A Prioritization Model for Suicidality Risk Assessment (2020)

Abstract: We reframe suicide risk assessment from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available. Building on measures developed for resource-bounded document retrieval, we introduce a well founded evaluation paradigm, and demonstrate using an expert-annotated test collection that meaningful improvements over plausible cascade model baselines can be achieved using an approach that jointly ranks individuals and their social media posts.

## introduction mental illness is one of the most significant problems in healthcare: in economic terms alone, by 2030 mental illness worldwide is projected to cost more than cardiovascular disease, and more than cancer, chronic respiratory diseases, and diabetes combined @xcite . suicide takes a terrible toll: in 2016 it became the second leading cause of death in the u.s. among those aged 10-34, fourth among those aged 35-54 @xcite . prevalence statistics suggest that roughly 141 of the 3,283 people who attended acl 2019 have since had serious thoughts of suicide, 42 have made a plan, and 19 have actually made attempts. 1 the good news is that nlp and machine learning are showing strong promise for impact in mental health, just as they are having large impacts everywhere else. traditional methods for predicting suicidal thoughts and behaviors have failed to make progress for fifty years @xcite , but with the advent of machine learning approaches @xcite , including text analysis methods for psychology @xcite and the rise of research on mental health using social media @xcite , algorithmic classification has reached the point where it can now dramatically outstrip performance of prior, more traditional prediction methods @xcite . further progress is on the way as the community shows increasing awareness and enthusiasm in this problem space (e.g., @xcite @xcite . the bad news is that moving these methods from the lab into practice will create a major new challenge: identifying larger numbers of people who may require clinical assessment and intervention will increase stress on a severely resource-limited mental health ecosystem that cannot easily scale up. 2 this motivates a reformulation of the technological problem from classification to prioritization of individuals who might be at risk, for clinicians or other suitably trained staff as downstream users. perhaps the most basic way to do prioritization is with a single priority queue that the user scans from top to bottom. this "ranked retrieval" paradigm is common for information retrieval (ir) tasks such as document retrieval. the same approach has been applied to ranking people based on their expertise @xcite , or more generally to ranking entities based on their characteristics @xcite . rather than evaluating categorical accuracy, ranked retrieval systems are typically evaluated by some measure of search quality that rewards placing desired items closer to the top @xcite . most such measures use only item position, but we find it important to also model the time it takes to recognize desired items, since in our setting the time of qualified users is the most limited resource. in this paper, we do so by building on timebiased gain (tbg, @xcite , an ir evaluation measure that models the expected number of relevant items a user can find in a ranked list given a time budget. we observe that in many risk assessment settings (e.g., yates et al. (2017 2019 )), the available information comprises a (possibly large and/or longitudinal) set of documents, e.g. social media posts, associated with each individual, of which possibly only a small number contain a relevant signal. 3 this gives rise to a formulation of our scenario as a nested, or hierarchical, ranking problem, in which individuals are ordered by priority, but each individual's documents must also be ranked (figure 1 ). accordingly, we introduce hierarchical time-biased gain (htbg), a variant of tbg in which individuals are the top level ranked items, and expected reading time is modeled for the ranked list of documents that provides evidence for each individual's assessment. in addition, we introduce a prioritization model that uses a three-level hierarchical attention network to jointly optimize the nested ranking task; this model also addresses the fact that in our scenario, as in many other healthcare-related scenarios, relevance obtains at the level of individuals rather than individual documents (cf. @xcite . using a test collection of reddit-posting individuals who have been assessed for suicide risk by clinicians based on their posts @xcite , we use htbg to model prioritization of individuals and demonstrate that our joint model substantially outperforms cascade model baselines in which the nested rankings are produced independently. hierarchical attention attention, especially in the context of nlp, has two main advantages: it allows the network to attend to likely-relevant parts of the input (either words or sentences), often leading to improved performance, and it provides insight into which parts of the input are being used to make the prediction. these characteristics have made attention mechanisms a popular choice for deep learning that requires human investigation, such as automatic clinical coding @xcite @xcite . although concerns about using attention for interpretation exist @xcite @xcite , @xcite show hierarchical document attention can align well with human-provided ground truth. our prediction model, 3han, is a variant of hierarchical attention networks @xcite . yang et al. use a two-level attention mechanism that learns to pay attention to specific words in a sentence to form a sentence representation, and at the next higher level to weight specific sentences in a document in forming a document representation. adapting this approach to suicide assessment of at-risk individuals, our model moves a level up the representational hierarchy, learning also to weight documents to form representations of individuals. this allows us to jointly model ranking individuals and ranking their documents as potentially relevant evidence, without document-level annotations. evaluating rankings. there is an extensive ir literature on quality measures for ranked lists @xcite @xcite , which generally reward placing highly relevant items near the top of the list, and are often relatively insensitive to mistakes made near the bottom. in the setting of suicidality risk assessment, we care about how much gain (number of at-risk individuals found) can be achieved for a given time budget. time-biased gain (tbg, smucker and clarke, 2012) measures this by assuming a determined user working down a ranked list, with the discount being a function of the time it takes to reach that position. however, neither tbg nor other ranking measures, to the best of our knowledge, can measure the hierarchical ranking found in the scenario that motivates our work: ranking items (i.e. individuals) when each item itself contains a ranked list of potential evidence (their posts). in this paper, we design a new metric, hierarchical time-biased gain (htbg), to measure the hierarchical ranking by incorporating the cascading user model found in expected reciprocal rank (err, @xcite into tbg. ## a measure