Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

Extracting Patterns in Medical Claims Data for Predicting Opioid Overdose

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created are input to recurrent neural networks with long short-term memory cells. Hyperparameters are found through Bayesian optimization. Validation data features are reduced using weights from the best model and compared against those found using unsupervised learning techniques in other classifiers

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

How I Treat Acute and Persistent Sickle Cell Pain.

Sickle pain is the hallmark of sickle cell disease (SCD). It could be acute, persistent/relapsing, chronic, or neuropathic. Although there is a general consensus that pain is a major manifestation of SCD, there is a controversy as to the types of pain and their interrelationship between acute, chronic, relapsing, persistent, etc. This report first reviews the general approach to the management of acute vaso-occlusive crisis (VOC) pain, including education, counseling, pharmacotherapy, non-pharmacotherapy, and fluid therapy. This is followed by the presentation of five patients that represent typical issues that are commonly encountered in the management of patients with SCD. These issues are: individualized treatment of pain, bilaterality of pain, use of illicit drugs, tolerance to opioids, opioid-induced hyperalgesia, and withdrawal syndrome. The clinical aspects and management of each of these issues are described. Moreover, such complications as tolerance and withdrawal may persist after discharge and may be mistaken as chronic pain rather than resolving, persistent or relapsing pain

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Medical Civil Rights as a Site of Activism: A Reply to Critics

See Craig Konnoth, Medicalization and the New Civil Rights, 72 Stan. L. Rev. 1165 (2020). See also Rabia Belt & Doron Dorfman, Response, Reweighing Medical Civil Rights, 72 Stan. L. Rev. Online 176 (2020), https://www.stanfordlawreview.org/online/reweighing-medical-civil-rights/; Allison K. Hoffman, Response, How Medicalization of Civil Rights Could Disappoint, 72 Stan. L. Rev. Online 165 (2020), https://www.stanfordlawreview.org/online/how-medicalization-of-civil-rights-could-disappoint/