Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy.

Background: While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these.

Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT.

Objective: Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment.

Methods: We use 1) commonly used time-independent methods (i.

Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH).

Purpose: Depression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions.

Stockholm score of lesion detection on computed tomography following mild traumatic brain injury (SELECT-TBI): study protocol for a multicentre, retrospective, observational cohort study.

Introduction: Mild traumatic brain injury (mTBI) is one of the most common reasons for emergency department (ED) visits. A portion of patients with mTBI will develop an intracranial lesion that might require medical or surgical intervention. In these patients, swift diagnosis and management is paramount.

Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach.

Background: The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process.

Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data.

This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016).

Predictive models for clinical decision making: Deep dives in practical machine learning.

The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted.

Comparing supervised and unsupervised approaches to multimodal emotion recognition.

We investigated emotion classification from brief video recordings from the GEMEP database wherein actors portrayed 18 emotions. Vocal features consisted of acoustic parameters related to frequency, intensity, spectral distribution, and durations. Facial features consisted of facial action units.

Prediction of individuals at high risk of chronic kidney disease during treatment with lithium for bipolar disorder.

Background: Lithium is the most effective treatment in bipolar disorder. Its use is limited by concerns about risk of chronic kidney disease (CKD). We aimed to develop a model to predict risk of CKD following lithium treatment initiation, by identifying individuals with a high-risk trajectory of kidney function.

Toward Learning Machines at a Mother and Baby Unit.

Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over time and over task.

Predicting treatment failure in regular care Internet-Delivered Cognitive Behavior Therapy for depression and anxiety using only weekly symptom measures.

Objective: Therapist guided Internet-Delivered Cognitive Behavior Therapy (ICBT) is effective, but as in traditional CBT, not all patients improve, and clinicians generally fail to identify them early enough. We predict treatment failure in 12-week regular care ICBT for Depression, Panic disorder and Social anxiety disorder, using only patients' weekly symptom ratings to identify when the accuracy of predictions exceed 2 benchmarks: (a) chance, and (b) empirically derived clinician preferences for actionable predictions.

Method: Screening, pretreatment and weekly symptom ratings from 4310 regular care ICBT-patients from the Internet Psychiatry Clinic in Stockholm, Sweden was analyzed in a series of regression models each adding 1 more week of data.

Supporting global health goals with information and communications technology.

The objective of this study is to critically assess the possible roles of information and communications technology (ICT) in supporting global health goals. This is done by considering privilege and connectibility. In short, ICT can contribute by providing health information via four different kinds of access, each with its own history and prospective future.