Running head and connection to substance abuse1comorbidity an.docx
- 1. Running head: and connection to substance abuse 1 comorbidity and connection to substance abuse 6Literature Review Comorbidity and Its Connection to Substance Abuse, Treatment, and Relapse Paula King Walden University Capstone Dr. Jane Lyons June 23, 2019 Comorbidity and Its Connection to Substance Abuse, Treatment, and Relapse Comorbidity is considered as two or more conditions that occur in one person. These disorders can happen one after another or at the same time. Comorbidity has a strong connection with substance abuse, treatment, as well as relapse (Kelly & Daley, 2013). It is essential to note that many of those who suffer from substance use disorder usually develop other mental disorders, which is a similar case to many of those diagnosed with mental disorders. Research indicates that about half of those experiencing mental illnesses have a high probability of
- 2. experiencing substance abuse disorders at some time in their life. Some few research have been conducted on children, and the result indicates that youths with substance abuse disorder typically have a high rate of co-occurring mental diseases like anxiety and depression (Child, 2012). Clinicians must find an effective way to treat individuals with substance use and addictions mental health disorders. To be effective they need to prescribe the right medication to treat alcohol, opioid, and nicotine addiction and there are also medications to alleviate symptoms of mental disorders. There are some behavioral therapies that have promise in treating comorbid conditions. The programs are tailored for the clients according to age, drug misused, and other factors, which can be used alone or with medication. Some effective therapies for treating comorbid conditions: cognitive behavioral therapy, Dialectical therapy, assertive community treatment, therapeutic communities, and contingency management (Kelly & Daley, 2013). According to Woody and Blaine (1979) for over 25 years there has been a correlation between substance abuse illnesses and other mental disorders is not a visible indication that one resulted in another, albeit one came after another. Understanding the directionality or causality can sometimes be difficult because of different reasons. For instance, emotional or behavior issues may not be severe enough to raise the alarm for diagnosis. However, sub-clinical mental health concerns may prompt abuse of drugs. The main factors that contribute to comorbidity between mental illnesses and drug abuse disorders include the aspect of conventional risk factors, the possibility of mental diseases contributing to addiction and drug abuse, and the possibility of drug abuse and addiction contributing to the growth of mental health disorders (Bukstein & Horner, 2015). Drug use and mental health illnesses can result from coinciding aspects such as epigenetic and genetic exposures, concerns with related parts of the brain, and environmental factors like the early experience of trauma or stress. Most of the vulnerabilities come from composite connections with several genes and
- 3. genetic relations with environmental factors. For instance, regular use of marijuana during the youthful stage may cause various complications during adulthood, especially among those carrying a given gene variant. Environmental aspects such as trauma, stress, and unfavorable childhood experience increase the risk of both mental illnesses and disorders associated with drug use (Ouimette & Brown, 2016). Treatment patients with substance use and psychiatric comorbidity is a challenge because they may resist psychiatric or additional treatment. They could relapse or stop treatment entirely, and placing those with substance abuse and mental disorders rehabilitation facilities (Bukstein & Horner, 2015). Research has established that those with mild, severe, or sub-clinical mental disorders may tend to use drugs for self- medication. Even though drugs can reduce the symptoms of mental illnesses temporarily, they can as well worsen the symptoms, both in the long run and acutely. For instance, proof indicates that the use of cocaine can aggravate bipolar disorder symptoms and lead to the advancement of the disorder (Ross & Peselow, 2014). When one develops a mental disorder, the resultant changes in the brain may intensify the susceptibility for challenging substance abuse use by increasing their positive impacts, lowering awareness of the harmful effects, or lessening the unfavorable signs of the mental illness and the side effect of the medications utilized in its treatment. For instance, neuroimaging connects ADHD with the neurobiological alterations in the circuit of the brain which are linked with drug yearnings, and this can partially explain why those with substance abuse disorders experience higher cravings when they have comorbid ADHD (Ross & Peselow, 2014). It is also essential to note that substance abuse and addiction has the capability of causing the development of mental illnesses. The use of drugs can cause alterations in certain parts of the brain, which are affected by other mental illnesses like anxiety, impulse control, or mood illnesses (Ross & Peselow, 2014). Treatment for comorbidity normally entails collaboration
