Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP.

Chronic kidney disease (CKD) is a multifactorial, complex condition that requires proper management to slow its progression. In Thailand, 11.6 million people (17.

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tomography (CT) scanning is the investigation of choice for TBI and has become increasingly utilized under the shortage of trained radiology personnel. It is anticipated that deep learning models will be a promising solution for the generation of timely and accurate radiology reports.

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of intracranial hemorrhage that may necessitate surgical intervention include subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage.

AUTOMATIC ICD-10 CODING USING PRESCRIBED DRUGS DATA.

This article discusses the emerging trends and challenges related to automatic clinical coding. We introduce an automatic coding system, which assigns short ICD-10 codes (restricted to the first three symbols, which define the category of the disease) based only on drugs prescribed to patients. We show that even with limited input data, the accuracy levels are comparable to those achieved by entry-level clinical coders as depicted by Seyed Nouraei et al.

High alert drugs screening using gradient boosting classifier.

Prescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem.

Auto-mapping Clinical Documents to ICD-10 using SNOMED-CT.

Excessive paperwork is a considerable issue that leads to additional burdens for health-care professionals. In Thai health-care systems, physicians manually review medical records to select an appropriate principle diagnosis and other co-morbidities and convert them into ICD-10s to claim financial support from the government. Accordingly, 160,000 ICD-10 codes and 46,000 in-patient discharge summaries are documented by physicians at Maharaj Nakorn Chiang Mai hospital each year.

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients.

DrKnow: A Diagnostic Learning Tool with Feedback from Automated Clinical Decision Support.

Providing medical trainees with effective feedback is critical to the successful development of their diagnostic reasoning skills. We present the design of DrKnow, a web-based learning application that utilises a clinical decision support system (CDSS) and virtual cases to support the development of problem-solving and decision-making skills in medical students. Based on the clinical information they request and prioritise, DrKnow provides personalised feedback to help students develop differential and provisional diagnoses at key decision points as they work through the virtual cases.

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e.

Effective ECG reference ranges for Northern Thai people.

Background: The numerical values and ranges of the ECG are used as criteria for classifying types of arrhythmia. However, one criterion cannot be generically applied for all patient groups. Several studies have shown that age, gender, and race are the major key factors which produce variations in ECG values.

Implementation of integrated heterogeneous electronic electrocardiography data into Maharaj Nakorn Chiang Mai Hospital Information System.

Electrocardiography is one of the most important non-invasive diagnostic tools for diagnosing coronary heart disease. The electrocardiography information system in Maharaj Nakorn Chiang Mai Hospital required a massive manual labor effort. In this article, we propose an approach toward the integration of heterogeneous electrocardiography data and the implementation of an integrated electrocardiography information system into the existing Hospital Information System.