Prolonged grief disorder affects around 1 in 20 people, and we're starting to understand the neuroscience behind it For most people, the intense sting of grief eases with time. For some, however, persistent and painful grief remains, developing into prolonged grief disorder. A new review of the condition, which affects around 5 per cent of bereaved people, sheds light on how it develops. This could help doctors predict which recently bereaved people will benefit from extra support. The decision to include prolonged grief disorder (PGD) in the American Psychiatric Association's diagnostic manual in 2022 sparked intense debate over whether it was pathologising a normal human response to loss and imposing an arbitrary timeline on what constitutes "normal" grief.

Antidepressant nonadherence is pervasive, driving relapse, hospitalization, suicide risk, and billions in avoidable costs. Clinicians need tools that detect adherence lapses promptly, yet current methods are either invasive (serum assays, neuroimaging) or proxy-based and inaccurate (pill counts, pharmacy refills). We present the first noninvasive biomarker that detects antidepressant intake from a single night of sleep. A transformer-based model analyzes sleep data from a consumer wearable or contactless wireless sensor to infer antidepressant intake, enabling remote, effortless, daily adherence assessment at home. Across six datasets comprising 62,000 nights from >20,000 participants (1,800 antidepressant users), the biomarker achieved AUROC = 0.84, generalized across drug classes, scaled with dose, and remained robust to concomitant psychotropics. Longitudinal monitoring captured real-world initiation, tapering, and lapses. This approach offers objective, scalable adherence surveillance with potential to improve depression care and outcomes.

Background: This study investigates how variations in Major Depressive Disorder (MDD) symptoms, quantified by the Hamilton Rating Scale for Depression (HAM-D), causally influence the prescription of SSRIs versus SNRIs. Methods: We applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on antidepressant choice. Results: Among 17 binary classifiers, Random Forest achieved highest performance (accuracy, F1, precision, recall, ROC-AUC near 0.85). Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection. Conclusions: Counterfactual reasoning elucidates which MDD symptoms most strongly drive SSRI versus SNRI selection, enhancing interpretability of AI-based clinical decision support systems. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.

In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledge for downstream tasks like question answering (QA), even in complex areas involving health topics. Considering their high potential for facilitating clinical work in the future, understanding the quality of encoded medical knowledge and its recall in LLMs is an important step forward. In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews -- studies synthesizing evidence-based answers for specific medical questions. Through experiments on the new MedREQAL dataset, comprising question-answer pairs extracted from rigorous systematic reviews, we assess six LLMs, such as GPT and Mixtral, analyzing their classification and generation performance. Our experimental insights into LLM performance on the novel biomedical QA dataset reveal the still challenging nature of this task.

INTRODUCTION: The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment and improve outcomes, which was deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study. OBJECTIVES: 1) Develop a model capable of predicting probabilities of remission across multiple pharmacological treatments for adults with at least moderate major depression. 2) Validate model predictions and examine them for amplification of harmful biases. METHODS: Data from previous clinical trials of antidepressant medications were standardized into a common framework and included 9,042 adults with moderate to severe major depression. Feature selection retained 25 clinical and demographic variables. Using Bayesian optimization, a deep learning model was trained on the training set, refined using the validation set, and tested once on the held-out test set. RESULTS: In the evaluation on the held-out test set, the model demonstrated achieved an AUC of 0.65. The model outperformed a null model on the test set (p = 0.01). The model demonstrated clinical utility, achieving an absolute improvement in population remission rate in hypothetical and actual improvement testing. While the model did identify one drug (escitalopram) as generally outperforming the other drugs (consistent with the input data), there was otherwise significant variation in drug rankings. On bias testing, the model did not amplify potentially harmful biases. CONCLUSIONS: We demonstrate the first model capable of predicting outcomes for 10 different treatment options for patients with MDD, intended to be used at or near the start of treatment to personalize treatment. The model was put into clinical practice during the AIDME randomized controlled trial whose results are reported separately.

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic health record (EHR) data to locate published prognostic factors that capture the clinical syndrome of DTD. In this work, we use LLM-generated synthetic data (GPT3.5) and a Non-Maximum Suppression (NMS) algorithm to train a BERT-based span extraction model. The resulting model is then able to extract and label spans related to a variety of relevant positive and negative factors in real clinical data (i.e. spans of text that increase or decrease the likelihood of a patient matching the DTD syndrome). We show it is possible to obtain good overall performance (0.70 F1 across polarity) on real clinical data on a set of as many as 20 different factors, and high performance (0.85 F1 with 0.95 precision) on a subset of important DTD factors such as history of abuse, family history of affective disorder, illness severity and suicidality by training the model exclusively on synthetic data. Our results show promise for future healthcare applications especially in applications where traditionally, highly confidential medical data and human-expert annotation would normally be required.

Move over Dr Google, it's Dr ChatGPT's time to shine. The AI chatbot has quickly become an online sensation due to its ability to rapidly research complicated topics, provide clear answers, and converse with its users in a human-like manner. Its rise has also been met with doom-mongering prophecies that it could replace human workers in some sectors and be used by children and university students alike to fake homework and essays. ChatGPT recently caused a stir in the medical community after it was found capable of passing the gold-standard exam required to practice medicine in the US, raising the prospect it could one day replace human doctors. To see if the chatbot is anywhere close to mimicking a real doctor, MailOnline posed five questions that patients commonly ask their GPs to ChatGPT.

Anticholinergic side effects include confusion, blurred vision, dizziness, falls and a decline in brain function. Anticholinergic effects may also increase risks of falls and may be associated with an increase in mortality. They have also been linked to a higher risk of dementia when used long term. Now, researchers have developed a tool to calculate harmful effects of medicines using artificial intelligence. The team created a new online tool, International Anticholinergic Cognitive Burden Tool (IACT), is uses natural language processing which is an artificial intelligence methdolody and chemical structure analysis to identify medications that have anticholinergic effect.

Artificial intelligence might help doctors in determining whether individuals are likely to have adverse effects from widely used antidepressants, antihistamines, and bladder medications. An evaluation of a new tool to determine which medications are more likely to have adverse anticholinergic effects on the body and brain was conducted under the direction of the University of Exeter and the Kent and Medway NHS and Social Care Partnership Trust. Their findings were recently published in the journal Age and Ageing. Many prescription and over-the-counter medications that affect the brain by inhibiting the neurotransmitter acetylcholine may result in adverse anticholinergic effects. Numerous drugs, including certain bladder medications, antidepressants, stomach medicines, and Parkinson's disease have some degree of anticholinergic impact.

Summary: Combining neuroimaging and artificial intelligence, researchers identified novel brain signatures unique to the response of each antidepressant. Research led by UT Southwestern has identified MRI brain imaging biomarkers that bring new levels of precision for prescribing the most effective antidepressants. The outcome predictive models were developed in part using data from a large multi-center National Institute of Mental Health-funded study and published in the journal Biological Psychiatry. The findings provide strong evidence that the current trial-and-error approach used in clinical practice for the selection of the right antidepressant can be replaced with this new precision medicine approach. "This is a significant advance. It can be and should be used immediately," said Madhukar Trivedi, M.D., Professor of Clinical Psychiatry, and Director of the Center for Depression Research and Clinical Care, one of the pillars of the Peter O'Donnell Jr. Brain Institute.