Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

Sleep Apnea In Veterans With Schizophrenia: Estimating Prevalence And Impact On Cognition

The cognitive impairments of schizophrenia drive the functional disability of the illness but are difficult to treat. One barrier to effective cognitive interventions may be medical illnesses that compromise cognition and are over-represented in people with schizophrenia. Obstructive sleep apnea (OSA) is treatable, causes reversible impairments in many cognitive domains also affected by schizophrenia, and is likely under-diagnosed in people with schizophrenia. We have estimated the prevalence of OSA in schizophrenia, both by self-report and with a predictive model, and characterized the associations between OSA and cognition and functional capacity in schizophrenia, using a large dataset of 3942 patients with schizophrenia collected by the Veterans Administration Cooperative Studies Program (CSP) #572 “Genetics of Functional Disability in Schizophrenia and Bipolar Illness”. Neuropsychological tests included TMT-A, BACS Symbol Coding, Category Fluency, verbal learning, working memory and NAB Mazes. Functional capacity measures were the UCSD Performance Skills Assessment Battery (UPSA-B) and the Everyday Functioning Battery- Advanced Finances (EFB-AF). Phi correlations were used to assess associations of self-reported OSA (R-OSA) with demographic and clinical factors. Self-reported diagnosis may underestimate prevalence of OSA in this sample, so a clinical prediction model was also used to calculate predicted prevalence of OSA (P-OSA). Each participant’s composite cognitive score (CCS) was calculated by averaging their age- and gender-corrected T-scores for each cognitive test, with higher scores indicative of better performance. T-tests compared assessments between reported and non-reported OSA (R-OSA v. nR-OSA) and predicted and non-predicted OSA (P-OSA v. nP-OSA). ANOVAs were used to examine differences in CCS, UPSA-B, and EFB-AF among R-OSA, predicted-and-not-reported OSA (PnR-OSA), and No-OSA. Binary logistic regression models of PnR-OSA with sociodemographic and clinical variables were used to characterize this vulnerable subgroup. The reported prevalence of OSA was 14.4% (n=566). R-OSA patients were more likely to have a college education, be married, and be functionally independent. The predicted prevalence of OSA was 71.9% (n=2834). R-OSA patients had higher CCS than nR-OSA, whereas P-OSA patients had lower CCS than nP-OSA (p’s\u3c0.0002). R-OSA patients performed better than nR-OSA in speed of processing assessments, whereas P-OSA individuals performed worse than nP-OSA in speed of processing, verbal learning, and working memory (p’s\u3c0.0005). R-OSA had higher UPSA-B and EFB-AF than nR-OSA (p’s\u3c0.0001). P-OSA patients had a lower EFB-AF than those with nP-OSA (p=0. 003). PnR-OSA patients performed worse than both R-OSA and No-OSA on CCS and EFB-AF, and worse than R-OSA on UPSA-B (p’s \u3c0.05). Veterans with PnR-OSA tended to be older, male, smokers, unmarried, and have higher BMI and less education that the rest of the sample. Our analyses suggest only 20% of OSA in schizophrenia is diagnosed. Self-reported OSA was associated with better performance on cognitive and functional measures, whereas predicted OSA was associated with worse performance on these measures. People with higher cognitive capacity may be more likely to seek medical care, while those with less cognitive capacity are at greater risk for having co-occurring medical conditions that further compromise cognition. Patients vulnerable to under-diagnosis likely have the most to gain from treatment of their OSA

c.1810C>T Polymorphism of NTRK1 Gene is associated with reduced Survival in Neuroblastoma Patients

<p>Abstract</p> <p>Background</p> <p>TrkA (encoded by <it>NTRK1 </it>gene), the high-affinity tyrosine kinase receptor for neurotrophins, is involved in neural crest cell differentiation. Its expression has been reported to be associated with a favourable prognosis in neuroblastoma. Therefore, the entire coding sequence of <it>NTRK1 </it>gene has been analysed in order to identify mutations and/or polymorphisms which may alter TrkA receptor expression.</p> <p>Methods</p> <p>DNA was extracted from neuroblastomas of 55 Polish and 114 Italian patients and from peripheral blood leukocytes of 158 healthy controls. Denaturing High-Performance Liquid Chromatography (DHPLC) and Single-Strand Conformation Polymorphism (SSCP) analysis were used to screen for sequence variants. Genetic changes were confirmed by direct sequencing and correlated with biological and clinical data.</p> <p>Results</p> <p>Three previously reported and nine new single nucleotide polymorphisms were detected. c.1810C>T polymorphism present in 8.7% of cases was found to be an independent marker of disease recurrence (OR = 13.3; p = 0.009) associated with lower survival rates (HR = 4.45 p = 0.041). c.1810C>T polymorphism's unfavourable prognostic value was most significant in patients under 18 months of age with no <it>MYCN </it>amplification (HR = 26; p = 0.008). <it>In-silico </it>analysis of the c.1810C>T polymorphism suggests that the substitution of the corresponding amino acid residue within the conservative region of the tyrosine kinase domain might theoretically interfere with the functioning of the TrkA protein.</p> <p>Conclusions</p> <p><it>NTRK1 </it>c.1810C>T polymorphism appears to be a new independent prognostic factor of poor outcome in neuroblastoma, especially in children under 18 months of age with no <it>MYCN </it>amplification.</p

