Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model

Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder. Early identification of mood episodes enabling timely mood stabilisation is an important clinical goal. Recent technological advances allow the prospective reporting of mood in real time enabling more accurate, efficient data capture. The complex nature of these data streams in combination with challenge of deriving meaning from missing data mean pose a significant analytic challenge. The signature method is derived from stochastic analysis and has the ability to capture important properties of complex ordered time series data. To explore whether the onset of episodes of mania and depression can be identified using self-reported mood data.Comment: 12 pages, 3 tables, 10 figure

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

The Role of Perceived Uncertainty, Ego Identity, and Perceived Behavioral Control in Predicting Patient's Attitude Toward Medical Surgery

Medical surgery has sometimes become the only best choice for a patient's well-being. Unfortunately, not all patients have the willingness to live it. Often, therapeutic failure is caused by uncooperative attitudes of the patients which originate from their negative attitudes toward the surgery. This research is aimed at finding a theoretical model to explain psychological factors forming the patient's attitudes. This predictive correlational research was conducted on 99 patients suffering heart disease and cancer continuum who require medical surgery in DKI Jakarta, Indonesia. Research results showed that a commitment aspect of ego identity is able to indirectly predict attitude toward medical surgery through mediation of perceived uncertainty. Perceived behavioral control directly predicts the attitude in a negative direction. This research concludes that patients' commitment towards their identity plays a significant role as they deal with medical surgery