01. Interestingly, RBC count also showed a significant relation with disease outcome X2 (2, N = 71) = 6.1, p <0.05. Discussion: This study provides more understanding of the laboratory characteristics in our part of the world to efficiently manage the disease.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis.

BACKGROUND: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. METHODS: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. RESULTS: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. CONCLUSION: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

Knowledge-Based Dietary Intake Recommendations of Nutrients for Pediatric Patients with Maple Syrup Urine Disease.

Maple syrup urine disease (MSUD) is a metabolic disorder characterized by a difficulty to digest and process proteins necessary for growth. To monitor and maintain the ideal growth of children with MSUD, caregivers need to carefully control the consumption of harmful branched-chain amino acids (BCAAs). The dietary limits of amino acids for MSUD patients are recommended and controlled by pediatricians and metabolic dietitians according to age, height, weight, and the prevailing percentage of amino acids in the body. This study introduces an intelligent dietary tool called MSUD Baby Buddy for caregivers of MSUD patients that tracks the amino acids intake out of baby formulas for babies 0-6 months old. This tool aims to provide accurate recommendations of the appropriate daily intake of protein and BCAAs based on the patients' data, plasma BCAAs, and formula preferences. We use a knowledge-based system, including knowledge acquisition and verification, as well as knowledge management tool validation, and the ripple-down rules are employed for building the system. MSUD Baby Buddy can support the maintenance of adequate amino acid levels and increase awareness about the control of BCAAs. The average usability of MSUD Baby Buddy is 84.25, indicating that the tool is intuitive and may help caregivers to easily determine the recommended doses of formula based on patients' biometric data and preferred formula. On the other hand, interviews with metabolic dietitians revealed some drawbacks, which were addressed to further improve the tool. MSUD Baby Buddy is expected to help caregivers of MSUD patients to independently track nutrient intake and reduce the number of visits to the pediatrician and metabolic dietitian.

A Smart Monitoring System for Self-Nutrition Management in Pediatric Patients with Inherited Metabolic Disorders: Maple Syrup Urine Disease (MSUD).

A metabolic disorder is due to a gene mutation that causes an enzyme deficiency which leads to metabolism problems. Maple Syrup Urine Disease (MSUD) is one of the most common and severe hereditary metabolic disorders in Saudi Arabia. Patients and families were burdened by complex and regular dietary therapy menus because of the lack of information on food labels, it was also difficult to keep track of MSUD's typical diet. The prototype smart plate system proposed in this work may help patients with MSUD and their caregivers better manage the patients' MSUD diet. The use of knowledge-based, food identification techniques and a device could provide a support tool for self-nutrition management in pediatric patients. The requirements of the system are specified by using questionaries. The design of the prototype is divided into two parts: software (mobile application) and hardware (3D model of the plate). The knowledge-based mobile application contains knowledge, databases, inference, food recognition, food plan, monitor food plan, and user interfaces. The hardware prototype is represented in a 3D model. All the patients agreed that a smart plate system connected to a mobile application could help to track and record their daily diet. A self-management application can help MSUD patients manage their diet in a way that is more pleasant, effortless, accurate, and intelligent than was previously possible with paper records. This could support dietetic professional practitioners and their patients to achieve sustainable results.

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

A Smart Chatbot for Interactive Management in Beta Thalassemia Patients.

Background: ß-thalassemia is an inherited blood disorder that affects the production of hemoglobin molecules owing to the reduction or absence of beta chains. Transfusion therapy has had a key role in extending the lifespan of ß-thalassemia patients. This life-saving therapy is linked to numerous assessments and complications that now comprise most thalassemia management considerations. Consequently, many patients do not receive adequate information about the required assessments, as indicated by evidence-based medical guidelines. Patients with ß-thalassemia may benefit from chatbots that follow up on their condition and that provide the required assessment information. Self-management will hopefully have a positive impact on health outcomes. Objectives: This study aims to develop a chatbot that can assist in the management of ß-thalassemia by providing the assessment information required to monitor patients' statuses. Methods: The chatbot operated as a messaging system. A question/answer system was created based on knowledge pertaining to ß-thalassemia assembled from experts, medical guidelines, and articles. Recommendations regarding the patient's follow-up assessment are made based on the answers. Results: A prototype was implemented to demonstrate how the chatbots could dynamically and flexibly provide the assessment information required to follow up on and monitor patients. A small sample of adults with ß-thalassemia used the chatbot to examine the system's usability and perceived utility. A system usability scale and utility scale were implemented to complete a post-test survey. The chatbots were considered by 34 patients, of whom the majority (72%) found them easy to use, while more than 90% of patients considered their use beneficial. Most of the participants agreed that the chatbots could improve their knowledge about their ß-thalassemia assessments. Conclusion: Our findings suggest that chatbots can be beneficial to the development of recommended tests and management related to the assessment of ß-thalassemia.

An Intelligent System for Proper Management and Disposal of Unused and Expired Medications.