Department of Family Medicine and Public Health Sciences 2010 Annual Report

2010 annual report includes: External Funding in 2010; Peer-reviewed publications in 2010; Full-time Affiliate and Voluntary Faculty; Honors, Awards and Appointments in 2010

COMPLEX SYSTEMS FOR THE ECONOMIC EVALUATION OF HEALTHCARE IN ONCOLOGY

1noNell’ambito dell' economia della salute si sta assistendo a una dura lotta negli ultimi anni rappresentata da un lato, dal taglio delle risorse da parte dei governi e, dall'altra parte, dall'aumento della domanda di salute. Inoltre, un altro fattore complesso è rappresentato dal rapido aumento del costo dei farmaci di nuova generazione, in particolare nel campo dell'oncologia, legato allo sviluppo di nuovi bersagli molecolari per l'immunoterapia. Lo scopo di questo manoscritto è valutare la relazione tra gli alti costi dei farmaci emergenti in oncologia e la loro relativa tossicità e realizzare un modello quantitativo per la previsione del numero di nuovi casi dei quattro tumori con le più alte incidenze (prostata, seno , polmone e colon-rettale) per stimare il futuro dispendio di droga negli Stati Uniti. Un elemento caratteristico di questo lavoro è lo studio di modelli complessi che stanno diventando sempre più importanti in campo economico e sanitario. La relazione tra tossicità e costo dei farmaci è stata valutata attraverso l'uso dell'analisi del cluster e della tassellatura di Voronoi. Abbiamo anche adottato un algoritmo per la creazione di una rete neurale artificiale (ANN) che consentirebbe, attraverso l'apprendimento basato sul tempo di fattori di rischio correlati al cancro, di stimare l'incidenza dei suddetti tumori maligni fino al 2050. Il costo per un il singolo paziente è stato stimato considerando un paziente ideale con un'altezza di 1,60 m e un peso di 60 kg idoneo a ricevere tutte le terapie attualmente disponibili. Il nostro pacchetto software è stato Matlab R2014b. L'analisi costo/tossicità ha mostrato l'assenza di una relazione tra l'aumento del costo dei farmaci antitumorali e la diminuzione del tasso di eventi avversi gravi (SAE) e le interruzioni (D) come evidenziato dalla presenza di farmaci costosi nel cluster 5 caratterizzato da massima tossicità. L'analisi di ANN ha evidenziato una diminuzione dell'incidenza delle quattro malattie, la più grande delle quali nei tumori del polmone (da 69 casi / 100.000 abitanti nel 1992 a 32 / 100.000 nel 2050). Per quanto riguarda il tumore al seno, la spesa stimata per il trattamento dei due principali tipi istologici (HER2 positivi e HER2 negativi) sarà di $1,435,532,808$ e $4,421,355,552, rispettivamente. Per quanto riguarda l'immunoterapia, la spesa relativa all'utilizzo dei tre agenti principali varierà nel 2050 da$ 1,616,529,467 (Pembrolizumab) a $2,782,155,725 (Nivolumab). In conclusione, i nostri risultati si concentrano sulla necessità di ottimizzare la valutazione del rapporto costi/benefici e costo/tossicità e sottolineano come l'ammontare della spesa per l'uso di farmaci di nuova generazione meriti un'attenta valutazione al fine di garantirne la sostenibilità futura.The health economy is witnessing a hard fight in recent years. On one side, the cut of resources by the governments and, on the other side, the increase in health demand. Additionally, another complex factor is represented by the rapid increase in the cost of new-generation drugs, especially in the oncology field, linked to the development of new molecular targets for immunotherapy. The purpose of this manuscript is to evaluate the relationship between the high costs of drugs emerging in oncology and their relative toxicity and to realize a quantitative model for the prediction of the number of new cases of the four tumors with the highest incidences (prostate, breast, lung and colon-rectal) in order to estimate the future drug expenditure in the United States. A characteristic element of this work is the study of complex models that are becoming increasingly important in the economic and health fields. The relationship between the toxicity and the cost of drugs was evaluated through the use of the cluster analysis and the Voronoi tessellation. We have also adopted an algorithm for the creation of an artificial neural network (ANN) that would allow, through the time-based learning of cancer-related risk factors, to estimate the incidence of the aforementioned malignancies up to 2050. The cost for a single patient was estimated considering an ideal patient with a height of 1.60 m and a weight of 60 kg eligible for receiving all the therapies currently available. Our software package Matlab R2014b. The cost/toxicity analysis showed the absence of a relationship between the increase of the cost of cancer drugs and the decrease in the rate of severe adverse events (SAE) and discontinuations (D) as evidenced by the presence of expensive drugs in the cluster 5 characterized by maximum toxicity. Analysis by ANN highlighted a decrease in the incidence of the four diseases, the largest of which in lung tumors (from 69 cases/100,000 inhabitants in 1992 to 32/100,000 in 2050). As for breast cancer, the estimated expenditure for the treatment of the two main histological types (HER2 positive and HER2 negative) will be in 2050$ 1,435,532,808 and $4,421,355,552, respectively. Regarding immunotherapy, the expenditure related to the use of the three main agents will vary in 2050 from$ 1,616,529,467 (Pembrolizumab) to $ 2,782,155,725 (Nivolumab). In conclusion, our results focus on the need to optimize the evaluation of the cost/benefit ratio and cost/toxicity and underline how the amount of expenditure for the use of new generation drugs deserves careful evaluation in order to ensure their future sustainability.openopenTartari FrancescaTartari, Francesc