- 4. between clinicians and organizations that offer supportive care. Treatment of the problem should focus on substance abuse and mental illness disorder together instead of focusing on one. There are effective medications and behavioral therapies to deal with addiction and mental illnesses. Relapse usually happens 70% of the time for substance use, identifying the characteristics associated with the patient relapsing and adjust the treatment program (Andersson, Wenaas, & Nordfjaern, 2018). References Andersson, Helle Wessel, Wenaas, Merethe, & Nordfjaern, Trond (2018). Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances. https://reader.elsevier.com/reader/pii/SO30640318308542?token =4D108E6999079029E Bukstein OG & Horner MS. (2015) Management of the adolescent with substance use disorders and comorbid psychopathology. Child Adolescent Psychiatric Clinics North America 19(3):609-623. doi:10.1016/j.chc.2010.03.011. Child, J. (2012). Adolescent Substance Abuse. 2012 Jul 1; 21(4): 310–322. doi:10.1080/1067828X.2012.709453/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923628/ Kelly, TM, Daley, DC. Integrated Treatment of Substance Use and Psychiatric Disorders. Soc Work Public Health. 2013; 28(0): 388-406. doi: 10.1080/19371918.2013.774673 Ouimette, P. E., & Brown, P. J. (2016). Trauma and substance
- 5. abuse: Causes, consequences, and treatment of comorbid disorders. American Psychological Association. Ross S. & Peselow E. (2014) Co-occurring psychotic and addictive disorders: neurobiology and diagnosis. Clin Neuropharmacol. 35(5):235-243. doi:10.1097/WNF.0b013e318261e193 Woody GE, Blaine J. Depression in narcotic addicts: Quite possibly more than a chance association. In: Dupont RL, Goldstein A, O’Donnell J, Brown B, editors. Handbook on Drug Abuse. Washington, DC: U.S. Government Printing Office; 1979. pp. 277–285 DataMining Homework/bankdata_csv_all.csvidagesexregionincomemarriedc hildrencarsave_actcurrent_actmortgagepepID1210148FEMALEI NNER_CITY17546NO1NONONONOYESID1210240MALETO WN30085.1YES3YESNOYESYESNOID1210351FEMALEINNE R_CITY16575.4YES0YESYESYESNONOID1210423FEMALET OWN20375.4YES3NONOYESNONOID1210557FEMALERURA L50576.3YES0NOYESNONONOID1210657FEMALETOWN378 69.6YES2NOYESYESNOYESID1210722MALERURAL8877.07 NO0NONOYESNOYESID1210858MALETOWN24946.6YES0Y ESYESYESNONOID1210937FEMALESUBURBAN25304.3YES 2YESNONONONOID1211054MALETOWN24212.1YES2YESY ESYESNONOID1211166FEMALETOWN59803.9YES0NOYES YESNONOID1211252FEMALEINNER_CITY26658.8NO0YES YESYESYESNOID1211344FEMALETOWN15735.8YES1NOYE SYESYESYESID1211466FEMALETOWN55204.7YES1YESYE
- 22. SYESNOID1269730FEMALEINNER_CITY9672.25YES0YESY ESYESNONOID1269831FEMALETOWN15976.3YES0YESYES NONOYESID1269929MALEINNER_CITY14711.8YES0NOYE SNOYESNOID1270038MALETOWN26671.6NO0YESNOYESY ESYES DataMining Homework/bankdataDescription.doc Bank Data Description (For bankdata file obtained from DePaul University data mining course materials.) The marketing department of a financial firm keeps records on customers, including demographic information and, number of type of accounts. When launching a new product, such as a "Personal Equity Plan" (PEP), a direct mail piece, advertising the product, is sent to existing customers, and a record kept as to whether that customer responded and bought the product. Based on this store of prior experience, the managers decide to use data mining techniques to build customer profile models. The data contains of a number of the following fields id a unique identification number age age of customer in years sex MALE / FEMALE region inner_city/rural/suburban/town income income of customer married Is the customer married (YES/NO) children
- 23. number of children car Does the customer own a car (YES/NO) save_acct Does the customer have a saving account (YES/NO) current_acct Does the customer have a current account (YES/NO) mortgage Does the customer have a mortgage (YES/NO) pep Did the customer buy a PEP after the last mailing (YES/NO) Each record is a customer description where the "pep" field indicates whether or not that customer bought a PEP after the last mailing. DataMining Homework/fedPapers85.csvauthorfilenameaallalsoanandanyarea satbebeenbutbycandodowneveneveryforfromhadhashaveherhisifi nintoisititsmaymoremustmynonotnowofononeonlyorourshallsho uldsosomesuchthanthatthetheirthentherethingsthistoupuponwasw erewhatwhenwhichwhowillwithwouldyourdisptdispt_fed_49.txt0 .280.0520.0090.0960.3580.0260.1310.1220.0170.4110.0260.009 0.140.0350.02600.0090.0440.0960.0440.0350.0170.04400.0170 0.2620.0090.1570.1750.070.0350.0260.02600.0350.11400.90.14 0.0260.0350.0960.0170.0170.0170.0350.0090.0260.0090.1841.4 250.11400.0090.0090.0440.507000.0090.01700.0090.1750.0440 .0090.0870.1920disptdispt_fed_50.txt0.1770.0630.0130.0380.39 30.0630.0510.1390.1140.3930.16500.13900.01300.02500.0760. 1010.1010.0130.152000.0250.2910.0250.0380.1270.0380.03800 .013000.12700.7470.1390.02500.114000.0130.0130.063000.152 1.2540.1650000.0510.35500.0130.0510000.1140.0380.0890.063 0.1390disptdispt_fed_51.txt0.3390.090.0080.030.3010.0080.068 0.2030.0230.4740.0150.0380.1730.02300.0080.0150.0230.0980. 0530.0080.0150.023000.0230.3080.0380.150.1730.030.120.038 0.08300.030.06800.8580.150.030.0230.0600.0080.0680.0380.03