for risk prioritization section 1 argued for formulating risk assessment as a prioritization process where the assessor has a limited time budget. this leads to four desired properties in an evaluation measure: 4 among many rank-based measures that satisfy the risk-based and head-weighted criteria, tbg directly accounts for assessment time in a way that also satisfies the speed-biased criterion (see theorem 3.1). furthermore, the numeric value of tbg is a lower bound on the expected number of relevant items -in our case, high-risk individualsfound in a given time budget @xcite , making it interpretable. after introducing tbg, in section 3.2 we develop hierarchical time-biased gain (htbg), an extension of tbg, to account for specific properties of risk assessment using social media posts. foot_4 ## classification model we began by motivating risk assessment via social media as a person-centered, time-limited prioritization problem, in which the technological goal is to support downstream clinicians or other assessors in identifying as many people at risk as possible. this led to the conclusion that systems should not only rank individuals but, for each individual, rank their posts, and we introduced an evaluation framework that involves an abstraction of the user's process of identifying people at risk given a nested ranking. next, we need a system that can produce such nested rankings of individuals and their posts. ideally such a system should be able to train on only individual-level, not document-level, labels, since suicide risk is a property of individuals, not documents, and document labels are more difficult to obtain. in addition, such a system should ideally produce additional information to help the downstream user -if not justification of its output, then at least highlighting potentially useful information. to address this need, we introduce 3han, a hierarchical attention network @xcite that extends up to the level of individuals, who are represented as sequences of documents. this architecture is similar to the network we proposed in @xcite for coding clinical encounters; it obtained good predictive performance and we also showed that, despite concerns about the interpretation of network attention @xcite , hierarchical document-level attention succeeded in identifying documents containing relevant evidence. the architecture here differs in that it builds representations hierarchically from the word level, as opposed to pre-extracted conceptual features, and takes document ordering into account using a bi-directional gru @xcite . specifically, our model has five layers (figure 4 ). the first is a word-embedding layer that turns a one-hot word vector into a dense vector. the second to fourth layers are three seq2vec layers with attention that learn to aggregate, respectively, a sequence of word vectors into a sentence vector, a sequence of sentence vectors into a document vector, and a sequence of document vectors into an individual vector (hence 3han). the final layer is a fully connected layer followed by softmax. we detail our seq2vec layer in the context of aggregating a sequence of document vectors to an individual's vector, though the three seq2vec layers are the same. see figure 4b for an illustration. document vectors {d i,j } m @xmath0. the outputs, after passing through a fully-connected layer and a non-linear layer, are then compared to a learnable attention vector, v attention . specifically, once we have the individual vector u i , we can predict the risk label of the individual by passing it through a fully-connected layer and a softmax. specifically, finally, we compare with the ground truth label y i of individual i using negative log-likelihood to calculate a loss: ## experimentation we first introduce the test collection and then show how we can evaluate 3han and the cascade model baselines on the test collection using htbg. to demonstrate the effectiveness of the 3han model, which jointly learns to rank individuals and, within each individual, their posts as evidence, we compare it with different combinations of individual-level rankers and document-level rankers. training details for all the models can be found in appendix c. ## results and discussion our model, 3han+3han att, the only joint model, achieves the best performance on htbg compared to all other combinations of individual rankers and document rankers across three different time budgets ( overall, the effect of document ranking is larger than the effect of individual ranking. notably, the forward document ranker always yields the worst performance. backward, on the other hand, is surprisingly competitive. we hypothesize that this may be an indication that suicidal ideation worsens over time, or perhaps of the unfortunate figure 1 shows the top 3 documents in a summary-style view for each of the highest ranked 3 individuals, with word-level attention shown using shading. words without attention are obfuscated; others are altered to preserve privacy. previously existing measures. for previously existing measures, e.g. tbg and ndcg@20, document ranking has no effect, and thus these are not suitable measures in our scenario. however, we include results here for reference ( ## conclusions and future work we introduced htbg, a new evaluation measure, as a step toward moving beyond risk classification to a paradigm in which prioritization is the focus, and where time matters. like tbg, the htbg score is interpretable as a lower bound on the expected ranker htbg tbg ndcg@20 3han+3han att. 12.49 11.46 70.90 3han av.+backward 12.09 11.40 68.28 lr+backward 11.70 10.98 69.44 optimal 20.39 19.75 100.00 number of relevant items found in a ranking, given a time budget. in our experiment, a "relevant item" is a person classified by experts as being at risk of attempting suicide in the near future. measured at an expected reading time budget of about half a day (4hr20min, half-life 3hrs), our joint ranking approach achieved htbg of 12.49 compared with 11.70 for a plausible baseline from prior art: using logistic regression to rank individuals, and then looking at a individual's posts in backward chronological order. that increase is just a bit short of identifying one more person in need of immediate help in the experiment's population of 242 individuals. there are certainly limitations in our study and miles to go before validating our approach in the real world, but our framework should make it easy to integrate and explore other individual rankers, document rankers and explanation mechanisms, and to actually build user interfaces like the schematic in figure 1 . finally, combing previous results, we can easily show: proof for theorem 3.2. a direct consequence of theorem 3.1 is that if the at-risk individuals are sorted by assessment time in ascending order, no swapping between any two individuals can increase tbg. this, combined with lemma b.2, that all at-risk individuals are on top of not-at-risk individuals, leads to the necessary condition. because any swapping within the not-at-risk individuals does not change tbg when no at-risk individuals are ranked lower, this implies that ranking according to theorem 3.2 gives us a unique and optimal value, which satisfies the sufficient condition of theorem 3.2.