Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs

Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in drug discovery and pharmaceutical research as they provide a structured way to integrate diverse information sources, enhancing AI system interpretability. This interpretability is crucial in healthcare, where trust and transparency matter, and eXplainable AI (XAI) supports decision making for healthcare professionals. This overview summarizes recent literature on the impact of KGs in healthcare and their role in developing explainable AI models. We cover KG workflow, including construction, relationship extraction, reasoning, and their applications in areas like Drug-Drug Interactions (DDI), Drug Target Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and bioinformatics. We emphasize the importance of making KGs more interpretable through knowledge-infused learning in healthcare. Finally, we highlight research challenges and provide insights for future directions.Comment: IEEE AIKE 2023, 8 Page

What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

In-context learning refers to the ability of a model to condition on a prompt sequence consisting of in-context examples (input-output pairs corresponding to some task) along with a new query input, and generate the corresponding output. Crucially, in-context learning happens only at inference time without any parameter updates to the model. While large language models such as GPT-3 exhibit some ability to perform in-context learning, it is unclear what the relationship is between tasks on which this succeeds and what is present in the training data. To make progress towards understanding in-context learning, we consider the well-defined problem of training a model to in-context learn a function class (e.g., linear functions): that is, given data derived from some functions in the class, can we train a model to in-context learn "most" functions from this class? We show empirically that standard Transformers can be trained from scratch to perform in-context learning of linear functions -- that is, the trained model is able to learn unseen linear functions from in-context examples with performance comparable to the optimal least squares estimator. In fact, in-context learning is possible even under two forms of distribution shift: (i) between the training data of the model and inference-time prompts, and (ii) between the in-context examples and the query input during inference. We also show that we can train Transformers to in-context learn more complex function classes -- namely sparse linear functions, two-layer neural networks, and decision trees -- with performance that matches or exceeds task-specific learning algorithms. Our code and models are available at https://github.com/dtsip/in-context-learning

The black bone disease: a case report of ochronotic hip arthropathy

Ochronotic arthropathy is a rare complication in patients with alkaptonuria (AKU) that arises as a result of accumulation of ochronotic pigment in the joints. This case report presented a 70-year-old female patient with chronic pain in B/L knee and right hip with decreased e range of motion. The physical and radiographic findings were agreeable with end-stage hip osteoarthritis and knee osteoarthritis. The diagnosis was done by finding of a dark capsule and femoral head during the total hip replacement. The surgical treatments significantly minimized and enhanced the range of motion (ROM). AKU normally emerges after age 40 and is normally asymptomatic till the involvement of the spine, hip, knee and shoulder joints. Therefore, orthopedic surgeons must be observant of clinical manifestations of this rare condition, before and during the surgery. Arthroplasty is an appropriate therapeutic recourse for patients suffering from ochronotic arthropathy

Testing with Non-identically Distributed Samples

We examine the extent to which sublinear-sample property testing and estimation applies to settings where samples are independently but not identically distributed. Specifically, we consider the following distributional property testing framework: Suppose there is a set of distributions over a discrete support of size $k$, $\textbf{p}_1, \textbf{p}_2,\ldots,\textbf{p}_T$, and we obtain $c$ independent draws from each distribution. Suppose the goal is to learn or test a property of the average distribution, $\textbf{p}_{\mathrm{avg}}$. This setup models a number of important practical settings where the individual distributions correspond to heterogeneous entities -- either individuals, chronologically distinct time periods, spatially separated data sources, etc. From a learning standpoint, even with $c=1$ samples from each distribution, $\Theta(k/\varepsilon^2)$ samples are necessary and sufficient to learn $\textbf{p}_{\mathrm{avg}}$ to within error $\varepsilon$ in TV distance. To test uniformity or identity -- distinguishing the case that $\textbf{p}_{\mathrm{avg}}$ is equal to some reference distribution, versus has $\ell_1$ distance at least $\varepsilon$ from the reference distribution, we show that a linear number of samples in $k$ is necessary given $c=1$ samples from each distribution. In contrast, for $c \ge 2$, we recover the usual sublinear sample testing of the i.i.d. setting: we show that $O(\sqrt{k}/\varepsilon^2 + 1/\varepsilon^4)$ samples are sufficient, matching the optimal sample complexity in the i.i.d. case in the regime where $\varepsilon \ge k^{-1/4}$. Additionally, we show that in the $c=2$ case, there is a constant $\rho > 0$ such that even in the linear regime with $\rho k$ samples, no tester that considers the multiset of samples (ignoring which samples were drawn from the same $\textbf{p}_i$) can perform uniformity testing