- 37. 0.0350.2221.3890.0900.00700.0760.41000.0070.00700.0140.18 80.0350.2570.0830.0830 DataMining Homework/HW1_Arule_DT_Instructions.docx CISC520 Data Engineering and Mining Homework 1 Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available on the LMS, and an accompanying description (bankdataDescription.doc) of the attributes and their values. The dataset contains attributes on each person’s demographics and banking information in order to determine they will want to obtain the new PEP (Personal Equity Plan). Your goal is to perform Association Rule discovery on the dataset using R. First perform the necessary preprocessing steps required for association rule mining, specifically the id field needs to be removed and a number of numeric fields need discretization or otherwise converted to nominal. Next, set PEP as the right hand side of the rules, and see what rules are generated. Select the top 5 most “interesting” rules and for each specify the following: · Support, Confidence and Lift values · An explanation of the pattern and why you believe it is interesting based on the business objectives of the company. · Any recommendations based on the discovered rule that might
- 38. help the company to better understand behavior of its customers or to develop a business opportunity. Note that the top 5 most interesting rules are most likely not the top 5 in the strong rules. They are rules, that in addition to having high lift and confidence, also provide some non-trivial, actionable knowledge based on underlying business objectives. To complete this assignment, write a short report describing your association rule mining process and the resulting 5 interesting rules, each with their three items of explanation and recommendations. For at least one of the rules, discuss the support, confidence and lift values and how they are interpreted in this data set. You should write your answers as if you are working for a client who knows little about data mining. Your report should give your client some insightful and reliable suggestions on what kinds of potential buyers your client should contact, and convince your client that your suggestions are reliable based on the evidence gathered from your experiment results. In more detail, your answers should include: · Description of preprocessing steps · Description of parameters and experiments in order to obtain strong rules · Give the top 5 most interesting rules and the 3 items listed above for each rule. Part II: In this part of homework, you are expected to apply decision tree induction algorithm to solve a mystery in history: who wrote the disputed essays, Hamilton or Madison? 1. About the Federalist Papers
- 39. Quote from the Library of Congress http://www.loc.gov/rr/program/bib/ourdocs/federalist.html The Federalist Papers were a series of eighty-five essays urging the citizens of New York to ratify the new United States Constitution. Written by Alexander Hamilton, James Madison, and John Jay, the essays originally appeared anonymously in New York newspapers in 1787 and 1788 under the pen name "Publius." A bound edition of the essays was first published in 1788, but it was not until the 1818 edition published by the printer Jacob Gideon that the authors of each essay were identified by name. The Federalist Papers are considered one of the most important sources for interpreting and understanding the original intent of the Constitution. 2. About the disputed authorship The original essays can be downloaded from the Library of Congress. http://thomas.loc.gov/home/histdox/fedpapers.html In the author column, you will find 74 essays with identified authors: 51 essays written by Hamilton, 15 by Madison, 3 by Hamilton and Madison, 5 by Jay. The remaining 11 essays, however, is authored by “Hamilton or Madison”. These are the famous essays with disputed authorship. Hamilton wrote to claim the authorship before he was killed in a duel. Later Madison also claimed authorship. Historians were trying to find out which one was the real author. 3. Computational approach for authorship attribution In 1960s, statistician Mosteller and Wallace analyzed the frequency distributions of common function words in the Federalist Papers, and drew their conclusions. This is a pioneering work on using mathematical approaches for
- 40. authorship attribution. Nowadays, authorship attribution has become a classic problem in the data mining field, with applications in forensics (e.g. deception detection), and information organization. The Federalist Paper data set (fedPapers85.csv) is provided in LMS. The features are a set of “function words”, for example, “upon”. The feature value is the percentage of the word occurrence in an essay. For example, for the essay “Hamilton_fed_31.txt”, if the function word “upon” appeared 3 times, and the total number of words in this essay is 1000, the feature value is 3/1000=0.3% Organize your report using the following template: Section 1: Data preparation You will need to separate the original data set to training and testing data for classification experiments. Describe what examples in your training and what in your test data. Section 2: Build and tune decision tree models First build a DT model using the default setting, and then tune the parameters to see if better model can be generated. Compare these models using appropriate evaluation measures. Describe and compare the patterns learned in these models. Section 3: Prediction After building the classification model, apply it to the disputed papers to find out the authorship and report the performance accuracy of your models.
