Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease

Exploring machine learning algorithms in sickle cell disease patient data: A systematic review

This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A comprehensive analysis of recent studies reveals promising results in using ML techniques for diagnosing and monitoring SCD. The review covers various ML algorithms, including Multilayer Perceptron, Support Vector Machine, Random Forest, Logistic Regression, Long short-term memory, Extreme Learning Machines, Convolutional Neural Networks, and Transfer Learning methods. Despite significant advances, challenges such as limited dataset sizes, interpretability concerns, and risks of overfitting are identified in studies. Future research directions entail addressing these limitations by harnessing larger and more representative datasets, enhancing model interpretability, and exploring advanced ML techniques like deep learning. Overall, this review underscores the transformative potential of ML in increasing the diagnosis, monitoring and define prognosis of sickle cell disease while also highlighting the need for further investigation in the field.

Complementary Metal-Oxide-Semiconductor-Based Magnetic and Optical Sensors for Life Science Applications

Optical and magnetic sensing methods are integral to both research and clinical applications in biological laboratories. The ongoing miniaturization of these sensors has paved the way for the development of point-of-care (PoC) diagnostics and handheld sensing devices, which are crucial for timely and efficient healthcare delivery. Among the various competing sensing and circuit technologies, CMOS (complementary metal-oxide-semiconductor) stands out due to its distinct cost-effectiveness, scalability, and high precision. By leveraging the inherent advantages of CMOS technology, recent developments in optical and magnetic biosensors have significantly advanced their application in life sciences, offering improved sensitivity, integration capabilities, and reduced power consumption. This paper provides a comprehensive review of recent advancements, focusing on innovations in CMOS-based optical and magnetic sensors and their transformative impact on biomedical research and diagnostics.

Artificial intelligence in sickle disease

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions

There remains a limited emphasis on the use beyond the research domain of artificial intelligence (AI) in haematology and it does not feature significantly in postgraduate medical education and training. This perspective article considers recent developments in the field of AI research in haematology and anticipates the potential benefits and risks associated with its deeper integration into the specialty. Anxiety towards the greater use of AI in healthcare stems from legitimate concerns surrounding data protection, lack of transparency in clinical decision-making, and erosion of the doctor-patient relationship. The specialty of haematology has successfully embraced multiple disruptive innovations. We are at the cusp of a new era of closer integration of AI into routine haematology practice that will ultimately benefit patient care but to harness its benefits the next generation of haematologists will need access to bespoke learning opportunities with input from data scientists.

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

BACKGROUND

Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient's pain intensity level.

OBJECTIVE

This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records.

METHODS

This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient's self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations.

RESULTS

We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings.

CONCLUSIONS

Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient's condition, in addition to the patient's self-reported pain scores.