Psychotic Disorders, Definition, Sign and Symptoms, Antipsychotic Drugs, Mechanism of Action, Pharmacokinetics & Pharmacodynamics with Side Effects & Adverse Drug Reactions: Updated Systematic Review Article

Psychosis is a mental disorder characterized by a disconnection from reality. Psychosis is a group of disorder characterized by thought disorder, abnormal behaviour, defective cognition, delusion and hallucination. Adverse drug reaction is defined as any undesired or unintended effects of drugs treatment. According to the World Health Organization (WHO)- “adverse drug reaction (ADRs) has been defined one which is noxious and unintended, and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of disease, or modification of physiological function”. Adverse drug reactions are the most important causes of the mortality and morbidity. Antipsychotics are the most effective drugs which are used in the psychiatry in the maintenance therapy of mania, psychoses and schizophrenia. The antipsychotics drugs are chemically disparate but have the common property of alleviating the symptoms of organic as well as functional psychosis. But they also have a capacity to cause a wide range of potential adverse drug reactions that can lead to non-compliance that can impair quality of life, may cause the extra pyramidal symptoms which can lead to discontinuation of therapy and in extreme cases it may be fatal. Knowledge of assessment of ADRs due to different antipsychotics is necessary. It helps to choose to safe treatment and reduce the risk of occurrence of ADRs by the clinicians. ADR are often poorly identified and reported in day to day medical practice. As we collect more and more information about ADRs, we need an active surveillance system regarding identification and reporting of ADRs with antipsychotic drugs.  On many review articles are read & ward round participation experiences we find that antipsychotic drugs can have shown a various kind of ADRs. Psychiatrist and clinical pharmacist are need to be made aware of these potentially fatal adverse effects associated with antipsychotic drugs via conduction of patients counseling regarding (drugs, disease, doses & side effects), quality-based seminars, published medical literature, conferences, learning programs and health care camps. Keywords: Antipsychotic Drugs, WHO, Adverse Drug Reactions, Pharmacovigilance, Psychiatrist

ANAGEMENT OF PROXIMAL HUMERUS FRACTURE IN ADULTS WITH PHILOS PLATE FIXATION IN NEER TYPE 2 AND TYPE 3

Objective: One of the most frequent bone fractures is a fracture of the proximal humerus. They make up between 4% and 5% of all fracture. With less invasive soft-tissue injury and a lower risk of iatrogenic avascular necrosis, closed reduction and percutaneous fixation have become more popular in recent years as opposed to open reduction (OR) and extensive internal fixation (IF) (by plates and screws). The aim of this study was to compare the functional results of proximal humerous locking osteosynthesis (PHILOS) fixation against OR and IF of proximal humerus fractures (2 and 3 Neer’s classification).  Methodology: This study involved 40 patients, with a mean age of 53 and a range of ages from 18 to 55, with 2 and 3 part fractures according to Neer’s classification. Patients were randomized to either group, with Group I type 2 fractures receiving OR and IF for 22 patients, and Group II (type 3 fractures) with 18 patients receiving PHILOS plate fixation, with function assessed using the CMS score.  Results: At 1 month, 3 months, and 6 months of follow-up, Group I’s mean Visual analog scale (VAS) score decreased to 2.52, 2.10, and 1.22 and in Group II, 3.86, 2.64, and 2.41. The VAS score was reduced and function CMS score were significantly increased in Group I (80% VAS score, 65% CMS score) as compared to Group II (64% VAS score, and 58%CMS score). At 1, 3, and 6 months, there was a statistically significant difference between the two groups. Conclusion: Both groups saw satisfactory results, with each method having benefits and drawbacks. We discovered that plate fixation provided stable fixation with few implant problems and early range-of-motion exercise to achieve acceptable functional results

Touchless Typing using Head Movement-based Gestures

Physical contact-based typing interfaces are not suitable for people with upper limb disabilities such as Quadriplegia. This paper, thus, proposes a touch-less typing interface that makes use of an on-screen QWERTY keyboard and a front-facing smartphone camera mounted on a stand. The keys of the keyboard are grouped into nine color-coded clusters. Users pointed to the letters that they wanted to type just by moving their head. The head movements of the users are recorded by the camera. The recorded gestures are then translated into a cluster sequence. The translation module is implemented using CNN-RNN, Conv3D, and a modified GRU based model that uses pre-trained embedding rich in head pose features. The performances of these models were evaluated under four different scenarios on a dataset of 2234 video sequences collected from 22 users. The modified GRU-based model outperforms the standard CNN-RNN and Conv3D models for three of the four scenarios. The results are encouraging and suggest promising directions for future research.Comment: *The two lead authors contributed equally. The dataset and code are available upon request. Please contact the last autho