- 41. Running head: substance use disorder and addictions 1 substance use disorder and addictions 8Substance Use Disorder and Addictions Paula King Walden University Capstone Dr. Jane Lyons June 16, 2019 Problem Statement Substance use disorders and addictions are becoming a common challenge within the social construct in todays’ society. It is vying with the normative of social structures that support good behavior and sustaining from using drugs. A drug is any that substance that is consumed by a person, that may change typical substantial capacities. A medication is a compound substance utilized in the treatment, fix, anticipation, or finding or used to generally upgrade physical or mental prosperity. Medications might be endorsed for a restricted term, or all the time for perpetual issue. Recreational medications are synthetic substances that influence the focal sensory system, for example, narcotics or drugs. They might be utilized for apparent gainful consequences for discernment, awareness, character, and
- 42. conduct. A few medications can cause dependence and habituation. Numerous regular substances, for example, brews, wine, and a few mushrooms, obscure the line among nourishment and medications, as when ingested they influence the working of both the brain and body. When an individual fall ill due to drug use (which they often do), it becomes very hard to treat them. More specifically, when an individual takes a drug overdose, it becomes hard to assist them because they cannot express themselves after passing out. As mentioned above, the number one barrier to treatment stems from the inability of patients to talk to physicians the moment they come into the emergency room. When a physician is not able to speak to a patient to find out whatever he or she got into his or her system, it becomes hard to conduct first aid effectively. In such a situation, what often happens is that the physician must perform the first aid quickly and screen his or her blood to determine the exact poison that is causing a problem. Which again is a challenge because today, people become addicted to a multiplicity of drugs and medicines. It requires a dreadful part of an investment to make sense of what number of kinds of medications a patient has been on (Straus, Glasziou, Richardson & Haynes, 2018). Many different factors determine the effectiveness of treatment. The problem here is that the barriers to treatment delay it and make treatment untimely. When a physician conducts first aid but is yet to fully help the patient realize vital signs because of diagnostic challenges, response to an emergency may be delayed, and a patient's life may be in danger. Lack of communication is the first barrier to treatment because drug and substance abusers have slurred speech, and at times, they cannot completely talk. A majority of drug and substance abusers do not use in front of their families. As such, it becomes difficult for a relative to tell the doctor what the patient used. Besides, the drugs abused (even if they are in the form of pills) are never preserved in labeled bottles. The use of multiple drugs and substances for the sake of realizing a
- 43. dopamine rush later causes a multiplicity of conditions or disorders. Afterward, what follows is a diagnosis of confusion since comorbid disorders may be complicated to deal with (Djulbegovic and Guyatt, 2017). Summary Evidence-Based Practice and Evidence-Based Medicine is yet to be embraced fully in the medical community. Health care professionals are, however, to be trained on matters revolving around Evidence-Based Practice and Evidence-Based Medicine. It will take a considerable amount of time before health caregivers figure out how Evidence-Based Practice and Evidence-Based Medicine can be used to deal with the multitude of challenges that surface when a drug and substance abuse patient is brought forward to the casualty of a hospital. Reference Djulbegovic, B., & Guyatt, G. H., (2017). Progress in evidence- based medicine: a quarter century on. The Lancet, 390(10092), 415-423. https://www.drugabuse.gov/publications/drugfacts/comorbidity- substance-use-disorders-other-mental-illnesses Straus, S. E., Glasziou, P., Richardson, W. S., & Haynes, R. B. (2018). Evidence-Based Medicine E-Book: How to Practice and Teach EBM. Elsevier Health Sciences.