The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

A semantic model to fight social exclusion

This work presents a semantic model meant to help with the identification and prediction of individuals at risk of social exclusion. The model is based on the self-sufficiency matrix, a tool that evaluates a person's self-sufficiency in different areas, and that is used by Barcelona's City Council. Existing data sources can then be mapped to this model, in order to analyze, query, and visualize the data.This work is partially supported by the Semiotic project, funded by Ministerio de Economia, Industria, y Competitividad (TIN2016-78473-C3-2-R).Peer ReviewedPostprint (author's final draft

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care

Refining Humane Endpoints in Mouse Models of Disease by Systematic Review and Machine Learning-Based Endpoint Definition

Ideally, humane endpoints allow for early termination of experiments by minimizing an animal’s discomfort, distress and pain, while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off values published in the literature remain a challenge to the accurate determination and application of humane endpoints. With the aim to synthesize and appraise existing humane endpoint definitions for commonly used physiological parameters, we conducted a systematic review of mouse studies of acute and chronic disease models, which used body weight, temperature and/or sickness scores for endpoint definition. In the second part of the study, we used previously published and unpublished data on weight, temperature and sickness scores from mouse models of sepsis and stroke and applied machine learning algorithms to assess the usefulness of this method for parameter selection and endpoint definition across models. Studies were searched for in two electronic databases (MEDLINE/Pubmed and Embase). Out of 110 retrieved full-text manuscripts, 34 studies were included. We found large intra- and inter-model variance in humane endpoint determination and application due to varying animal models, lack of standardized experimental protocols and heterogeneity of performance metrics (part 1). Machine learning models trained with physiological data and sickness severity score or modified DeSimoni neuroscore identified animals with a high risk of death at an early time point in both mouse models of stroke (male: 93.2% at 72h post-treatment; female: 93.0% at 48h post-treatment) and sepsis (96.2% at 24h post-treatment), thus demonstrating generalizability in endpoint determination across models (part 2)

A Novel Correction for the Adjusted Box-Pierce Test — New Risk Factors for Emergency Department Return Visits within 72 hours for Children with Respiratory Conditions — General Pediatric Model for Understanding and Predicting Prolonged Length of Stay

This thesis represents the results of three research projects that underline the breadth and depth of my interests. Firstly, I devoted some efforts to the well-known Box-Pierce goodness-of-fit tests for time series models which has been an important research topic over the last few decades. All previously proposed tests are focused on changes of the test statistics. Instead, I adopted a different approach that takes the best performing test and modifying the rejection region. Thus, I developed a semiparametric correction of the Adjusted Box-Pierce test that attains the best I error rates for all sample sizes and lags and outperforms all previous global time series goodness-of-fit approaches. Secondly, I aimed to study and identify novel risk factors significantly associated with 72-hour return visits to emergency departments. I queried data consisting of 185,000 ED visits of patients less than 18 years in the United States using the Cerner® Health Facts Database. A nested mixed-effects logistic regression model to provide statistical inference on associated risk factors was built, and a representative set of machine learning algorithms for our predictive modeling task was selected. New respiratory conditions including acute bronchiolitis, pneumonia, and asthma were identified as risk factors for return visits to ED. Thirdly, I ambitioned to design and implement a comprehensive study to identify new clinical and demographic factors associated with prolonged length of stay ($\u3e$ two weeks) among pediatric patients (aged 18 years and under) in a number of free-standing pediatric and mixed medical facilities. I implemented a mixed effect model to assess the statistical significance and effect sizes of age, race/ethnicity, number of medications, medical family history, presence of infection agents (fungi, bacteria, virus), cancer diagnoses, and other conditions as well as some clinical variables. A stochastic gradient model was also implemented for prediction. From the mixed-effects model, 11 main effect predictors were found to be significantly and statistically associated with an increase in the odds of prolonged length of stay. The area under the operator characteristic curve (AUROC) for the mixed-effects model was 0.887 (0.885, 0.889) and the extreme gradient boosting model attained an AUROC of 0.931 (0.930, 0.933)

Machine Learning techniques applied to the consumption of illegal psychoactive substances: A systematic mapping.

The consumption of illicit psychoactive substances is an issue that is experienced daily, involving people of different ages. It is worth noting that many of these substances can cause disorders, such as:Marijuana or cannabis: Its consumption directly affects brain function, particularly the parts of the brain responsible for memory, learning, attention, and decision-making. Bazuco: It is a toxic substance with significant risks for neurological and bodily deterioration. It dissolves rapidly in the bloodstream, making it highly addictive. Cocaine: Its consumption directly affects the nervous system and the rest of the body immediately. Its effects include vasoconstriction, dilated pupils, hyperthermia, rapid heartbeat, and hypertension. Heroin: It is a highly addictive substance that initially produces pleasurable effects, leading to continued and repetitive use. It also causes dry mouth, flushed skin, heaviness in the limbs, nausea, vomiting, intense itching, and mental impairment. Furthermore, in Colombia, this issue is particularly prominent among young people, depending on the context they find themselves in. Nowadays, there is easy access to these types of substances. As a result, several works have been proposed to address this issue using artificial intelligence. In this regard, the present study reviews 50 publications related to the use of machine learning (ML) methods and techniques applied to the consumption of illicit psychoactive substances. Common themes were found among the included publications, and a summary of the selected articles is provided for each theme. The adopted methods are briefly described, along with a comparison between them, noting the methods used, their results, and other important factors of the application or model in different areas. The study concludes with a series of proposals regarding future research directions in this field.The consumption of illicit psychoactive substances is a problem experienced every day, by people of different ages who have been involved in it, highlighting that many of these substances generate disorders such as, for example: Marijuana or cannabis: its consumption affects brain function directly, and particularly the parts of the brain responsible for memory, learning, attention, decision making. Bazuco: it is a toxic substance, which main risks of consumption are reflected in the neurological deterioration and in the organism, and its dissolution in the bloodstream is very fast, an aspect that makes it very addictive. Cocaine: its consumption, directly affects the nervous system and the rest of the organism immediately, these affectations include vasoconstriction, mydriasis, hyperthermia, tachycardia and hypertension. Heroin: is a highly addictive substance, initially, its effects are very pleasant, which leads to a continuous and repetitive consumption behavior, in addition, it produces sensations of dry mouth, reddening and heating of the skin, heaviness in arms and legs, nausea and vomiting, intense itching and clouding of the mental faculties. On the other hand, in Latin American regions and all over the world, this problem is something that stands out a lot and has a great impact on young people according to the context they are in, since nowadays it is very easy to obtain this type of substances, therefore, a series of works have been proposed that address this problem from artificial intelligence, in this way, the current study is a review of 50 publications related to the use of ML methods and techniques applied to the consumption of illicit psychoactive substances. From the publications included, common themes were found, so a summary is made of the articles selected for each theme and the methods adopted are briefly described, as well as a comparison between them, noting the methods used, their results and other important factors of the application or model in different areas, and concluding with a series of proposals on the lines that could guide future research in this field