For years, several countries have been concerned about how to dispose of unused pharmaceuticals that can endanger human health and the environment. Moreover, some people are in desperate need of medical attention and medications, but they lack the financial resources to obtain them. In Saudi Arabia, there are no take-back medicine programs, and there is no published research on how medications properly are disposed. The aim of this research is to use the power of artificial intelligence to assist in the proper management and disposal of expired and unused medications and to develop a prototype device for collecting medication by automatically classifying medications for proper disposal and donation. In this research, artificial intelligence technologies such as web-based expert systems, image recognition and classification algorithms, chatbots, and the internet of things are used to assist in a take-back medications program. In conclusion, the prototype design of a web-based expert system and the device reduced improper disposal risks by providing significant advice on the safe disposal of unwanted pharmaceuticals. By using an organized method of collecting expired medications, the benefits were made possible.

Increasing Awareness of Proper Disposal of Unused and Expired Medication Using a Knowledge-Based Disposal Management System.

Medicines are used daily in Saudi Arabian homes. However, when these medicines are no longer needed, most people dispose of them incorrectly, forgetting the harmful impact of improper disposal. Inadequate awareness and knowledge are major reasons for improper disposal. In this study, we create a broad inclusive knowledge base that includes many types of medications available in Saudi homes and provides guidance on how to dispose of them as a means of raising awareness on correct disposal methods and preventing harmful impacts on both the environment and society. The study primarily aims to understand societal behaviour regarding the disposal of unused and expired medications and develop a prototype of a knowledge-based system that helps raise awareness of correct disposal methods for unused and expired medications. The data in the knowledge base are presented in tables that are easy to understand and comprehend, and the recommendations are also easy to apply and practice in everyday life. The results from the survey show that 66.8% of the 310 participants had unneeded medications in their homes, and only 14.9% knew how to dispose of unusable medications, while only 6.5% knew how to dispose of expired medications. Overall, the research studied Saudi society's behaviour regarding unused and expired medications, and we created a prototype of a knowledge-based system designed to increase awareness of proper disposal and management of unused and expired medications.

Web-based expert system with quick response code for beta-thalassemia management.

ß-thalassemia is an inherited blood disorder in which the body cannot produce hemoglobin normally. Since patients with this condition receive blood transfusions regularly, iron builds up primarily in organs such as the heart, liver and endocrine glands. Accumulation of iron in the organs necessitates chelation therapy. These patients must visit the hospital frequently to assess and follow up on their health condition. Physician intervention is required after each regular assessment to adjust the treatment. Lifelong healthcare support using a web-based expert system with a quick response code is designed for ß-thalassemia management in order to deliver benefits to patients, physicians, and other healthcare providers. The aim of this study is to implement a web-based expert system for ß-thalassemia management in order to provide treatment recommendations and support the lifelong healthcare of patients. The system provides patient-related details, such as medical history, medicines, and appointments, in real-time. It has been also tested in real-life cases and shown to enhance ß-thalassemia management.

Prototype Development of an Expert System of Computerized Clinical Guidelines for COVID-19 Diagnosis and Management in Saudi Arabia.

The increasing number of COVID-19 patients has increased health care professionals' workloads, making the management of dynamic patient information in a timely and comprehensive manner difficult and sometimes impossible. Compounding this problem is a lack of health care professionals and trained medical staff to handle the increased number of patients. Although Saudi Arabia has recently improved the quality of its health services, there is still no suitable intelligent system that can help health practitioners follow the clinical guidelines and automated risk assessment and treatment plan remotely, which would allow for the effective follow-up of patients of COVID-19. The proposed system includes five sub-systems: an information management system, a knowledge-based expert system, adaptive learning, a notification and follow-up system, and a mobile tracker system. This study shows that, to control epidemics, there is a method to overcome the shortage of specialists in the management of infections in Saudi Arabia, both today and in the future. The availability of computerized clinical guidance and an up-to-date knowledge base play a role in Saudi health organizations, which may not have to constantly train their physician staff and may no longer have to rely on international experts, since the expert system can offer clinicians all the information necessary to treat their patients.

Corrigendum to "Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System".

[This corrects the article DOI: 10.1155/2017/3587309.].

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System.

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Consistency Test between Scoring Systems for Predicting Outcomes of Chronic Myeloid Leukemia in a Saudi Population Treated with Imatinib.

Inconsistency in prognostic scores occurs where two different risk categories are applied to the same chronic myeloid leukemia (CML) patient. This study evaluated common scoring systems for identifying risk groups based on patients' molecular responses to select the best prognostic score when conflict prognoses are obtained from patient profiles. We analyzed 104 patients diagnosed with CML and treated at King Abdulaziz Medical City, Saudi Arabia, who were monitored for major molecular response (achieving a BCR-ABL1 transcript level equal to or less than 0.1%) by Real-Time Quantitative Polymerase Chain Reaction (RQ-PCR), and their risk profiles were identified using Sokal, Hasford, EUTOS, and ELTS scores based on the patients' clinical and hematological parameters at diagnosis. Our results found that the Hasford score outperformed other scores in identifying risk categories for conflict groups, with an accuracy of 63%.

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia.

BACKGROUND: Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. PRINCIPLE FINDINGS: The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.