Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression

Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.

Incremental Learning of Human Activities in Smart Homes

Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.

Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques

Suicide is a major public-health problem that exists in virtually every part of the world. Hundreds of thousands of people commit suicide every year. The early detection of suicidal ideation is critical for suicide prevention. However, there are challenges associated with conventional suicide-risk screening methods. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 2018 and 2022. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection.

Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes

Activity recognition in smart homes aims to infer the particular activities of the inhabitant, the aim being to monitor their activities and identify any abnormalities, especially for those living alone. In order for a smart home to support its inhabitant, the recognition system needs to learn from observations acquired through sensors. One question that often arises is which sensors are useful and how many sensors are required to accurately recognise the inhabitant’s activities? Many wrapper methods have been proposed and remain one of the popular evaluators for sensor selection due to its superior accuracy performance. However, they are prohibitively slow during the evaluation process and may run into the risk of overfitting due to the extent of the search. Motivated by this characteristic, this paper attempts to reduce the cost of the evaluation process and overfitting through tree alignment. The performance of our method is evaluated on two public datasets obtained in two distinct smart home environments.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies

PURPOSE

To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies.

METHODS

An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enroll by batch. A prior-population model was used to determine the optimal sampling schedule for the first batch and to stabilise the data analysis in the interim iteration. Simulation studies were performed under two scenarios (1) the prior- and target-populations have similar pharmacokinetic profiles and (2) the pharmacokinetic profiles diverge significantly. A design criterion to determine early full enrollment was also proposed.

RESULTS

The target-population estimates obtained using the proposed method were compared to estimates obtained if the target-population was studied with a design optimized based on the prior-population model. The proposed method is shown to be not inferior in scenario (1) and superior in scenario (2). The criterion to determine early full enrollment was proven to be effective.

CONCLUSIONS

An adaptive optimal design method together with an early full enrollment criterion were evaluated and resulted in more accurate estimates for the target-population in bridging studies.

Coronary stents in the management of coronary artery disease review of first 100 stents

This is a review of the first 100 coronary stents implanted for the indications of PTCA restenosis, acute or threatened closure and De Novo lesions. The success rates were high and complications rates were low. Subacute stent thrombosis rates were low and stenting for De Novo lesions in > 3.0 mm arteries provide the best short and long term results. Six months restenosis rate was low. Stent assisted high pressure balloon angioplasty is an important advance.